Showing papers in "IEEE Transactions on Control Systems and Technology in 2017"
TL;DR: A new control structure is presented that integrates path tracking, vehicle stabilization, and collision avoidance and mediates among these sometimes conflicting objectives by prioritizing collision avoidance.
Abstract: Emergency scenarios may necessitate autonomous vehicle maneuvers up to their handling limits in order to avoid collisions. In these scenarios, vehicle stabilization becomes important to ensure that the vehicle does not lose control. However, stabilization actions may conflict with those necessary for collision avoidance, potentially leading to a collision. This paper presents a new control structure that integrates path tracking, vehicle stabilization, and collision avoidance and mediates among these sometimes conflicting objectives by prioritizing collision avoidance. It can even temporarily violate vehicle stabilization criteria if needed to avoid a collision. The framework is implemented using model predictive and feedback controllers. Incorporating tire nonlinearities into the model allows the controller to use all of the vehicle’s performance capability to meet the objectives. A prediction horizon comprised of variable length time steps integrates the different time scales associated with stabilization and collision avoidance. Experimental data from an autonomous vehicle demonstrate the controller safely driving at the vehicle’s handling limits and avoiding an obstacle suddenly introduced in the middle of a turn.
317 citations
TL;DR: It is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions, by using the sum of local cost functions as a Lyapunov candidate.
Abstract: This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between the predicted and assumed trajectories. Together with this penalization, an equality-based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of the proposed DMPC.
305 citations
TL;DR: A two-layer control architecture for heavy-duty vehicle platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon is proposed and a distributed model predictive control framework is developed for the real-time control of the vehicles.
Abstract: The operation of groups of heavy-duty vehicles at a short inter-vehicular distance, known as platoon, allows one to lower the overall aerodynamic drag and, therefore, to reduce fuel consumption and greenhouse gas emissions. However, due to the large mass and limited engine power of trucks, slopes have a significant impact on the feasible and optimal speed profiles that each vehicle can and should follow. Maintaining a short inter-vehicular distance, as required by platooning, without coordination between vehicles can often result in inefficient or even unfeasible trajectories. In this paper, we propose a two-layer control architecture for heavy-duty vehicle platooning aimed to safely and fuel-efficiently coordinate the vehicles in the platoon. Here, the layers are responsible for the inclusion of preview information on road topography and the real-time control of the vehicles, respectively. Within this architecture, dynamic programming is used to compute the fuel-optimal speed profile for the entire platoon and a distributed model predictive control framework is developed for the real-time control of the vehicles. The effectiveness of the proposed controller is analyzed by means of simulations of several realistic scenarios that suggest a possible fuel saving of up to 12% for follower vehicles compared with the use of standard platoon controllers.
297 citations
TL;DR: The control problem for flexible wings of a robotic aircraft is addressed by using boundary control schemes based on the original coupled dynamics, and bounded stability is proved by introducing a proper Lyapunov function.
Abstract: In this brief, the control problem for flexible wings of a robotic aircraft is addressed by using boundary control schemes Inspired by birds and bats, the wing with flexibility and articulation is modeled as a distributed parameter system described by hybrid partial differential equations and ordinary differential equations Boundary control for both wing twist and bending is proposed on the original coupled dynamics, and bounded stability is proved by introducing a proper Lyapunov function The effectiveness of the proposed control is verified by simulations
259 citations
TL;DR: A single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM and an estimation scheme that exploits the dynamical properties, such as marginal stability, local invertibility, and conservation of lithium.
Abstract: This paper studies a state estimation scheme for a reduced electrochemical battery model, using voltage and current measurements. Real-time electrochemical state information enables high-fidelity monitoring and high-performance operation in advanced battery management systems, for applications such as consumer electronics, electrified vehicles, and grid energy storage. This paper derives a single particle model (SPM) with electrolyte that achieves higher predictive accuracy than the SPM. Next, we propose an estimation scheme and prove estimation error system stability, assuming that the total amount of lithium in the cell is known. The state estimation scheme exploits the dynamical properties, such as marginal stability, local invertibility, and conservation of lithium. Simulations demonstrate the algorithm’s performance and limitations.
238 citations
TL;DR: A novel distributed voltage controller with nonlinear state feedback with event-triggered communication among inverters is proposed for reactive power sharing of the microgrid and can dramatically reduce the amount of communication, and significantly relax the requirement for precise real-time information transmission among the inverters.
Abstract: Due to the inherently distributed and heterogeneous nature of the microgrids, distributed control can be a promising approach to improve the stability, reliability, and scalability of the microgrids compared with centralized control strategies. This paper studies the distributed reactive power sharing problem for a microgrid with connected ac inverters. Under the standard decoupling approximation for bus angle differences, the reactive power flow of each inverter is dependent on the voltage amplitudes of its neighboring inverters connected by electrical power lines. Using the Lyapunov approach, a novel distributed voltage controller with nonlinear state feedback is proposed for reactive power sharing of the microgrid. It is proved that the inverters can achieve accurate reactive power sharing under the proposed controller if the communication network of inverters is connected. Then, by introducing the sampling and holding scheme, we extend the proposed controller to the nonlinear state feedback control with event-triggered communication among inverters. The new event-triggered control approach can dramatically reduce the amount of communication of the microgrid, and significantly relax the requirement for precise real-time information transmission among the inverters. Both the proposed controllers are validated by simulations on a group of inverters with time-varying loads.
180 citations
TL;DR: A comparative simulation study points out the intriguing performance properties of the proposed method, while its applicability is experimentally verified using a small unicycle-like underactuated underwater vehicle in a test tank.
Abstract: This paper addresses the tracking control problem of 3-D trajectories for underactuated underwater robotic vehicles. Our recent theoretical results on the prescribed performance control of fully actuated nonlinear systems are innovatively extended on the control of the most common types of underactuated underwater vehicles, namely, the torpedo-like (i.e., vehicles actuated only in surge, pitch, and yaw) and the unicycle-like (i.e., vehicles actuated only in surge, heave, and yaw). The main contributions of this paper concentrate on: 1) the reduced design complexity; 2) the increased robustness against system uncertainties; 3) the prescribed transient and steady-state performance; and 4) the minimal tracking information requirements. A comparative simulation study points out the intriguing performance properties of the proposed method, while its applicability is experimentally verified using a small unicycle-like underactuated underwater vehicle in a test tank.
159 citations
TL;DR: A novel distributed cascade robust feedback control approach is proposed for formation and reconfiguration control of a team of vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs) that guarantees intervehicle collision avoidance and considers dynamic constraints of UAVs.
Abstract: In this brief, a novel distributed cascade robust feedback control approach is proposed for formation and reconfiguration control of a team of vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs). This approach is based on dynamic communication network. It guarantees intervehicle collision avoidance and considers dynamic constraints of UAVs. In the outer loop of the cascade formation control, a potential field approach is used to generate a desired velocity for each UAV, which ensures that the team of UAVs can perform formation flying, formation rotating and reconfiguration, avoid intervehicle collision, as well as track a specified virtual leader. In the inner loop of the cascade formation control, the velocity of each UAV is designed to track its desired velocity generated by the outer loop, subject to dynamic constraints. The proposed approach is demonstrated via both simulation and flight test.
156 citations
TL;DR: A fast model predictive control (MPC)-based fuel economic control strategy for a group of connected vehicles in urban road conditions and the simulation results indicate the improvement in group performance and computational advantages of the proposed method.
Abstract: In this paper, we develop a fast model predictive control (MPC)-based fuel economic control strategy for a group of connected vehicles in urban road conditions. The proposed control strategy is decentralized in nature, as every vehicle evaluates its own strategy using only neighborhood information. Along with the vehicle-to-vehicle communication, we exploit the signal phase and timing information from traffic lights to develop computationally efficient MPC-based strategies that reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance and computational advantages of our proposed method.
146 citations
TL;DR: This paper develops a two-stage nonlinear nonconvex control approach for autonomous vehicle driving during highway cruise conditions that solves the optimization problem through the generalized minimal residual method augmented with a continuation method.
Abstract: This paper develops a two-stage nonlinear nonconvex control approach for autonomous vehicle driving during highway cruise conditions. The goal of the controller is to track the centerline of the roadway while avoiding obstacles. An outer-loop nonlinear model predictive control is adopted for generating the collision-free trajectory with the resultant input based on a simplified vehicle model. The optimization is solved through the generalized minimal residual method augmented with a continuation method. A sufficient condition to overcome limitations associated with continuation methods is introduced. The inner loop is a simple linear feedback controller based on an optimal preview distance. Simulation results illustrate the effectiveness of the approach. These are bolstered by scaled-vehicle experimental results.
137 citations
TL;DR: A new battery state-of-charge (SOC) estimation method for lithium-ion batteries (LIBs) based on a nonlinear fractional model with incommensurate differentiation orders with a Luenberger-type observer is presented.
Abstract: This paper presents a new battery state-of-charge (SOC) estimation method for lithium-ion batteries (LIBs) based on a nonlinear fractional model with incommensurate differentiation orders. A continuous frequency distributed model is used to describe the incommensurate fractional system. A Luenberger-type observer is designed for battery SOCestimation. The observer gain that can stabilize the zero equilibrium of the estimation error system is derived by Lyapunov’s direct method. The proposed SOC observer is examined using the real-time experimental data of LIBs. The robustness of the observer under different test conditions, including different aging levels, different driving cycles, and different cells, is also presented.
TL;DR: This work investigates the real-time feasible implementation of model predictive path-following control for an industrial robot, and considers constrained output path following with and without reference speed assignment.
Abstract: Many robotic applications, such as milling, gluing, or high precision measurements, require the precise following of a predefined geometric path. We investigate the real-time feasible implementation of model predictive path-following control for an industrial robot. We consider constrained output path following with and without reference speed assignment. Finally, we present the results of an implementation of the proposed model predictive path-following controller on a KUKA LWR IV robot.
TL;DR: In P-JITL, variational Bayesian principal component analysis is first utilized to handle missing values and extract Gaussian posterior distributions of latent variables, and a nonlinear regression model, Gaussian process regression, is carried out to model the nonlinear relationship between the output and the extracted latent variables.
Abstract: Just-in-time learning (JITL) is one of the most widely used strategies for soft sensor modeling in nonlinear processes. However, traditional JITL methods have difficulty in dealing with data samples that contain missing values. Meanwhile, data noises and uncertainties have not been taken into consideration for relevant sample selection in existing JITL approaches. To overcome these problems, a new probabilistic JITL (P-JITL) framework is proposed in this brief. In P-JITL, variational Bayesian principal component analysis is first utilized to handle missing values and extract Gaussian posterior distributions of latent variables. Then, symmetric Kullback–Leibler divergence is creatively employed to measure the dissimilarity of two distributions for relevant sample selection in the JITL framework. Finally, a nonlinear regression model, Gaussian process regression, is carried out to model the nonlinear relationship between the output and the extracted latent variables. In this way, the proposed probabilistic JITL (P-JITL) is able to deal with missing data and select relevant samples more accurately. To evaluate the effectiveness and flexibility of P-JITL, comparative studies between P-JITL and traditional deterministic JITL (D-JITL) are carried out on a numerical example and an industrial application example, in which missing data are simulated with percentages from 0% to 50%. The results show that P-JITL can provide more accurate prediction accuracy than D-JITL in each scenario considered.
TL;DR: It is shown that an inverter-interfaced microgrid under plug-and-play (PnP) functionality of distributed generations (DGs) can be cast as a linear time-invariant system subject to polytopic-type uncertainty.
Abstract: This paper proposes a decentralized control strategy for the voltage regulation of islanded inverter-interfaced microgrids. We show that an inverter-interfaced microgrid under plug-and-play (PnP) functionality of distributed generations (DGs) can be cast as a linear time-invariant system subject to polytopic-type uncertainty. Then, by virtue of this novel description and use of the results from theory of robust control, the microgrid control system guarantees stability and a desired performance even in the case of PnP operation of DGs. The robust controller is a solution of a convex optimization problem. The main properties of the proposed controller are that: 1) it is fully decentralized and local controllers of DGs that use only local measurements; 2) the controller guarantees the stability of the overall system; 3) the controller allows PnP functionality of DGs in microgrids; and 4) the controller is robust against microgrid topology change. Various case studies, based on time-domain simulations in MATLAB/SimPowerSystems Toolbox, are carried out to evaluate the performance of the proposed control strategy in terms of voltage tracking, microgrid topology change, PnP capability features, and load changes.
TL;DR: This brief presents the systematic design and real-time experimental results of a fault detection, isolation, and accommodation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques.
Abstract: This brief presents the systematic design and real-time experimental results of a fault detection, isolation, and accommodation algorithm for quadrotor actuator faults using nonlinear adaptive estimation techniques. The fault diagnosis architecture consists of a nonlinear fault detection estimator and a bank of nonlinear adaptive fault isolation estimators designed based on the functional structures of the faults under consideration. Adaptive thresholds for fault detection and isolation are systematically designed to enhance the robustness and fault sensitivity of the diagnostic algorithm. After fault isolation, the fault parameter estimate generated by the matched isolation estimator is used for accommodating the fault effect. Using an indoor quadrotor test environment, real-time experimental results are shown to illustrate the effectiveness of the algorithms.
TL;DR: A fault degradation-oriented Fisher discriminant analysis is proposed on the selected variables to model the fault evolution process and a SF-based non-steady faulty variable identification method is developed to find critical-to-fault-degradation variables.
Abstract: Fault prognostic determines whether a failure is impending and estimates how soon an incident will occur; it is nowadays recognized as a key feature in maintenance strategies. For slowly time-varying autocorrelated fault process, the fault degradation process can be revealed for fault prognostic. Based on this assumption, a fault degradation modeling and online fault prognostic strategy is developed in this paper. A stability factor (SF) is defined to evaluate the changing characteristics of process status and a SF-based non-steady faulty variable identification method is developed to find critical-to-fault-degradation variables. A fault degradation-oriented Fisher discriminant analysis is proposed on the selected variables to model the fault evolution process. Uninformative fault effects that do not present degradation are excluded, so that the critical fault degradation information can be focused on. The proposed method is verified by three cases, including a numerical case, cut-made process of cigarette, and the well-known Tennessee Eastman benchmark chemical process.
TL;DR: This paper proposes a two-level hierarchical control framework for large-scale urban traffic networks based on decomposing a heterogeneous traffic network into several homogeneous subnetworks and develops a distributed control scheme within each subnetwork at the lower level.
Abstract: Network-wide control of large-scale urban traffic networks using a hierarchical framework can be more efficient and flexible than centralized strategies for reducing the traffic congestion in big cities, because it can adequately address some problems that occur in controlling such large systems, e.g., computational complexity, multiple control objectives, weak robustness to uncertainties, and so on. In this paper, we propose a two-level hierarchical control framework for large-scale urban traffic networks. At the upper level, based on decomposing a heterogeneous traffic network into several homogeneous subnetworks, a higher level optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand-balance problem. At the lower level, the controller with a more detailed traffic flow model for each subnetwork determines the optimal signal timing within the given region under the guidance of the upper-level controller through communication. For the application of this architecture in real time, the model-based predictive control approach is utilized so as to obtain the best solutions for both levels. Moreover, in order to decrease the computational complexity, a distributed control scheme within each subnetwork is developed at the lower level. The proposed approach is evaluated by simulation under different scenarios on a hypothetical urban traffic network, and the performance is compared with that of other control strategies.
TL;DR: This brief provides a preliminary research on the autonomous cooperative takeoff for miniature fixed-wing UAVs, by considering collision avoidance, communication failure, etc.
Abstract: This brief is concerned with integrated autonomous takeoff, target search, task assignment, and tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) in urban environments. The problem is to design flight autonomy that is embedded onboard each UAV to enable autonomous flight coordination and distributed tasking. Control logic design based on a finite state automaton model, integrating four modes of operations, namely, the takeoff mode, the fly-to-area of operation mode, the search mode, and the tracking mode, is presented. Different from the state-of-the-art of recent research, this brief provides a preliminary research on the autonomous cooperative takeoff for miniature fixed-wing UAVs, by considering collision avoidance, communication failure, etc. To make UAVs autonomously and cooperatively search roads in the urban environments, an efficient improved search algorithm is proposed based on recent research on the coverage search in the literature. For the target tracking, using geometric relations (relative position, orientations, speed ratio, and minimal turning radius), a systematic algorithm is developed to generate an optimal online path. All the algorithms in this work are developed based on realistic miniature fixed-wing UAV dynamic models. The main focus of the brief is to test the developed control logic and also the algorithms. The proposed framework is evaluated by our 3-D multi-UAV test bed.
TL;DR: By augmenting the desired output trajectory to a reference dynamical system, the tracking task can fit into the standard NMPC framework, which effectively avoids possible numerical difficulties in the following fast NMPC implementation.
Abstract: This brief presents a nonlinear model predictive control (NMPC) method for the trajectory tracking problem of an autonomous underwater vehicle (AUV). By augmenting the desired output trajectory to a reference dynamical system, the tracking task can fit into the standard NMPC framework, which effectively avoids possible numerical difficulties in the following fast NMPC implementation. To relieve the conflict between short sampling period and high demand of online calculation, Ohtsuka’s continuation/generalized minimal residual (C/GMRES) algorithm is investigated. In order to handle the realistic constraints on the AUV thrusters, we incorporate the log barrier functions into the cost function and modify the C/GMRES algorithm. Several different reference trajectories are tested using the identified dynamic model of the Saab SeaEye Falcon open-frame ROV/AUV, which demonstrate the effectiveness and efficiency of the proposed fast algorithm for the AUV tracking control.
TL;DR: This brief addresses attitude tracking problems for an over-actuated spacecraft in the presence of actuator faults, imprecise fault estimation, and external disturbances by proposing a robust control allocation (RobCA) strategy.
Abstract: This brief addresses attitude tracking problems for an over-actuated spacecraft in the presence of actuator faults, imprecise fault estimation, and external disturbances. First, a model reference adaptive control technique is used to design a high-level controller to produce the three-axis virtual control torque. Then, taking fault estimation uncertainties into account, a robust control allocation (RobCA) strategy is proposed to redistribute virtual control signals to the remaining actuators when an actuator fault occurs. The RobCA is formulated as a min–max optimization problem, which deals with actuator faults directly without reconfiguring the controller and ensures some robustness of system performances. Finally, simulation results are provided to show the effectiveness of the overall control strategy.
TL;DR: In this brief, a boundary control scheme is designed to suppress the vibration for a nonlinear drilling riser system using the Lyapunov’s direct method to guarantee the closed-loop system being exponentially stable.
Abstract: In this brief, a boundary control scheme is designed to suppress the vibration for a nonlinear drilling riser system. Considering the varying length, varying tension, and varying speed of the riser, the drilling riser is modeled as a moving Euler–Bernoulli beam system, which is described by a partial differential equation and four ordinary differential equations. Employing the Lyapunov’s direct method, boundary control is proposed to guarantee the closed-loop system being exponentially stable. Extensive numerical simulations are presented to show the effectiveness of the proposed control law.
TL;DR: A new inner-loop/outer-loop robot controller formulation is developed that makes pHRI robust to changes in both corobot and human user and allows formal inclusion in an outer design of both an ideal task model and unknown human operator dynamics.
Abstract: Corobotics involves humans and robots working collaboratively as a team. This requires physical human-robot interaction (pHRI) systems that can adapt to the preferences of different humans and have good robustness and stability properties. In this brief, a new inner-loop/outer-loop robot controller formulation is developed that makes pHRI robust to changes in both corobot and human user. First, an inner-loop controller with guaranteed robustness and stability causes a robot to behave like a prescribed admittance model. Second, an outer-loop controller tunes the admittance model so that the robot system assists humans with varying levels of skill to achieve task-specific objectives. This design technique cleanly separates robot-specific control from task performance objectives and allows formal inclusion in an outer design of both an ideal task model and unknown human operator dynamics. Experimental results with the controllers running on a PR2 robot demonstrate the effectiveness of this approach.
TL;DR: This brief proposes a distributed predictive path following controller with arrival time awareness for multiple waterborne automated guided vessels (waterborne AGVs) applied to interterminal transport (ITT) by iteratively incorporating in local problems adaptive global information to improve convergence rates.
Abstract: This brief proposes a distributed predictive path following controller with arrival time awareness for multiple waterborne automated guided vessels (waterborne AGVs) applied to interterminal transport (ITT). The goal is to design an efficient cooperative distributed algorithm that solves local problems in parallel and minimizes an overall objective. We model the ITT problem using waterborne AGVs with independent dynamics and objectives but coupling collision avoidance constraints. The problem is then solved by distributed model predictive control (DMPC) of which the parallelism is realized using the alternating direction method of multipliers (ADMM). Successive linearizations are utilized to maintain a tradeoff among computational complexity, optimality, and ease of decomposition. Moreover, we propose a fast ADMM by iteratively incorporating in local problems adaptive global information to improve convergence rates. Simulation results for an ITT case study illustrate the effectiveness of the proposed algorithms for DMPC of time-varying networks in general and cooperative distributed waterborne AGVs in particular.
TL;DR: This paper proposes a novel data-driven nonlinear state-space modeling method for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques together and shows that this simplified model can not only significantly reduce the computational complexity, but can also exhibit a good reliability and accuracy for a stable prediction ofMIQ indices.
Abstract: Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multiphases and multifields interactions with long time delay phenomena take place during its operation. In BF operation, the molten iron temperature as well as the Si content ([Si]), the phosphorus content ([P]), and the sulfur content ([S]) is the most essential quality (MIQ) indices. The measurement, modeling, and control of these indices have always been important issues in metallurgic engineering and automation. This paper proposes a novel data-driven nonlinear state-space modeling method for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques together. First, to improve modeling efficiency, a data-driven hybrid method that combines canonical correlation analysis and correlation analysis is established to identify the most influential controllable variables as the modeling inputs from multitudinous factors. Then, a Hammerstein model for the prediction of MIQ indices is established using the Least squares support vector machine-based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method. Compared with the original Hammerstein model, it has been shown that this simplified model can not only significantly reduce the computational complexity, but can also exhibit a good reliability and accuracy for a stable prediction of MIQ indices. In order to verify the practicability of the developed model, it is applied to the design of a genetic algorithm based nonlinear predictive controller for the multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approaches.
TL;DR: The proposed autoregressive dynamic latent variable model is a rather general dynamic model which can improve the performance of modeling and process monitoring and can capture both dynamic and static relationships simultaneously.
Abstract: In most industrial processes, both autocorrelations and cross correlations in the data need to be extracted for the purpose of process monitoring and diagnosis. However, traditional dynamic modeling methods focus on the dynamic relationship while the cross correlations are at best implicit. In this brief, a new autoregressive dynamic latent variable model is proposed to capture both dynamic and static relationships simultaneously. The proposed method is a rather general dynamic model which can improve the performance of modeling and process monitoring. The Kalman filter and smoother are employed for inference while model parameters are estimated with an expectation-maximization algorithm. The corresponding fault detection method is also developed and a numerical example and the Tennessee Eastman benchmark process are used to evaluate the performance of the proposed model.
TL;DR: This brief investigates the observability of one of the most commonly used equivalent circuit models for lithium-ion batteries and presents a method to estimate the state of charge in the presence of sensor biases, highlighting the importance of observability analysis for choosing appropriate state estimation algorithms.
Abstract: This brief investigates the observability of one of the most commonly used equivalent circuit models (ECMs) for lithium-ion batteries and presents a method to estimate the state of charge in the presence of sensor biases, highlighting the importance of observability analysis for choosing appropriate state estimation algorithms. Using a differential geometric approach, necessary and sufficient conditions for the nonlinear ECM to be observable are derived and are shown to be different from the conditions for the observability of the linearized model. It is then demonstrated that biases in the measurements, due to sensor aging or calibration errors, can be estimated by applying a nonlinear Kalman filter to an augmented model where the biases are incorporated into the state vector. Experiments are carried out on a lithium-ion pouch cell and three types of nonlinear filters, the first-order extended Kalman filter (EKF), the second-order EKF, and the unscented Kalman filter, are applied using experimental data. The different performances of the filters are explained from the point of view of observability.
TL;DR: A novel semiactive-inerter-based adaptive tuned vibration absorber (SIATVA) that can be applied to a variety of primary systems without resetting the parameters and can also tolerate the parameter variation of the primary system.
Abstract: This brief presents a novel framework to realize the semiactive inerter, and proposes a novel semiactive-inerter-based adaptive tuned vibration absorber (SIATVA). The proposed semiactive inerter can be realized by replacing the fixed-inertia flywheel in the existing flywheel-based inerters with a controllable-inertia flywheel. Then, by using the proposed semiactive inerter, an SIATVA is constructed, and two control methods, that is, the frequency-tracker-based (FT) control and the phase-detector-based (PD) control, are derived. The experimental results show that both the FT control and the PD control can effectively neutralize the vibration of the primary mass, although the excitation frequency may vary. The proposed SIATVA can also tolerate the parameter variation of the primary system. As a result, it can be applied to a variety of primary systems without resetting the parameters. The performance degradation by the inherent damping is also demonstrated.
TL;DR: The performance of most aspects of the proposed scheme is illustrated by considering various simulations of the control framework applied to a high-fidelity vehicle dynamics model of the (semi-)autonomous vehicle in typical public driving events, such as intersections, passing, emergency braking, and collision avoidance.
Abstract: In this paper, a predictive trajectory guidance and control framework is proposed that enables the safe operation of autonomous and semiautonomous vehicles considering the constraints of operating in dynamic public traffic. The core module of the framework is a nonlinear model predictive guidance module that uses a computationally expedient curvilinear frame for the description of the road and of the motion of the vehicle and other objects. The module enforces constraints generated from information about obstacles/other vehicles/objects, public traffic rules for speed limits and lane boundaries, and the limits of the vehicle’s dynamics. The module can be configured in two basic modes. The first is a tracking mode, where the control inputs computed by the model predictive guidance module act as references for traditional lower level control systems. The second is a planning mode, where the traffic-optimal state trajectories computed by the model predictive control are reinterpreted for planning the optimal path and speed, which in turn can be tracked by an elaborate speed and path tracking controller. The performance of most aspects of the proposed scheme is illustrated by considering various simulations of the control framework applied to a high-fidelity vehicle dynamics model of the (semi-)autonomous vehicle in typical public driving events, such as intersections, passing, emergency braking, and collision avoidance. The feasibility of the proposed control framework for real-time application is highlighted with the discussions of the computational execution times observed for these various scenarios.
TL;DR: The main purpose of this paper is to devise an electrothermal control scheme for cases where full future driving information is not accessible and the results show that a rather short prediction horizon is sufficient to achieve robust control performance.
Abstract: Thermal and state-of-charge (SOC) imbalances are well known to cause nonuniform aging in batteries. This paper presents the electrothermal control of a multilevel converter-based modular battery to address this issue. The modular battery provides a large redundancy in synthesizing terminal voltage, which gives extra degrees of freedom in control on cell level. There are multiple tightly coupled control objectives including the simultaneous thermal and SOC balancing as well as battery terminal voltage control. The main purpose of this paper is to devise an electrothermal control scheme for cases where full future driving information is not accessible. The control scheme is based on decomposition of controller into two orthogonal components, one for voltage control and the other for balancing control. This problem decomposition enables the application of constrained linear quadratic model predictive control scheme to solve the balancing problem elegantly. The control scheme is thoroughly evaluated through simulations of a four cell modular battery. The results show that a rather short prediction horizon is sufficient to achieve robust control performance.
TL;DR: This paper presents an automatic loop-shaping method for designing proportional integral derivative controllers using criteria for load disturbance attenuation, measurement noise injection, set-point response and robustness to plant uncertainty.
Abstract: This paper presents an automatic loop-shaping method for designing proportional integral derivative controllers. Criteria for load disturbance attenuation, measurement noise injection, set-point response and robustness to plant uncertainty are given. One criterion is chosen to be optimized with the remaining ones as constraints. Two cases are considered: M-constrained integral gain optimization and minimization of the cost of feedback according to quantitative feedback theory. Optimization is performed using a convex–concave procedure (CCP). The method that relies on solving a sequence of convex optimization problems converges to a local minimum or a saddle point. The proposed method is illustrated by examples.