Showing papers on "Vehicle dynamics published in 2013"

Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics

Abstract: In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.

Model Predictive Control for Vehicle Stabilization at the Limits of Handling

Abstract: Recent developments in vehicle steering systems offer new opportunities to measure the steering torque and reliably estimate the vehicle sideslip and the tire-road friction coefficient. This paper presents an approach to vehicle stabilization that leverages these estimates to define state boundaries that exclude unstable vehicle dynamics and utilizes a model predictive envelope controller to bound the vehicle motion within this stable region of the state space. This approach provides a large operating region accessible by the driver and smooth interventions at the stability boundaries. Experimental results obtained with a steer-by-wire vehicle and a proof of envelope invariance demonstrate the efficacy of the envelope controller in controlling the vehicle at the limits of handling.

Adaptive Backstepping Control for Active Suspension Systems With Hard Constraints

Abstract: This paper proposes an adaptive backstepping control strategy for vehicle active suspensions with hard constraints. An adaptive backstepping controller is designed to stabilize the attitude of vehicle and meanwhile improve ride comfort in the presence of parameter uncertainties, where suspension spaces, dynamic tire loads, and actuator saturations are considered as time-domain constraints. In addition to spring nonlinearity, the piecewise linear behavior of the damper, which has different damping rates for compression and extension movements, is taken into consideration to form the basis of accurate control. Furthermore, a reference trajectory is planned to keep the vertical and pitch motions of car body to stabilize in predetermined time, which helps adjust accelerations accordingly to high or low levels for improving ride comfort. Finally, a design example is shown to illustrate the effectiveness of the proposed control law.

Vehicle Yaw Stability Control by Coordinated Active Front Steering and Differential Braking in the Tire Sideslip Angles Domain

Abstract: Vehicle active safety receives ever increasing attention in the attempt to achieve zero accidents on the road. In this paper, we investigate a control architecture that has the potential of improving yaw stability control by achieving faster convergence and reduced impact on the longitudinal dynamics. We consider a system where active front steering and differential braking are available and propose a model predictive control (MPC) strategy to coordinate the actuators. We formulate the vehicle dynamics with respect to the tire slip angles and use a piecewise affine (PWA) approximation of the tire force characteristics. The resulting PWA system is used as prediction model in a hybrid MPC strategy. After assessing the benefits of the proposed approach, we synthesize the controller by using a switched MPC strategy, where the tire conditions (linear/saturated) are assumed not to change during the prediction horizon. The assessment of the controller computational load and memory requirements indicates that it is capable of real-time execution in automotive-grade electronic control units. Experimental tests in different maneuvers executed on low-friction surfaces demonstrate the high performance of the controller.

Robust Adaptive Attitude Tracking on ${\rm SO}(3)$ With an Application to a Quadrotor UAV

Abstract: This brief describes robust adaptive tracking control systems for the attitude dynamics of a rigid body. Both the attitude dynamics and the proposed control system are globally expressed on the special orthogonal group, to avoid complexities and ambiguities associated with other attitude representations, such as Euler angles or quaternions. By designing an adaptive law for the inertia matrix of a rigid body, the proposed control system can asymptotically follow an attitude command without the knowledge of the inertia matrix, and it is extended to guarantee boundedness of tracking errors in the presence of unstructured disturbances. These are illustrated by the experimental results of the attitude dynamics of a quadrotor unmanned aerial vehicle.

Adaptive Robust Vibration Control of Full-Car Active Suspensions With Electrohydraulic Actuators

Abstract: This paper investigates the problem of vibration suppression in vehicular active suspension systems, whose aim is to stabilize the attitude of the vehicle and improve the riding comfort. A full-car model is adopted, and electrohydraulic actuators with highly nonlinear characteristics are considered to form the basis of accurate control. In this paper, the H∞ performance is introduced to realize the disturbance suppression by selecting the actuator forces as virtual inputs, and an adaptive robust control technology is further used to design controllers which help real force inputs track virtual ones. The resulting controllers are robust against both actuator parametric uncertainties and uncertain actuator nonlinearities. The stability analysis for the closed-loop system is given within the Lyapunov framework. Finally, a numerical example is given to illustrate the effectiveness of the proposed control law, where different road conditions are considered in order to reveal the closed-loop system performance in detail.

Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle

Abstract: This paper compares two optimal energy management methods for parallel hybrid electric vehicles using an Automatic Manual Transmission (AMT). A control-oriented model of the powertrain and vehicle dynamics is built first. The energy management is formulated as a typical optimal control problem to trade off the fuel consumption and gear shifting frequency under admissible constraints. The Dynamic Programming (DP) and Pontryagin’s Minimum Principle (PMP) are applied to obtain the optimal solutions. Tuning with the appropriate co-states, the PMP solution is found to be very close to that from DP. The solution for the gear shifting in PMP has an algebraic expression associated with the vehicular velocity and can be implemented more efficiently in the control algorithm. The computation time of PMP is significantly less than DP.

Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models

Abstract: In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly “controls” the forward velocity such that it tracks the desired velocity asymptotically The stability and optimal tracking performance have been rigorously established by theoretic analysis In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller

An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles

Abstract: We address the problem of real-time obstacle avoidance on low-friction road surfaces using spatial Nonlinear Model Predictive Control (NMPC). We use a nonlinear four-wheel vehicle dynamics model that includes load transfer. To overcome the computational difficulties we propose to use the ACADO Code Generation tool which generates NMPC algorithms based on the real-time iteration scheme for dynamic optimization. The exported plain C code is tailored to the model dynamics, resulting in faster run-times in effort for real-time feasibility. The advantages of the proposed method are shown through simulation.

Sensing vehicle dynamics for determining driver phone use

Abstract: This paper utilizes smartphone sensing of vehicle dynamics to determine driver phone use, which can facilitate many traffic safety applications. Our system uses embedded sensors in smartphones, i.e., accelerometers and gyroscopes, to capture differences in centripetal acceleration due to vehicle dynamics. These differences combined with angular speed can determine whether the phone is on the left or right side of the vehicle. Our low infrastructure approach is flexible with different turn sizes and driving speeds. Extensive experiments conducted with two vehicles in two different cities demonstrate that our system is robust to real driving environments. Despite noisy sensor readings from smartphones, our approach can achieve a classification accuracy of over $90\%$ with a false positive rate of a few percent. We also find that by combining sensing results in a few turns, we can achieve better accuracy (e.g., $95\%$) with a lower false positive rate.

Robust sliding mode control for Steer-by-Wire systems with AC motors in road vehicles

Abstract: In this paper, the modeling of steer-by-wire (SbW) systems is further studied, and a sliding mode control scheme for the SbW systems with uncertain dynamics is developed. It is shown that an SbW system, from the steering motor to the steered front wheels, is equivalent to a second-order system. A sliding mode controller can then be designed based on the bound information of uncertain system parameters, uncertain self-aligning torque, and uncertain torque pulsation disturbances, in the sense that not only the strong robustness with respect to large and nonlinear system uncertainties can be obtained but also the front-wheel steering angle can converge to the handwheel reference angle asymptotically. Both the simulation and experimental results are presented in support of the excellent performance and effectiveness of the proposed scheme.

Robust Estimation of Road Frictional Coefficient

Abstract: Knowledge of tire force potential, i.e., tire-road frictional coefficient, is important for vehicle active safety systems because tire-road friction is an effective measure of the safety margin of vehicle dynamics. For vehicle handling dynamics, the frictional coefficient is highly coupled with tire slip angle, therefore, they need to be estimated simultaneously when the latter is not measured. This paper presents an estimation algorithm based on a robust adaptive observer methodology. Stability and robustness of this observer are analyzed numerically. The performance is analyzed using computer simulations and experiments under various road and steering conditions.

Distributed Control of Coordinated Path Tracking for Networked Nonholonomic Mobile Vehicles

Abstract: This paper addresses the problem of coordinated path tracking for networked nonholonomic mobile vehicles, while building and keeping a desired formation. The control laws proposed are categorized into two envelopes by integrating individual path tracking and global virtual structure approaches. One is steering individual vehicles to track virtual vehicles moving along predefined paths, generated by a formation reference vehicle (FRV) of a time-varying desired virtual structure. The other is ensuring paths to be well tracked in order to build a geometric formation, through the distributed feedback law for path parameters related to the virtual vehicles, such that the physical vehicles are on the desired placements of the formation structure. Within this framework, geometric path tracking is achieved via nonlinear control theory, where an approaching angle is injected as a heading guidance design. The distributed feedback law is analyzed under communication constraints using algebraic graph theory. It is formally shown that the path tracking error of each vehicle is reduced to zero, and vehicles in the networked team globally asymptotically converge to a desired formation with equal path parameters. Simulation results illustrate the effectiveness of the proposed control design.

Optimal motion planning with the half-car dynamical model for autonomous high-speed driving

Abstract: We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we observe that the motion of a special point (namely, the front center of oscillation) can be modeled as a double integrator augmented with fictitious inputs. We first map the constraints on tire friction forces to constraints on these augmented inputs, which provides instantaneous, state-dependent bounds on the curvature of geometric paths feasibly traversable by the front center of oscillation. Next, we map the vehicle's actual inputs to the augmented inputs. The local steering problem for the half-car dynamical model can then be transformed to a simpler steering problem for the front center of oscillation, which we solve efficiently by first constructing a curvature-bounded geometric path and then imposing a suitable speed profile on this geometric path. Finally, we demonstrate the efficacy of the proposed motion planner via numerical simulation results.

Linear model predictive control for lane keeping and obstacle avoidance on low curvature roads

Abstract: This paper presents a control architecture based on a linear MPC formulation that addresses the lane keeping and obstacle avoidance problems for a passenger car driving on low curvature roads. The proposed control design decouples the longitudinal and lateral dynamics in two successive stages. First, plausible braking or throttle profiles are defined over the prediction horizon. Then, based on these profiles, linear time-varying models of the vehicle lateral dynamics are derived and used to formulate the associated linear MPC problems. The solutions of the optimization problems are used to determine for every time step, the optimal braking or throttle command and the corresponding steering angle command. Simulations show the ability of the controller to overcome multiple obstacles and keep the lane. Experimental results on an autonomous passenger vehicle driving on slippery roads show the effectiveness of the approach.

A Unified Approach to Threat Assessment and Control for Automotive Active Safety

Abstract: This paper presents the design of a novel active safety system preventing unintended roadway departures. The proposed framework unifies threat assessment, stability, and control of passenger vehicles into a single combined optimization problem. A nonlinear model predictive control (MPC) problem is formulated, where nonlinear vehicle dynamics, in closed-loop with a driver model, is used to optimize the steering and braking actions needed to keep the driver safe. A model of the driver's nominal behavior is estimated based on his observed behavior. The driver commands the vehicle, whereas the safety system corrects the driver's steering and braking actions in case there is a risk that the vehicle will unintentionally depart from the road. The resulting predictive controller is always active, and mode switching is not necessary. We show simulation results detailing the behavior of the proposed controller and experimental results obtained by implementing the proposed framework on embedded hardware in a passenger vehicle. The results demonstrate the capability of the proposed controller to detect and avoid roadway departures while avoiding unnecessary interventions.

Modeling and Velocity Control for a Novel Narrow Vehicle Based on Mobile Wheeled Inverted Pendulum

Abstract: Traffic problems such as pollution and congestion are becoming more and more serious in urban areas. A potential solution to these problems is to develop narrow vehicles that occupy less space and have lower emissions. There has been increasing interest in underactuated mechanical systems, i.e., mobile wheeled inverted pendulum (MWIP) models, which are widely used in the field of autonomous robotics and intelligent narrow vehicles. A novel narrow vehicle based on an MWIP and a movable seat, called UW-Car, is investigated in this paper. The dynamic model of the underactuated vehicle system running on flat ground is derived by Lagrange's equation of motion. Based on the dynamic model and terminal sliding mode control method, two terminal sliding mode controllers are designed to control velocity and braking of the UW-Car. The first one is used to control the forward speed to a desired value while keeping the body upright and the seat on some fixed position. The second one is a switching sliding mode controller, composed of three terminal sliding mode controllers that quickly brakes the system according to an optimal braking scheme. All the control algorithms are tested in both Matlab simulation and a UW-Car experiment. The simulation and experimental results demonstrate the efficiency of the model and controllers.

An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics

Abstract: This paper proposes an efficient neural network (NN) approach to tracking control of an autonomous surface vehicle (ASV) with completely unknown vehicle dynamics and subject to significant uncertainties. The proposed NN has a single-layer structure by utilising the vehicle regressor dynamics that expresses the highly nonlinear dynamics in terms of the known and unknown dynamic parameters. The learning algorithm of the NN is simple yet computationally efficient. It is derived from Lyapunov stability analysis, which guarantees that all the error signals in the control system are uniformly ultimately bounded (UUB). The proposed NN approach can force the ASV to track the desired trajectory with good control performance through the on-line learning of the NN without any off-line learning procedures. In addition, the proposed controller is capable of compensating bounded unknown disturbances. The effectiveness and efficiency are demonstrated by simulation and comparison studies.

Quadrotors flight formation control using a leader-follower approach

Abstract: In this paper it is presented a control strategy to solve the trajectory tracking and flight formation problem, in horizontal plane, of multiple unmanned aerial vehicles (UAVs) kind quadrotor, by means of a leader-follower scheme. Time scale separation of the translational and rotational quadrotor dynamics is used to achieve trajectory tracking. A sliding mode controller is proposed for the translational dynamic and provides the desired orientation for the UAV, which is controlled by a linear PD control. Finally, from the formation error dynamics, a sliding mode control is used by the follower to preserve the formation with respect to a leader. Experimental results, using a virtual leader and a follower in formation, are shown to evaluate the proposed control law.

Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient

Abstract: The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.

Optimal vehicle dynamics control for combined longitudinal and lateral autonomous vehicle guidance

Abstract: This contribution proposes a model-based predictive control approach for combined longitudinal and lateral vehicle guidance. The controller, which has been designed for an automotive collision avoidance system, aims at following a desired evasion trajectory at the handling limits. Thereby, the trajectory following problem is decomposed in a path following and a velocity trajectory tracking problem using the wheel steering angle and the longitudinal acceleration as control inputs. There are two major advantages of this approach. First, the a priori knowledge of the evasion trajectory is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the sense of an integrated vehicle dynamics control approach. Experimental results show the potential of the introduced control scheme.

Model predictive coordination of autonomous vehicles crossing intersections

Abstract: This paper presents a decentralized model predictive control approach for the coordination of autonomous vehicles at intersections. A linear quadratic optimal controller is introduced for each vehicle with predefined path, in order to minimize energy as well as to pass intersection smoothly. We guarantee collision avoidance by adding linear constraints to the optimization problem. We apply the so-called soft constraints for the collision avoidance problem to reduce the computation time in order to be able to run the simulations in real-time. In addition, the method can take into account crossing of vehicles in platoons by extending the linear quadratic cost functions.

Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation

Abstract: This paper presents design and experimental validation of a vehicle lateral controller for autonomous vehicle based on a higher-order sliding mode control. We used the super-twisting algorithm to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle and the output is the lateral displacement error. The particularity of such a strategy is to take advantage of the robustness of the sliding mode controller against nonlinearities and parametric uncertainties in the model, while reducing chattering, the main drawback of first order sliding mode. To validate the control strategy, the closed-loop system simulated on Matlab-Simulink has been compared to the experimental data acquired on our vehicle DYNA, a Peugeot 308, according to several driving scenarios. The validation shows robustness and good performance of the proposed control approach.

Data-Driven Design of Braking Control Systems

Abstract: The spread of active braking controllers on vehicles with significant mechanical differences and on low-cost products asks for control design approaches which offer easy and fast calibration and re-tuning capabilities. This task is made difficult by the use of model-based control approaches which heavily rely on specific vehicle dynamics descriptions. To address these issues, this brief paper proposes a data-driven approach to active braking control design, grounded on the virtual reference feedback tuning (VRFT) approach complemented with a data-driven nonlinear compensator. The effectiveness of the proposed approach is assessed both on a full-fledged multibody simulator and on a tire-in-the-loop experimental facility.

Adaptive trajectory tracking for quadrotor MAVs in presence of parameter uncertainties and external disturbances

Abstract: The paper presents an adaptive trajectory tracking control strategy for quadrotor Micro Aerial Vehicles. The proposed approach, while keeping the typical assumption of an orientation dynamics faster than the translational one, removes that of absence of external disturbances and of perfect symmetry of the vehicle. In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments, and to the uncertainty of dynamic parameters as the position of the center of mass of the vehicle. A stability analysis as well as numerical simulations are provided to support the control design.

EMBARC: error model based adaptive rate control for vehicle-to-vehicle communications

Abstract: Channel congestion is one of the major challenges for deployment of collision avoidance systems based on DSRC (Dedicated Short Range Communication) in large scale networks. If vehicles do not adapt to congestion conditions, DSRC transmissions could encounter extensive packet losses in areas of high vehicle density, leading to degradation in the performance of safety applications. In this paper, we propose a novel congestion control algorithm called Error Model Based Adaptive Rate Control (EMBARC) which adapts a vehicle's transmission rate as a function of channel load and vehicular dynamics. In particular, we extend Linear Integrated Message Rate Control (LIMERIC) algorithm's message rate adaptation with the capability to preemptively schedule messages based on the vehicle's movement. This leads to more transmission opportunities for vehicles with higher dynamics. The determination of a preemptive scheduling event is based on a novel suspected tracking error technique. Since LIMERIC maintains the channel load around a specific value, vehicles moving less dynamically will adapt to slightly reduced transmission rates in EMBARC. The extra transmit opportunities for highly dynamic vehicles reduce incidences of large tracking error compared to a pure LIMERIC approach. At the same time, EMBARC's use of adaptive rate control provides tracking error advantages over systems that transmit largely independent of channel load. We use simulations of a road with a winding segment to compare EMBARC with algorithms that do not take both channel load and vehicle dynamics into account. The results show that EMBARC has the best tracking accuracy among these algorithms over a wide range of node densities.

Preceding vehicle state prediction

Abstract: A model-based approach is presented for predicting future state (position and velocity) of the preceding vehicle in response to velocity disturbance from lead vehicle in a platoon. Online parameter estimation is used to adapt model parameters based on characteristics of individual drivers in the platoon. A car-following model is used to describe platoon longitudinal dynamics. Examples are presented using simulated as well as real-traffic data.

Stabilization of a flying vehicle on a taut tether using inertial sensing

Abstract: Given a hover-capable flying vehicle attached to a fixed point by a taut tether, we present a novel method to recover the vehicle's relative position and absolute orientation. The proposed method requires only on-board inertial sensors, and indirectly measures the string force, enabling the additional use of the tether as a physical user interaction medium. We present the vertical-plane dynamics of such a system and the localization approach, discuss sensitivity issues, and implement an estimator and controller based on the presented model. We demonstrate the method experimentally on a tethered quadrocopter in the Flying Machine Arena, using both a vertical-plane-constrained vehicle and in 3D.

New paradigms for the integration of yaw stability and rollover prevention functions in vehicle stability control

Abstract: The integration of rollover prevention and yaw stability control objectives in electronic stability control (ESC) has traditionally been done based on a priority calculation. The control system nominally focuses on yaw stability control until a danger of rollover is detected. When a danger of rollover is detected, the control system switches from yaw stability control to rollover prevention. This paper focuses on an integrated ESC system wherein the objectives of yaw stability and rollover prevention are addressed simultaneously, rather than one at a time. First, we show that staying on a desired planar trajectory at a specified speed results in an invariant rollover index. This implies that rollover prevention can be achieved whenever there is a danger of rollover only by reducing vehicle speed, since changing the desired vehicle trajectory is not a desirable option. In this regard, it is shown that a vehicle that reduces its speed before entering a sharp curve performs significantly better than a vehicle that uses differential braking during the turn for yaw stability control. Second, this paper explores how the use of steer-by-wire technology can address the tradeoff between yaw stability, speed, and rollover prevention performance. It is shown that the use of traditional steer-by-wire simply as an additional actuator cannot by itself ameliorate the tradeoff. However, this tradeoff can be eliminated if steer-by-wire is used to invert the direction of the roll angle of the vehicle. A new steer-by-wire algorithm that uses transient countersteering is shown to change the location of the rollover dynamics from the neighborhood of an unstable to a stable equilibrium. In this case, a desired trajectory can indeed be achieved by the vehicle at the same speed with a much smaller danger of rollover. This is a novel and viable approach to integrating the yaw stability and rollover prevention functions and eliminating the inherent tradeoffs in the performance of both.