Showing papers on "Vehicle dynamics published in 2018"
TL;DR: It is proven that distributed maneuvering errors converge to a residual set by virtue of cascade stability analysis, and an optimization-based command governor is employed to generate an optimal guidance signal for vehicle kinetics.
Abstract: This brief is concerned with the distributed maneuvering of multiple autonomous surface vehicles guided by a virtual leader moving along a parameterized path. In the guidance loop, a distributed guidance law is developed by incorporating a constant bearing strategy into a path-maneuvering design such that a prescribed formation pattern can be reached. To optimize the guidance signal under velocity constraint as well as minimize control torque during transient phase, an optimization-based command governor is employed to generate an optimal guidance signal for vehicle kinetics. The optimization problem is formulated as a bound-constrained quadratic programming problem, which is solved using a recurrent neural network. In the control loop, an estimator is developed where a fuzzy system is used to approximate unknown kinetics based on input and output data. Next, a kinetic control law is constructed based on the optimal command signal and the fuzzy-system-based estimator. By virtue of cascade stability analysis, it is proven that distributed maneuvering errors converge to a residual set. The simulation results illustrate the efficacy of the proposed method.
284 citations
TL;DR: Simulation results substantiate the efficacy of the proposed method for output-feedback path-following of under-actuated autonomous underwater vehicles and prove that all error signals in the closed-loop system are uniformly and ultimately bounded.
Abstract: This paper presents a design method for output-feedback path-following control of under-actuated autonomous underwater vehicles moving in a vertical plane without using surge, heave, and pitch velocities. Specifically, an extended state observer (ESO) is developed to recover the unmeasured velocities as well as to estimate total uncertainty induced by internal model uncertainty and external disturbance. At the kinematic level, a commanded guidance law is developed based on a vertical line-of-sight guidance scheme and the observed velocities. To optimize guidance signals, optimization-based reference governors are formulated as bound-constrained quadratic programming problems for computing optimal reference signals. Two globally convergent recurrent neural networks called projection neural networks are used to solve the optimization problems in real-time. Based on the optimal reference signals and ESO, a kinetic control law with disturbance rejection capability is constructed at the kinetic level. It is proved that all error signals in the closed-loop system are uniformly and ultimately bounded. Simulation results substantiate the efficacy of the proposed method for output-feedback path-following of under-actuated autonomous underwater vehicles.
281 citations
TL;DR: Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon than the existing physics- and maneuver-based approaches.
Abstract: Vehicle trajectory prediction helps automated vehicles and advanced driver-assistance systems have a better understanding of traffic environment and perform tasks such as criticality assessment in advance. In this study, an integrated vehicle trajectory prediction method is proposed by combining physics- and maneuver-based approaches. These two methods were combined for the reason that the physics-based trajectory prediction method could ensure accuracy in the short term with the consideration of vehicle running dynamic parameters, and the maneuver-based prediction approach has a long-term insight into future trajectories with maneuver estimation. In this study, the interactive multiple model trajectory prediction (IMMTP) method is proposed by combining the two predicting models. The probability of each model in the interactive multiple models could recursively adjust according to the predicting variance of each model. In addition, prediction uncertainty is considered by employing unscented Kalman filters in the physics-based prediction model. To the maneuver-based method, random elements for uncertainty are introduced to the trajectory of each maneuver inferred by using the dynamic Bayesian network. The approach is applied and analyzed in the lane-changing scenario by using naturalistic driving data. Comparison results indicate that IMMTP could achieve a more accurate prediction trajectory with a long prediction horizon.
250 citations
TL;DR: An adaptive formation control that ensures internal stability of closed-loop systems with guaranteed prescribed performance is proposed and both collision avoidance and connectivity maintenance between two consecutive vehicles are guaranteed during the whole operation.
Abstract: This paper studies the platoon formation control problem for unmanned surface vehicles, in the presence of modeling uncertainties and time-varying external disturbances. The control objective is to make the vehicular platoons proceed along a given trajectory while maintaining a desired line-of-sight (LOS) range between each vehicle and its predecessor. To provide transient performance specifications on formation errors, including LOS range and angle errors, we enforce prescribed performance guarantees in the control design. The prescribed performance guarantees mean that formation errors evolve always within the predefined regions that are bounded by exponentially decaying functions of time. Using prescribed performance control methodology, neural network approximation, disturbance observers, dynamic surface control technique, and Lyapunov synthesis, we propose an adaptive formation control that ensures internal stability of closed-loop systems with guaranteed prescribed performance. Meanwhile, both collision avoidance and connectivity maintenance between two consecutive vehicles are guaranteed during the whole operation. The proposed formation control is decentralized in the sense that the control action on each vehicle depends only on information from its immediate predecessor. Simulation results demonstrate the performance of the proposed control.
238 citations
TL;DR: A new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics.
Abstract: In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs’ heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.
199 citations
TL;DR: The stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller, which can stabilize states of the UMV.
Abstract: This paper is concerned with a Takagi–Sugeno (T–S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T–S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T–S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.
197 citations
TL;DR: In this paper, a dual-loop primary controller is proposed to regulate primary-side power and current, which allows sequential and timely activation of segmented primary coils; it controls the primary coil current at the reference value under no-load and loaded conditions, compensates for power transfer reduction caused by vehicle lateral misalignment (LTM), and prevents primary overloading.
Abstract: Dynamic wireless charging of electric vehicles (EVs) can significantly extend the EVs’ driving range and consequently, the prospect of electrified transportation. In this paper, a comprehensive study is conducted to elaborate the constraints of real driving conditions and propose a solution that could cope with misalignment problem and the dynamics imposed by the charging process and by EVs passing over road-embedded charging pads. A dual-loop primary controller is proposed to regulate primary-side power and current. The controller allows sequential and timely activation of segmented primary coils; it controls the primary coil current at the reference value under no-load and loaded conditions, compensates for power transfer reduction caused by the vehicle lateral misalignment (LTM), and prevents primary overloading. The primary of the dynamic wireless charger is modeled using the generalized state-space averaging method and the model is verified through simulations and experiments. After that, a controller has been designed and implemented and its operation is evaluated through simulations and experimental tests. A 25-kW charging system with two primary coils is built and tested in a real environment. The measured energy efficiency is 86% for the laterally aligned vehicle, with the possibility to be increased over 90% using enhanced schemes for coils’ activation and deactivation. The system is delivering an equal amount of energy for all LTMs in the range of ±15 cm, which improves the expected value of transferred energy by more than 30%.
190 citations
TL;DR: A novel coordinated path following system (PFS) and direct yaw-moment control (DYC) of autonomous electric vehicles via hierarchical control technique is presented, and a pseudo inverse (PI) low-level control allocation law is designed to realize the tracking of desired external moment torque and management of the redundant tire actuators.
166 citations
TL;DR: An adaptive control for vehicle active suspensions with unknown nonlinearities (e.g., nonlinear springs and piecewise dampers) is proposed, such that both the transient and steady-state suspension response are guaranteed.
Abstract: This paper proposes an adaptive control for vehicle active suspensions with unknown nonlinearities (eg, nonlinear springs and piecewise dampers) A prescribed performance function that characterizes the convergence rate, maximum overshoot, and steady-state error is incorporated into the control design to stabilize the vertical and pitch motions, such that both the transient and steady-state suspension response are guaranteed Moreover, a novel adaptive law is used to achieve precise estimation of essential parameters (eg, mass of vehicle body and moment of inertia for pitch motion), where the parameter estimation error is obtained explicitly and then used as a new leakage term Theoretical studies prove the convergence of the estimated parameters, and compare the suggested controller with generic adaptive controllers using the gradient descent and e-modification schemes In addition to motion displacements, dynamic tire loads and suspension travel constraints are also considered Extensive comparative simulations on a dynamic simulator consisting of commercial vehicle simulation software Carsim 81 and MATLAB Simulink are provided to show the efficacy of the proposed control, and to illustrate the improved performance
162 citations
TL;DR: The coupled dynamics between the rigid body payload, links, and quadrotors are explicitly incorporated into control system design and stability analysis to avoid singularities and complexities that are associated with local parameterizations.
Abstract: This paper is focused on tracking control for a rigid body payload that is connected to an arbitrary number of quadrotor unmanned aerial vehicles via rigid links. An intrinsic form of the equations of motion is derived on the nonlinear configuration manifold, and a geometric controller is constructed such that the payload asymptotically follows a given desired trajectory for its position and attitude in the presence of uncertainties. The unique feature is that the coupled dynamics between the rigid body payload, links, and quadrotors are explicitly incorporated into control system design and stability analysis. These are developed in a coordinate-free fashion to avoid singularities and complexities that are associated with local parameterizations. The desirable features of the proposed control system are illustrated by a numerical example.
152 citations
TL;DR: A novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach and the validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.
Abstract: As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.
21 May 2018
TL;DR: This article introduces two conditions which simultaneously stabilize traffic while imposing a safety constraint on the autonomous vehicle and limiting degradation of performance, and formalizes the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis, and pose the resulting nonlinear optimization problem.
Abstract: Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematical modeling, and control analysis. In this article, we study the ability of small numbers of autonomous vehicles to stabilize a single-lane system of human-driven vehicles. We formalize the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis, and pose the resulting nonlinear optimization problem. In particular, we introduce two conditions which simultaneously stabilize traffic while imposing a safety constraint on the autonomous vehicle and limiting degradation of performance. With this optimal linear controller in a system with typical human driver behavior, we can numerically determine that only a 6% uniform penetration of autonomously controlled vehicles (i.e. one per string of up to 16 human-driven vehicles) is necessary to stabilize traffic across all traffic conditions.
TL;DR: Theoretical analysis and simulation results reveal that the proposed bioinspired nonlinear dynamics-based adaptive controller has a significant impact on the amount of energy consumption, considering the same basic control method and random excitation of road irregularity for a similar ride comfort performance.
Abstract: This paper investigates the energy-efficiency design of adaptive control for active suspension systems with a bioinspired nonlinearity approach To this aim, a bioinspired dynamics-based adaptive tracking control is proposed for nonlinear suspension systems In many existing techniques, one important effort is used for canceling vibration energy transmitted by suspension inherent nonlinearity to improve ride comfort Unlike existing methods, the proposed approach takes full advantage of beneficial nonlinear stiffness and damping characteristics inspired by the limb motion dynamics of biological systems to achieve advantageous nonlinear suspension properties with potentially less energy consumption The stability analysis of the desired bioinspired nonlinear dynamics is conducted within the Lyapunov framework Theoretical analysis and simulation results reveal that the proposed bioinspired nonlinear dynamics-based adaptive controller has a significant impact on the amount of energy consumption, considering the same basic control method and random excitation of road irregularity for a similar ride comfort performance
TL;DR: A robust fuzzy control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering is presented.
Abstract: This paper presents a robust fuzzy $H_{\infty }$ control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering. Since vehicle lateral dynamics possesses inherent nonlinearities, the main objective is dedicated to deal with the nonlinear challenge in vehicle lateral dynamics by applying Takagi-Sugeno (T-S) fuzzy modeling approach. First, the nonlinear Brush tire dynamics and the nonlinear functions of longitudinal velocity are represented via a T-S fuzzy modeling technique, and vehicle parametric uncertainties are handled by the norm-bounded uncertainties. An uncertain nonlinear vehicle lateral dynamic T-S fuzzy model is then obtained with multi-fuzzy-rules. The resulting robust fuzzy $H_{\infty }$ state-feedback controller is designed with the parallel-distributed compensation strategy and premise variables, and solved via a set of linear matrix inequalities derived from Lyapunov asymptotic stability and quadratic $H_{\infty }$ performance. Simulations for two different maneuvers are implemented with a high-fidelity, CarSimⓇ, full-vehicle model to verify the effectiveness of the developed approach. It is confirmed from the results that the proposed controller can effectively preserve vehicle lateral stability and enhance yaw handling performance.
TL;DR: In this article, a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control is presented, where the safety specifications for these driver assistance modules are expressed in terms of set invariance.
Abstract: This paper develops a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control. The safety specifications for these driver assistance modules are expressed in terms of set invariance. Control barrier functions (CBFs) are used to design a family of control solutions that guarantee the forward invariance of a set, which implies satisfaction of the safety specifications. The CBFs are synthesized through a combination of sum-of-squares program and physics-based modeling and optimization. A real-time quadratic program is posed to combine the CBFs with the performance-based controllers, which can be either expressed as control Lyapunov function conditions or as black-box legacy controllers. In both cases, the resulting feedback control guarantees the safety of the composed driver assistance modules in a formally correct manner. Importantly, the quadratic program admits a closed-form solution that can be easily implemented. The effectiveness of the control approach is demonstrated by simulations in the industry-standard vehicle simulator Carsim. Note to Practitioners —Safety is of paramount importance for the control of automated vehicles. This paper is motivated by the problem of designing controllers that are provably correct for the simultaneous operation of two driver assistance modules, lane keeping and adaptive cruise control. This is a challenging problem partially, because the lateral and longitudinal dynamics of the vehicles are coupled, with few results known to exist that provide formal guarantees. In this paper, we employ an assume-guarantee formalism between these two subsystems, such that they can be considered individually; based on that, we use optimization to design safe sets that serves as “supervisors” for vehicle behavior, such that the trajectories of the closed-loop system are confined within the safe sets using predetermined bounds on wheel force and steering angle. The feedback controller is constructed by solving convex quadratic programs online, which can also be given in closed form, making the implementation much easier. One particular advantage of this control approach is that the safety set and the performance controller can be designed separately, which enables the integration of a legacy controller into a correct-by-construction solution.
TL;DR: A tracking control scheme for proximity operations between a target and a pursuer spacecraft that ensures accurate relative position tracking as well as attitude synchronization and a novel time-varying forcing function into the sliding dynamics is proposed.
Abstract: The capture of a free-floating tumbling object using an autonomous vehicle is a key technology for many future orbital missions. Spacecraft proximity operations will play an important role in guaranteeing the success of such missions. In this paper, we technically propose a tracking control scheme for proximity operations between a target and a pursuer spacecraft that ensures accurate relative position tracking as well as attitude synchronization. Specifically, an integrated six degrees of freedom dynamics model is first established to describe the relative motion of the pursuer with respect to the target. Then, a robust fault-tolerant controller is derived by combining the sliding mode control and the adaptive technique. The designed controller is proved to be not only robust against unexpected disturbances and adaptive to unknown and uncertain mass/inertia properties of the pursuer, but also able to accommodate a large class of actuator faults. In particular, by incorporating a novel time-varying forcing function into the sliding dynamics, the proposed control algorithm is able to guarantee the finite-time convergence of the translational and rotational tracking errors, and the convergence time as an explicit parameter can be assigned a priori by the designers. Furthermore, a theoretical analysis is also presented to assess the fault tolerance ability of the designed controller. Finally, numerous examples are carried out to evaluate the effectiveness and demonstrate the benefits of the overall control approach.
TL;DR: In this article, a funnel non-affine controller applying neural approximation for prescribed tracking of air-breathing hypersonic vehicles (AHVs) is proposed, and the desired transient performance and steady-state performance are ensured for both tracking errors.
Abstract: This paper presents a funnel non-affine controller applying neural approximation for prescribed tracking of air-breathing hypersonic vehicles (AHVs). We propose a new funnel control to force velocity and altitude tracking errors to fall within bounded funnels, while the desired transient performance and steady-state performance are ensured for both tracking errors. To handle the non-affine dynamics, a simplified neural controller is addressed for a velocity subsystem based on implicit function theorem, and a new back-stepping control without virtual control laws is exploited for the altitude subsystem via a model transformation combined with low-pass-filter approach. Neural approximations and regulation laws for guaranteeing approximation performance are employed to reject system unknown dynamics. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov synthesis. Finally, the tracking performance of the proposed control approach is verified by simulation results.
TL;DR: A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles and numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.
Abstract: This paper deals with the trajectory following control problem of a class of autonomous vehicles with parametric uncertainties, external disturbances, and over-actuated features. A novel adaptive hierarchical control framework is proposed to supervise the lateral motion of autonomous four-wheel independent drive electric vehicles. First, an adaptive sliding mode high-level control law with the linear matrix inequality-based switching surface is designed to produce a vector of front steering angle and external yaw moment, in which the uncertain term and the switching control gain are adaptively regulated by the fuzzy logic technique, to further moderate the chattering phenomenon, an adaptive boundary layer is introduced. Second, a pseudo-inverse low-level control allocation algorithm is presented to optimally allocate the external yaw moment via coordinating and reconstructing the tire longitudinal forces. Finally, numerical simulation and experimental results demonstrate that the proposed adaptive control approach has outstanding tracking performance.
TL;DR: Both safe passage of the vehicles through the intersection and a high intersection throughput (due to close “virtual” vehicle following) can be achieved.
Abstract: This paper proposes a cooperative intersection control strategy, which aims to decrease the number of accidents and to increase the traffic flow at intersections. Existing high-level automation methodologies mainly focus on the determination of a safe crossing sequence of the involved vehicles, typically ignoring realistic vehicle dynamics aspects. The solution proposed in this paper, referred to as cooperative intersection control (CIC), takes into account the dynamics of the vehicles and is based on the novel concept of virtual platooning. Virtual platooning allows to form platoons of vehicles that are in different lanes of the intersection and have different directional intentions. Herewith, both safe passage of the vehicles through the intersection and a high intersection throughput (due to close “virtual” vehicle following) can be achieved. The performance of the proposed strategy is assessed, and a comparison between the CIC and an intersection controlled with traffic lights is presented.
TL;DR: This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications and demonstrates the predictive power of the analytical string stability conditions.
Abstract: Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance. The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string. Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance of the adaptive driving strategy under V2I cooperation is tested in simulation. Results show that even the estimation of control parameters of non-connected automated vehicles are imperfect and there is mismatch between the model used in analytical derivation and that in simulation, the proposed adaptive driving strategy suppresses disturbances in a wide range of situations.
TL;DR: The application to the assembly line of the AGV with different payloads to track the circular and piecewise straight-line paths by the proposed HIFDSMC is compared with the hierarchical fuzzy decentralized PTC.
Abstract: A hierarchically improved fuzzy dynamical sliding-model control (HIFDSMC) is presented to address the autonomous ground vehicle (AGV) path tracking problem. The proposed controller has two portions: one is the virtual desired input (VDI), and the second is the path tracking control (PTC). In addition to the equivalent control in VDI and PTC, an improved fuzzy dynamical sliding-mode control (IFDSMC) is designed to deal with the system uncertainties, e.g., different payloads. Contributions of this paper include the following four parts: 1) Based on the nominal system response, the fuzzy rules and scaling factors of the IFDSMCs in the VDI and PTC are easily chosen. In contrast, a conventional fuzzy logic control approach requires more trial-and-error tuning to obtain a satisfactory performance. 2) The proposed HIFDSMC possesses the tuning mechanism (the coefficients of two sliding surfaces, the scaling factors in indirect and direct modes, and the fine tuning in fuzzy table) such that the uncertainties are tackled without a larger computational burden. 3) The stability of the closed-loop system is verified by the Lyapunov stability with hierarchical concept. 4) Different payloads not at the mass center of the AGV (e.g., greater than 25% in the total weight of the AGV) are tackled by the IFDSMCs to obtain a satisfactory performance. Finally, the application to the assembly line of the AGV with different payloads to track the circular and piecewise straight-line paths by the proposed HIFDSMC is compared with the hierarchical fuzzy decentralized PTC.
03 Apr 2018
TL;DR: In this paper, a generic simulation-based toolchain for the model-in-the-loop identifcation of critical scenarios is introduced, which allows the identification of critical scenario with respect to the vehicle development process.
Abstract: One of the major challenges for the automotive industry will be the release and validation of cooperative and automated vehicles. The immense driving distance that needs to be covered for a conventional validation process requires the development of new testing procedures. Further, due to limited market penetration in the beginning, the driving behavior of other human trafc participants, regarding a mixed trafc environment, will have a signifcant impact on the functionality of these vehicles.
In this article, a generic simulation-based toolchain for the model-in-the-loop identifcation of critical scenarios will be introduced. The proposed methodology allows the identifcation of critical scenarios with respect to the vehicle development process. The current development status of the cooperative and automated vehicle determines the availability of testable simulation models, software, and components.
The identifcation process is realized by a coupled simulation framework. A combination of a vehicle dynamics simulation that includes a digital prototype of the cooperative and automated
vehicle, a trafc simulation that provides the surrounding environment, and a cooperation simulation including cooperative features is used to establish a suitable comprehensive simulation environment. The behavior of other trafc participants is considered in the trafc simulation environment. The criticality of the scenarios is determined by appropriate metrics. Within the context of this article, both standard safety metrics and newly developed trafc quality metrics are used for evaluation. Furthermore, we will show how the use of these new metrics allows for investigating the impact of cooperative and automated vehicles on trafc. The identifed critical scenarios are used as an input for X-in-the-Loop methods, test benches, and proving ground tests to achieve an even more precise comparison to real-world situations. As soon as the vehicle development process is in a mature state, the digital prototype becomes a “digital twin” of the cooperative and automated vehicle.
TL;DR: The proposed general feedback control architecture includes an estimator design for fusion of database information, exteroceptive as well as proprioceptive measurements, a geometric corridor planner based on graph theory for the avoidance of multiple, potentially dynamically moving objects, and a spatial-based predictive controller.
Abstract: This paper presents an integrated control approach for autonomous driving comprising a corridor path planner that determines constraints on vehicle position, and a linear time-varying model predictive controller combining path planning and tracking in a road-aligned coordinate frame. The capabilities of the approach are illustrated in obstacle-free curved road-profile tracking, in an application coupling adaptive cruise control (ACC) with obstacle avoidance (OA), and in a typical driving maneuver on highways. The vehicle is modeled as a nonlinear dynamic bicycle model with throttle, brake pedal position, and steering angle as control inputs. Proximity measurements are assumed to be available within a given range field surrounding the vehicle. The proposed general feedback control architecture includes an estimator design for fusion of database information (maps), exteroceptive as well as proprioceptive measurements, a geometric corridor planner based on graph theory for the avoidance of multiple, potentially dynamically moving objects, and a spatial-based predictive controller. Switching rules for transitioning between four different driving modes, i.e., ACC, OA, obstacle-free road tracking (RT), and controlled braking (Brake), are discussed. The proposed method is evaluated on test cases, including curved and highway two-lane road tracks with static as well as moving obstacles.
TL;DR: Two torque estimation methods for vehicle engines are presented: unknown input observer (UIO) and adaptive parameter estimation, which allow for improved computational efficiency and show very encouraging results with small estimation errors even in the presence of sensor noise.
Abstract: This paper presents two torque estimation methods for vehicle engines: unknown input observer (UIO) and adaptive parameter estimation. We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the engine torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise.
TL;DR: This proposed hierarchical control system is tested in a hardware-in-the-loop system with four typical maneuvers, which are constant velocity, accelerating, decelerating, and low road adhesion coefficient situations to show different driver command.
Abstract: For four wheel independent motor-drive electric vehicle, the vehicle longitudinal and lateral motion can be controlled by distributing the driving and regenerative braking torques of four wheel motors. To meet the driving command of driver and keep the vehicle lateral stability, a hierarchical control system is proposed in this paper. In the upper layer, a nonlinear model predictive control is implemented to solve the nonlinear multiinput multioutput, over-actuated problem. The controller is based on a nonlinear three degree-of-freedom model with nonlinear tire model, considering wheel slips as virtual control input. In the lower layer, the wheel slips are manipulated by a PID controller for generating driving and regenerative braking torques of the independent motors. This proposed controller is tested in a hardware-in-the-loop system with four typical maneuvers, which are constant velocity, accelerating, decelerating, and low road adhesion coefficient situations to show different driver command. The results show that the driver command of longitudinal and lateral motion control are both satisfied.
TL;DR: The method computes smooth motion trajectories, satisfying the vehicle’s kinematics, by using a spline parameterization, and is embodied in a user-friendly open-source software toolbox.
Abstract: Autonomous vehicles require a collision-free motion trajectory at every time instant. This brief presents an optimization-based method to calculate such trajectories for autonomous vehicles operating in an uncertain environment with moving obstacles. The proposed approach applies to linear system models, as well as to a particular class of nonlinear models, including industrially relevant vehicles, such as autonomous guided vehicles with front wheel, differential wheel, and rear-wheel steering. The method computes smooth motion trajectories, satisfying the vehicle’s kinematics, by using a spline parameterization. Furthermore, it exploits spline properties to keep the resulting nonlinear optimization problem small scale and to guarantee constraint satisfaction, without the need for time gridding. The resulting problem is solved sufficiently fast for online motion planning, dealing with uncertainties and changes in the environment. This brief demonstrates the potential of the method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot , steered as a holonomic or differential drive vehicle, drives through an environment with moving obstacles. To facilitate the further development and the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.
TL;DR: To address the multimotor coordinate operation against the actuator faults in the 4WID system, combining the adaptive sliding mode (ASM) control and the fault-tolerant (FT) control allocation, anAdapt sliding mode fault-Tolerant coordination (ASm-FTC) control is proposed.
Abstract: Four wheel independently driven (4WID) electric vehicles are promising vehicle architectures. However, the complex coordination control of the four driving motors and the operation reliability of the vehicle are challenges. In this paper, to address the multimotor coordinate operation against the actuator faults in the 4WID system, combining the adaptive sliding mode (ASM) control and the fault-tolerant (FT) control allocation, an adaptive sliding mode fault-tolerant coordination (ASM-FTC) control is proposed. First, a new vehicle dynamic model with a driving motor fault is set up. In the control layer, an adaptive variable exponential reaching law is used in the ASM to alleviate the chattering and improve the reaching speed, precision, and robustness. Then, the FTC allocation based on the quadratic programming is designed to properly coordinate the four in-wheel motors at the presentation of the motor fault. To verify the proposed control method, a 4WID electric vehicle platform is set up. Simulation and experimental results demonstrate the effectiveness of the ASM-FTC control.
TL;DR: The results demonstrate that the energy-efficient torque allocation scheme considerably improves the vehicle efficiency and increases the braking energy recovery compared with the conventional approach.
Abstract: Electric vehicles with a distributed drive train configuration provide great possibilities for the improvement of the vehicle dynamics, handling, safety as well as efficiency. In this paper, an energy-efficient torque allocation scheme is proposed for the improvement of traction efficiency and braking energy recovery. In traction conditions, the traction distribution is developed using an objective function of minimizing power loss of four electric motors. In braking conditions, aiming at guaranteeing the braking stability and recapturing the braking energy as much as possible, the changeable distribution of braking torque is obtained based on the ideal front-rear braking force distribution curve, while complying with braking regulations of Economic Commission for Europe. The proposed allocation scheme does not rely on the complex online computation. It is obtained via an offline optimization procedure and utilized for online allocation by simple interpolation. The low calculation effort makes it easy to implement the algorithm on real vehicles. Additionally, a conventional torque allocation is introduced as a contrasting approach. Finally, the simulations are conducted in CarSim and MATLAB/Simulink environment. The results demonstrate that the energy-efficient torque allocation scheme considerably improves the vehicle efficiency and increases the braking energy recovery compared with the conventional approach.
TL;DR: Control strategy is designed to track the desired sideslip angle and yaw rate and experiment results show that the designed controller can make the vehicle well track the reference model and improve the vehicle maneuverability.
Abstract: A four-wheel steer-by-wire vehicle (FSV), which is a combination of a steer-by-wire (SBW) system and a four wheel steering (4WS) system, not only can improve vehicle safety and maneuverability, but also steering flexibility. Considering the parameters, uncertainties of vehicle speed, and tire cornering stiffness, weighted function is solved to express the uncertain system. Aiming at the multiple-input multiple-output (MIMO) system, the structured singular value $\mu $ is used to research FSV under multiple perturbations in this paper. Based on $\mu $ control strategy, $\mu $ controller is designed to track the desired sideslip angle and yaw rate. Thus, the vehicle gets better performance. Compared with SBW and 4WS systems, FSV has better state response under steering angle step input simulation experiment. The advantages of $\mu $ control compared with $H$ ∞ control on FSV have been explained in the simulation experiments. Furthermore, experiment results show that the designed controller can make the vehicle well track the reference model and improve the vehicle maneuverability.
TL;DR: This paper proposes a model and algorithm for optimally designing DWC electric vehicle (EV) systems, particularly those operating in multiple-route environments, and applies a particle swarm optimization algorithm to solve the given multi-route DWC-EV system optimization problem.
Abstract: Dynamic wireless charging (DWC) technology, a novel way of supplying vehicles with electric energy, allows the vehicle battery to be recharged remotely while it is moving over power tracks, which are charging infrastructures installed beneath the road. DWC systems mitigate the range limitation of electric vehicles by using power tracks as additional sources of electric energy. This paper proposes a model and algorithm for optimally designing DWC electric vehicle (EV) systems, particularly those operating in multiple-route environments. Multi-route system comprises several single routes that share common road segments, and the vehicles operating on a specific route are equipped with identical batteries. We build a general model to optimally allocate power tracks and determine the vehicle battery size for each route. Then, we apply a particle swarm optimization algorithm to solve the given multi-route DWC-EV system optimization problem. A numerical example is solved to illustrate the characteristics of the multi-route model, and we show that the proposed modeling approach and algorithm are effective, compared with a mixed integer programming-based exact solution approach. We also conduct a sensitivity analysis to examine the solution behavior of the problem.