Control Problem Of Vehicle Research Articles

Co-Optimization of Design and Control of Energy Efficient Hybrid Electric Vehicles Using Coordination Schemes

AbstractDesign and control co-optimization studies for hybrid vehicles have been proposed in the past. However, such works suffer from difficulties arising due to (a) diverse real- and integer-valued variables, (b) complex nonlinear powertrain dynamics and design interconnections, (c) conflicting objective functions with path constraints, and (d) high computational resources requirements. To meet these challenges, this study presents an efficient co-optimization framework for hybrid electric vehicles (HEVs) which is built using existing algorithms and coordination schemes. Particular emphasis is given to the simultaneous scheme and the decomposition-based scheme. The decomposition-based scheme with the problem decomposition proposed in this work can efficiently handle multitime scale state variables and both integer- and real-valued design and control optimization variables. This is demonstrated by solving the mixed-integer optimal design and control problem of a series hybrid vehicle over a 1-h long drive cycle with time discretization of 1 s. The problem complexity is elevated by using an increasing number of state variables (including battery state of charge, battery energy, and after-treatment system temperature), control variables (such as the engine power and engine on/off), and design parameters (such as the number of battery cells and the type and size of the engine). In addition, a multi-objective cost function is used to find a tradeoff solution between fuel consumption and emissions minimization. The results show that in terms of optimality of the solution, the decomposition-based scheme is comparable with the simultaneous but can give a 14% improvement in computational performance. The effectiveness of the proposed framework is demonstrated by comparing the co-optimization results against a baseline case in which only the optimal control problem is solved. The co-optimized solution yields up to 3.7% average genset efficiency improvement and a fuel consumption reduction to 1.6 kg from 2.5 kg, which is further reduced to 1.5 kg by adding the engine on-off control. Finally, a decision matrix is developed to provide guidance on the selection of the optimization algorithm and coordination scheme for any problem at hand.

Adaptive Backstepping Axial Position Tracking Control of Autonomous Undersea Vehicles with Deferred Output Constraint

In this paper, an adaptive backstepping control scheme is proposed to solve the the surge motion tracking control problem of an autonomous undersea vehicle (AUV) with system constraint. First, an initial rectification reference signal is constructed for the subsequent implementation of deferred output constraint and making the control input smaller and smoother in the early stage of system operation. Second, a barrier Lyapunov function is adopted for developing an output-constrained state feedback adaptive controller. Then, on the basis of coordinate transformation and estimating the derivative of surge displacement by a linear and nonlinear combined differentiator, we develop an output feedback adaptive backstepping control scheme for AUVs whose velocity signals are unmeasurable. We also carried out a comparative numerical simulation with traditional adaptive control to verify the feasibility of the proposed control strategy.

Adaptive Fault-Tolerant Control of Hypersonic Vehicles with Unknown Model Inertia Matrix and System Induced by Centroid Shift

In this paper, the fault-tolerant control problem of hypersonic vehicle (HSV) in the presence of unexpected centroid migration, actuator failure and external interference is studied in depth. First, the proposed dynamics for HSV with the aforementioned unexpected factors are modeled to demonstrate the peculiar nature of the subject under study. The adverse effects of accidental centroid migration are mainly reflected in the following aspects: (1) the change of inertia matrix of the system, (2) the uncertainty of the system and (3) the eccentric moment, which are coupled and unknown. Subsequently, to account for the effect of unexpected centroid shifts, a sliding-mode observer and an adaptive estimator are designed to obtain unknowns useful for subsequent FTC controller designs. Later, we derived an innovative adaptive FTC scheme by employing the observer in conjunction with a specific adaptive controller consisting of a sixth-order dynamic compensator, which can guarantee the achievement of the control objective without resorting to the exact knowledge of the inertial matrix. Moreover, the analysis of boundedness with respect to the entire signal in this closed system is performed by means of the Lyapunov stability theory. Ultimately, simulation results show that the proposed FTC strategy is efficient and powerful.

Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles’ intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle’s own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, we test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.

Q-learning–based practical disturbance compensation control for hypersonic flight vehicle

Aiming at the attitude control problem of hypersonic flight vehicle, a compound control strategy based on the disturbance compensation technique and Q-learning optimization is proposed. Firstly, a three-channel independent control scheme based on the super-twisting extended state observer (STESO) is proposed such that coupling among channels, uncertainties, and external disturbances can be estimated and compensated in real time. Secondly, the Q-learning mechanism is introduced to optimize the control parameters while keeping the control structure unchanged. Lastly, the convergence of the STESO is analyzed by the Lyapunov theory, and some numerical simulation results are carried out to verify the effectiveness of the proposed strategy. Compared with the traditional linear active disturbance rejection control, the proposed method can avoid a lot of manual parameter tuning process and also has better performance.

Adaptive optimal trajectory tracking control of AUVs based on reinforcement learning

In this paper, an adaptive model-free optimal reinforcement learning (RL) neural network (NN) control scheme based on filter error is proposed for the trajectory tracking control problem of an autonomous underwater vehicle (AUV) with input saturation. Generally, the optimal control is realized by solving the Hamilton–Jacobi–Bellman (HJB) equation. However, due to its inherent nonlinearity and complexity, the HJB equation of AUV dynamics is challenging to solve. To deal with this problem, an RL strategy based on an actor–critic framework is proposed to approximate the solution of the HJB equation, where actor and critic NNs are used to perform control behavior and evaluate control performance, respectively. In addition, for the AUV system with the second-order strict-feedback dynamic model, the optimal controller design method based on filtering errors is proposed for the first time to simplify the controller design and accelerate the response speed of the system. Then, to solve the model-dependent problem, an extended state observer (ESO) is designed to estimate the unknown nonlinear dynamics, and an adaptive law is designed to estimate the unknown model parameters. To deal with the input saturation, an auxiliary variable system is utilized in the control law. The strict Lyapunov analysis guarantees that all signals of the system are semi-global uniformly ultimately bounded (SGUUB). Finally, the superiority of the proposed method is verified by comparative experiments.

Intelligent Event-Based Fuzzy Dynamic Positioning Control of Nonlinear Unmanned Marine Vehicles Under DoS Attack.

This article addresses the dynamic positioning control problem of a nonlinear unmanned marine vehicle (UMV) system subject to network communication constraints and deny-of-service (DoS) attack, where the dynamics of UMV are described by a Takagi-Sugeno (T-S) fuzzy system (TSFS). In order to save limited communication resource, a new intelligent event-triggering mechanism is proposed, in which the event triggering threshold is optimized by a Q -learning algorithm. Then, a switched system approach is proposed to deal with the aperiodic DoS attack occurring in the communication channels. With a proper piecewise Lyapunov function, some sufficient conditions for global exponential stability (GES) of the closed-loop nonlinear UMV system are derived, and the corresponding observer and controller gains are designed via solving a set of matrix inequalities. A benchmark nonlinear UMV system is adopted as an example in simulation, and the simulation results validate the effectiveness of the proposed control method.

Saturated-command-deviation based finite-time adaptive control for dynamic positioning of USV with prescribed performance

This paper investigates the finite-time adaptive dynamic positioning (DP) control problem of an unmanned surface vehicle (USV) with prescribed performance subject to time-varying disturbances, unknown model parameters and input saturation. First, the error transformation is performed to provide a framework for the design of model-free prescribed performance DP controller, which eliminates the need for accurate prior information of the model and external disturbances. Then, a saturated-command-deviation based compensated law is proposed by incorporating the fuzzy supervisory approach to account for the excess control efforts resulted by input saturation. Subsequently, the fuzzy logic systems (FLS) integrated with minimum learning parameters technique are employed to approximate the model-based and high-order terms, such that the explosion of computational complexity is removed and requirements for accurate modeling are eliminated. Meanwhile, to suppress the negative effects induced by time-varying disturbances and unknown approximation errors, the adaptive technique is adopted to estimate the upper boundary of compounded disturbances online. Finally, by incorporating the softening sign signal and piecewise function into recursive backstepping procedure, a DP controller is developed to avoid potential singularity and achieve finite time convergence. Theoretical analysis and simulation results successfully illustrate the effectiveness of the proposed scheme.

A predictive type-3 fuzzy control for underactuated surface vehicles

In this paper, a path-following control (PFC) problem of an underactuated surface vehicle (USV) is scrutinized based on an interval type-3 fuzzy logic system (IT3FLS). The suggested method is able to tackle the main limitations of the singleton fuzzy controller in terms of the approximation ability for uncertainties and exogenous disturbances of a complex nonlinear USV. Since a novel structure is employed for the FLS, the control policy is capable of ensuring robust performance against uncertainties with a wide range of variations and higher volumes of complex disturbances. Moreover, surge-guided line-of-sight (SGLOS) and auxiliary dynamics are utilized to design surge and heading control policies, adjusting the velocity of a vehicle and its position at each moment respective to the predefined path. Predictive compensators are designed to enhance the robustness by reducing the approximation error signals while the stability analysis of both tracking and approximation error signals are investigated based on the Lyapunov approach and Barbalat’s lemma. Finally, simulation results demonstrate the effectiveness of the suggested improved fuzzy-based control strategy to solve a PFC problem.

Automated Vehicle Path Following: A Non-Quadratic-Lyapunov-Function-Based Model Reference Adaptive Control Approach With C∞-Smooth Projection Modification

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}^{\mathbf {1+\alpha }}$ </tex-math></inline-formula> tracking performance. Second, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{C}^{ \boldsymbol {\infty }}$ </tex-math></inline-formula> -differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.

Robust Event Triggered Control for Lateral Dynamics of Intelligent Vehicle With Designable Inter-Event Times

In this brief, an improved event-triggered mechanism (ETM) whose inter-event times (IETs) are designable for the linear quadratic regulator is proposed to solve the lateral motion control problem of intelligent vehicle under bounded disturbances. Based on the IETs-designable event-triggered control mechanism proposed by our previous work, we further introduce two new design parameters into the clock-like variable dynamics in the event condition, so as to derive larger IETs. Distinct from existing ETMs, the above control framework features a global robust event-separation property (RESP) that is extremely required for resisting diverse disturbances, and its IETs are designable to cater to different needs, meaning that the proposed ETM can be deployed on the practical vehicle more easily and effectively. Theoretical analysis proves the stability and robustness of the proposed event-based lateral motion control strategy with the adjustability of IETs and global RESP. Finally, simulation results verify the theoretical results and illustrate the advantages of the proposed control strategy.

Hybrid event-triggered impulsive flocking control for multi-agent systems via pinning mechanism

This paper studies the leader-following flocking control problem of unmanned aerial vehicle (UAV) systems by integrating a hybrid impulsive framework, where the full exchange of information only occurs at each impulsive time instant. It is also shown that the overall stability can be achieved via impulses while the continuous dynamics remains destabilizing. Meanwhile, the even-triggered and pinning mechanisms are considered to further reduce control resource usage and transmission redundancy. Moreover, topology switching and strong nonlinearity are taken into account for more practical application. Based on transmission topology structure, impulsive control theory, and Lyapunov based event-triggered strategy, some improved theorems are derived to guarantee convergence of the corresponding error dynamics without exhibiting Zeno behaviour. Compared with most existing results on continuous or impulsive systems, the obtained stability criteria are more general as they are applicable to systems involving both delayed coupling feedback and delayed impulses. In addition, a novel approach using braking and gyroscopic forces is utilized for collision avoidance purposes. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.

Data-driven finite control set model predictive speed control of an autonomous surface vehicle subject to fully unknown kinetics and propulsion dynamics

This article investigates the speed control problem of an autonomous surface vehicle (ASV) suffering from fully unknown kinetics and propulsion dynamics. A data-driven finite control set model predictive control (FCS-MPC) strategy is presented for tracking a desired surge velocity. In the learning loop, a data-driven neural predictor is present to learn the unknown input gains, model nonlinearities, and environmental disturbances simultaneously. Filtered data of vehicle states are recorded and used to update the neural network parameters. In the control loop, a pulsewidth-modulation-driven speed controller is developed based on the FCS-MPC technique. The optimal control actions are selected based on the receding horizon optimization with a prediction model. The learning error system of the data-driven neural predictor is proved to be input-to-state stable via Lyapunov analysis. A major feature of the proposed method is that optimal performance can be provided for speed tracking without using any a priori knowledge of model parameters or external disturbances. Simulation results substantiate the effectiveness of the proposed data-driven FCS-MPC strategy for an ASV to track a reference surge velocity.

A Merging Strategy Based on Optimal Control of Main-lane Downstream and On-ramp Vehicles

The on-ramp merging on expressway is one of the hotspots of research, which usually leads to reduced traffic efficiency, increased risk of collision, and increased fuel consumption. However, most current on-ramp merging strategies coordinate merging by controlling upstream vehicles, which adversely effects on the main-lane traffic after the merging position (MP). To address this problem, this paper proposes an on-ramp merging strategy for connected and autonomous vehicles (CAVs) based on the collaboration of downstream main-lane vehicles and on-ramp vehicles. First, we propose a necessary communication topology to meet the information needs of vehicle collaboration in this strategy. Then, the downstream vehicles of the MP act using the feedback from the upstream vehicles of the MP to cooperate with the on-ramp vehicles. Meanwhile, in order to ensure the safe, we transform the vehicle control problem in the strategy into an optimization problem with state and input constraints, and the optimal solution is calculated using model predictive control (MPC). Finally, we use simulation experiments to verify, compare and analyze the proposed strategy. Simulation results show that the strategy is effective in preventing the deterioration of traffic conditions caused by merging behavior and has advantages in improving merging efficiency, reducing fuel consumption, and improving comfort.

Monte Carlo-based reinforcement learning control for unmanned aerial vehicle systems

In this paper, a new data-driven reinforcement learning method based on Monte Carlo simulation is developed to solve the optimal control problem of unmanned aerial vehicle (UAV) systems. Based on the data which are generated by Monte Carlo simulation, neural network (NN) is used to construct the dynamics of the UAV system with unknown disturbances, where the mathematical model of the UAV system is unnecessary. An effective iterative framework of action and critic is constructed to obtain the optimal control law. The convergence property is developed to guarantee that the iterative performance cost function converges to a finite neighborhood of the optimal performance cost function. Finally, numerical results are given to illustrate the effectiveness of the developed method.

Reinforcement learning‐based tracking control for a quadrotor unmanned aerial vehicle under external disturbances

AbstractThis article addresses the high‐accuracy intelligent trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV) subject to external disturbances. The tracking error systems are first reestablished by utilizing the feedforward control technique to compensate for the raw error dynamics of the quadrotor UAV. Then, two novel appointed‐fixed‐time observers are designed for the processed error systems to reconstruct the disturbance forces and torques, respectively. And the observation errors can converge to origin within the appointed time defined by users or designers. Subsequently, two novel control policies are developed utilizing reinforcement learning methodology, which can balance the control cost and control performance. Meanwhile, two critic neural networks are used to replace the traditional actor‐critic networks for approximating the solutions of Hamilton–Jacobi–Bellman equations. More specifically, two novel weight update laws are developed. They can not only update the weights of the critic neural networks online, but also avoid utilizing the persistent excitation condition innovatively. And that the ultimately uniformly bounded stability of the whole control system is proved according to Lyapunov method by utilizing the proposed reinforcement learning‐based control polices. Finally, simulation results are presented to illustrate the effectiveness and superior performances of the developed control scheme.

Adaptive prescribed performance second-order sliding mode tracking control of autonomous underwater vehicle using neural network-based disturbance observer

For trajectory tracking control problem of autonomous underwater vehicle (AUV) with model uncertainties, unknown nonlinear external disturbances, and input saturation, an observer-based adaptive second-order sliding mode control scheme with prescribed performance is proposed. Firstly, a finite time performance function (FTPF) is constructed to constrain the tracking error to the prescribed precision within the preset finite time, and achieve the prescribed dynamic convergence performance and tracking accuracy. Then, a neural network-based disturbance observer (NNDO) is designed to deal with model uncertainties and external disturbances, respectively. Based on prescribed performance control and sliding mode control technique, an adaptive prescribed performance second-order sliding mode control strategy was proposed. In addition, we construct an auxiliary system to overcome the effect of thrust saturation. Lyapunov method is applied to demonstrate the stability of the closed-loop system. Finally, the validity of the proposed control law is verified by numerical simulations.

Target Tracking Control of a Biomimetic Underwater Vehicle Through Deep Reinforcement Learning.

In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking control is converted into the Markov decision process and is further achieved via deep reinforcement learning. The system state and reward function of underwater target tracking control are described. Based on the actor-critic reinforcement learning framework, the deep deterministic policy gradient actor-critic algorithm with supervision controller is proposed. The training tricks, including prioritized experience replay, actor network indirect supervision training, target network updating with different periods, and expansion of exploration space by applying random noise, are presented. Indirect supervision training is designed to address the issues of low stability and slow convergence of reinforcement learning in the continuous state and action space. Comparative simulations are performed to show the effectiveness of the training tricks. Finally, the proposed actor-critic reinforcement learning algorithm with supervision controller is applied to the physical BUV. Swimming pool experiments of underwater object tracking of the BUV are conducted in multiple scenarios to verify the effectiveness and robustness of the proposed method.

AUV Formation Coordination Control Based on Transformed Topology under Time-Varying Delay and Communication Interruption

Aiming at the control problem of autonomous underwater vehicle (AUV) pilot-following formation with communication delay and communication interruption, a controller based on feedback linearization and the PD control method is designed in this paper. Firstly, the nonlinear, strongly coupled vehicle model is transformed into a second-order model via the feedback linearization method, and then the formation coordination controller is designed based on consistency theory and the PD control method. The Markov random jump process is used to simulate the formation topology in the event of communication interruption. The condition of stable convergence of the AUV pilot-following formation is analyzed in the presence of time-varying delay and Markov transformation topology. A Lyapunov–Krasovskii equation is established, and linear matrix inequality (LMI) is used to solve the problem of communication interruption and communication delay. The boundary conditions of error convergence of the control system are obtained. Finally, the effectiveness of the formation coordination controller based on the second-order integral model under the unstable conditions of underwater acoustic communication is verified by simulation.

Reinforcement learning reward function in unmanned aerial vehicle control tasks

This paper presents a new reward function that can be used for deep reinforcement learning in unmanned aerial vehicle (UAV) control and navigation problems. The reward function is based on the construction and estimation of the time of simplified trajectories to the target, which are third-order Bezier curves. This reward function can be applied unchanged to solve problems in both two-dimensional and three-dimensional virtual environments. The effectiveness of the reward function was tested in a newly developed virtual environment, namely, a simplified two-dimensional environment describing the dynamics of UAV control and flight, taking into account the forces of thrust, inertia, gravity, and aerodynamic drag. In this formulation, three tasks of UAV control and navigation were successfully solved: UAV flight to a given point in space, avoidance of interception by another UAV, and organization of interception of one UAV by another. The three most relevant modern deep reinforcement learning algorithms, Soft actor-critic, Deep Deterministic Policy Gradient, and Twin Delayed Deep Deterministic Policy Gradient were used. All three algorithms performed well, indicating the effectiveness of the selected reward function.