Tracking Control Of Vehicles Research Articles

Robust Speed Tracking Control for Future Electric Vehicles under Network-Induced Delay and Road Slope Variation.

Integrated motor-transmission (IMT) powertrain systems are widely used in future electric vehicles due to the advantages of their simple structure configuration and high controllability. In electric vehicles, precise speed tracking control is critical to ensure good gear shifting quality of an IMT powertrain system. However, the speed tracking control design becomes challenging due to the inevitable time delay of signal transmission introduced by the in-vehicle network and unknown road slope variation. Moreover, the system parameter uncertainties and signal measurement noise also increase the difficulty for the control algorithm. To address these issues, in this paper a robust speed tracking control strategy for electric vehicles with an IMT powertrain system is proposed. A disturbance observer and low-pass filter are developed to decrease the side effect from the unknown road slope variation and measurement noise and reduce the estimation error of the external load torque. Then, the network-induced delay speed tracking model is developed and is upgraded considering the damping coefficient uncertainties of the IMT powertrain system, which can be described through the norm-bounded uncertainty reduction method. To handle the network-induced delay and parameter uncertainties, a novel and less-conservative Lyapunov function is proposed to design the robust speed tracking controller by the linear matrix inequality (LMI) algorithm. Meanwhile, the estimation error and measurement noise are considered as the external disturbances in the controller design to promote robustness. Finally, the results demonstrate that the proposed controller has the advantages of strong robustness, excellent speed tracking performance, and ride comfort over the current existing controllers.

Model predictive trajectory tracking control of unmanned vehicles based on radial basis function neural network optimisation

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.

Optimal control of high speed unmanned vehicle path tracking

In order to ensure the accuracy and stability of tracking control of driverless vehicle at high speed, an improved model predictive control method was designed. First of all, for the current high-speed unmanned vehicle tracking control field, there is little research and the tracking accuracy is poor. Starting from the three-degree-of-freedom vehicle dynamics model, by analyzing the vehicle yaw stability, the model is added with envelope constraints, and the linear time is derived. The variable path tracking controller model is designed with or without additional constraints. Through Matlab and CarSim co-simulation, it is shown that the MPC controller with envelope constraint has higher tracking accuracy than the ordinary MPC controller, and the vehicle has better robustness to the road adhesion coefficient, and the vehicle driving stability is significantly improved.

Learning‐based robust control methodologies under information constraints

Learning‐based robust control methodologies under information constraints

Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network

Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.

Path tracking control for autonomous vehicles with saturated input: A fuzzy fixed‐time learning control approach

This paper presents a fuzzy fixed-time learning path tracking control strategy for autonomous vehicles with saturated input and uncertainties. First, Takagi-Sugeno (T-S) model is designed to realize an accurate description of the nonlinear path tracking systems. The T-S model is the weighted sum of a series of linear dynamics models. The weighting coefficients are described by fuzzy membership functions. Then, with the consideration of the saturated input, a fixed-time learning control is proposed. It contains a learning law to estimate the uncertainties of the system on-line. The fixed-time convergence and the robustness properties are analyzed through Lyapunov stability theory. Finally, the effectiveness of the proposed control method has been verified through the CarSim/Simulink co-simulations under different driving conditions. The results demonstrate that the developed controller significantly improves the performance of path tracking and follows the desired path well even in the case of parameter uncertainties and external disturbances.

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.

Time-Varying Formation Tracking Control for Unmanned Aerial Vehicles: Theories and Applications

Time-varying formation tracking (TVFT) problems for unmanned aerial vehicles (UAVs) swarm systems with the leader’s unknown input are studied. The follower UAVs form an expected formation while tracking the leader UAV. According to the information of the neighboring UAVs, a TVFT control protocol is constructed. It’s proved by Lyapunov stability theory that the error of each follower UAV converge to a specified boundary, which means that the swarm system of UAVs can realize the practical TVFT. A platform consisting of five Tello quadrotors and ultrawideband (UWB) positioning technology is given. The effectiveness of the TVFT control protocol is verified on numerical simulation and Tello-UWB experiment platform.

NN-Based Fixed-Time Attitude Tracking Control for Multiple Unmanned Aerial Vehicles with Nonlinear Faults

This paper focuses on the fixed-time fault-tolerant attitude tracking control problem for multiple unmanned aerial vehicles (MUAVs) with nonaffine nonlinear faults. First, the command filter and neural networks (NNs) are employed to characterize unknown nonlinearities in MUAVs, and the update law of NN is developed via convex optimization technique. Second, the algebraic loop problem caused by nonaffine nonlinear faults is adequately solved by introducing the Butterworth low-pass filter. Then, the curve-fitting method is utilized to construct a piecewise virtual control signal to avoid the singularity problem in fixed-time control. Furthermore, based on the Lyapunov stability theory, a general fixed-time stability criterion is adopted to prove that the designed fault-tolerant attitude controller can guarantee the stability of MUAVs in fixed time. Finally, the effectiveness of the proposed control design method is verified via illustrative examples.

Finite-Time Prescribed Performance Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles Based on a Tan-Type Barrier Lyapunov Function

This paper proposed a finite-time prescribed performance control scheme for underactuated autonomous underwater vehicles (AUVs) based on adaptive neural networks and a tan-type barrier Lyapunov function. Even in the presence of output constraints and environmental disturbances, the AUV can also precisely track the desired trajectory in a finite time. By introducing a tan-type barrier Lyapunov Function (TBLF), the singularity problem in process design is solved and all output errors are guaranteed to satisfy the prescribed performance specifications. Dynamic surface control (DSC) and the minimal learning parameter (MLP) are employed to greatly simplify the complexity of the algorithm and enhance the robustness of the control system, respectively. Lyapunov stability analysis proves that the proposed controller guarantees all signals in the closed-loop system to be uniformly ultimately bounded (UUB), and that the tracking errors converge to a small neighborhood near the origin in a finite time. Finally, the simulation results demonstrate the effectiveness and feasibility of the proposed controller.

Modeling of an unmanned aerial vehicle and trajectory tracking control using backstepping approach

A quadcopter is a type of unmanned under-actuated aerial vehicle (UAV) that has four arms, each connected to a motor (Hussein, 2020). It is an excellent platform for research on control, due to its high non-linearity and the high degree of coupling in its dynamic representation of the model. This paper deals with the modeling of this nonlinear system using the Newton-Euler formalism, combining the dynamics of translation and rotation of the vehicle. The control objective is to track the reference trajectory by managing altitude and attitude at the same time. A control structure based on two cascade loops is developed using the nonlinear Backstepping approach with Lyapunov's stability theory. Simulation results in Matlab/Simulink show that the control objective is met.

Vehicle Model Predictive Trajectory Tracking Control with Curvature and Friction Preview

Autonomous vehicle trajectory tracking control is challenged by situations of varying road surface friction, especially in the scenario where there is a sudden decrease in friction in an area with high road curvature. If the situation is unknown to the control law, vehicles with high speed are more likely to lose tracking performance and/or stability, resulting in loss of control or the vehicle departing the lane unexpectedly. However, with connectivity either to other vehicles, infrastructure, or cloud services, vehicles may have access to upcoming roadway information, particularly the friction and curvature in the road path ahead. This paper introduces a model-based predictive trajectory-tracking control structure using the previewed knowledge of path curvature and road friction. In the structure, path following and vehicle stabilization are incorporated through a model predictive controller. Meanwhile, long-range vehicle speed planning and tracking control are integrated to ensure the vehicle can slow down appropriately before encountering hazardous road conditions. This approach has two major advantages. First, the prior knowledge of the desired path is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the controller to avoid violation of tire force limits while keeping performance and stability guarantees. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a sharply curving road with varying friction conditions, with results showing that the controller can drive a vehicle up to the handling limits and track the desired trajectory accurately.

Space‐and‐time‐synchronized simultaneous fully‐actuated vehicle tracking/formation using cascaded prescribed‐time control

AbstractIn this article, we present a space‐and‐time‐synchronized control method with application to the simultaneous tracking/formation of fully‐actuated vehicles. In the framework of polar coordinates, through correlating and decoupling the reference/actual kinematics between the self vehicle and target, time and space are separated, controlled independently. As such, the specified state can be achieved at the predetermined terminal time, meanwhile, the relative trajectory in space is independent of time. In addition, for the stabilization before the predesigned time, a cascaded prescribed‐time control theorem is provided as the preliminary of vehicle tracking control. The obtained results can be directly extended to the simultaneous tracking/formation of multiple vehicles. Finally, numerical examples are provided to verify the effectiveness and superiority of the proposed scheme.

Trajectory Tracking Control of Intelligent Electric Vehicles Based on the Adaptive Spiral Sliding Mode

Aiming at the multi-objective control problem of the tracking effect and vehicle stability in the process of intelligent vehicle trajectory tracking, a coordinated control strategy of the trajectory tracking and stability of intelligent electric vehicles is proposed based on the hierarchical control theory. The vehicle dynamics model and trajectory tracking model are established. In order to tackle the chattering problem in the traditional sliding mode controller, an Adaptive Spiral Sliding Mode controller is designed by taking the derivative of the controller as the upper controller, which is intended to reduce the heading deviation and lateral deviation in the trajectory tracking process whilst ensuring the stability of the vehicle itself. In the lower controller, a four-wheel tire force optimal distribution method is designed. According to the requirements of the upper controller, combined with the yaw stability of the vehicle, the directional control distribution of the four-wheel tire force is realized. A joint simulation model was built based on CarSim and Simulink, and simulation experiments were performed. The results show that the proposed control strategy can effectively control the heading deviation and lateral deviation in the vehicle trajectory tracking while ensuring the lateral stability of the vehicle.

Trajectory Tracking Control for Intelligent Vehicles Based on Cut-In Behavior Prediction

For intelligent vehicles, trajectory tracking control is of vital importance. However, due to the cut-in possibility of adjacent vehicles, trajectory planning of intelligent vehicles is challenging. Therefore, this paper proposes a trajectory tracking control method based on cut-in behavior prediction. A method of cut-in intention recognition is adopted to judge the possibility of adjacent vehicle and the driver preview model is used to predict the trajectory of the cut-in vehicle. The three driving scenarios are divided to manage trajectory planning under different cut-in behaviors. At the same time, the safety distance model is established as the basis for scene conversion. Taking the predicted trajectory of the cut-in vehicle as a reference, the model predictive control (MPC) method is used to plan and control the driving trajectory of the subject vehicle, so as to realize the coordinated control of the subject vehicle and the cut-in vehicle. Finally, the simulation shows that the subject vehicle can effectively recognize the cut-in intention of the adjacent vehicle and predict its trajectory. Facing with the cut-in vehicle, the subject vehicle can take appropriate control actions in advance to ensure the safety. Finally, a smoother coordinate control process is obtained between the subject vehicle and the cut-in vehicle.

Robust trajectory tracking control of underactuated unmanned surface vehicles with exponential stability: theory and experimental validation

PurposeTrajectory tracking is an important issue to underactuated unmanned surface vehicles (USVs). However, parametric uncertainties and environmental disturbances bring great challenges to the precise trajectory tracking control of USVs. This paper aims to propose a robust trajectory tracking control algorithm with exponential stability for underactuated USVs with parametric uncertainties and unknown environmental disturbances.Design/methodology/approachIn this method, the backstepping method and sliding mode control method are combined to ensure that the underactuated USV can track and maintain the desired trajectory. In addition, a modified switching-gain adaptation algorithm is adopted to enhance the robustness and reduce chattering. Besides, the global exponential stability of the closed-loop system is proved by Lyapunov’s direct method.FindingsThe proposed method in this paper offers a robust trajectory tracking solution to underactuated USVs and it is verified by simulations and experiments. Compared with the traditional proportion-integral-derivative method and several state-of-the-art algorithms, the proposed method has superior performance in simulation and experimental results.Originality/valueThis paper proposes a robust trajectory tracking control algorithm with exponential stability for underactuated USVs. The proposed method achieves exponential stability with better robustness and transient performance.

Oriented manifold-based error tracking control for nonholonomic wheeled autonomous vehicles on Lie group for curvilinear approach

This paper considers the trajectory tracking control of wheeled autonomous vehicles (WAV) with slipping in the wheels, i.e., when the kinematic constraints are not satisfied. Usually, the coordinates system used to represent all control problems suggest invariant subspaces mutually orthogonal, but this approach can not be enough to treat curvatures significative large at different navigation speed. In order to get a slight im- provement on this topic, there are previous works showing that the kinematic problem (commonly associated with an outer loop) can be resynthesized by using other invariant subspaces, i.e., another representation of the configuration space. For this reason, the proposal reported here uses an oriented-manifold parametrized by a coordinate system on a curve viewpoint of the trajectory to describe the kinematic problem, however, the dynamic control law remains faithful to the singular perturbation approach with invariant subspaces mutually orthogonal, thus, it is possible to include the flexibility through a small factor in the dynamic model (well-known as ε), responsible to avoid the good-performance of the kinematic constraints. Only a common curvature-transformation between orthogonal and curve coordinates will be used to couple both approaches. Finally, it will be observed that when the controller is applied to the control scheme the behavior of the tracking is meaningfully improved.

Fixed-time sliding mode tracking control for autonomous underwater vehicles

In this paper, a fixed-time sliding mode controller (FSMC) with disturbance observer (DO) is proposed to solve the trajectory tracking control problem for autonomous underwater vehicles (AUVs) under time-varying external disturbances. Firstly, by using the sliding mode control method, an FSMC is proposed for reducing the tracking errors. Secondly, considering the estimation problem of time-varying disturbances, a fixed-time DO is designed to reconstruct the information of the AUV. Finally, The DO can compensate time-varying disturbances and the FSMC can reduce the tracking errors incorporating the information from the DO. Thus, the FSMC-DO for AUV is derived. Compared with the conventional FSMC and the finite time controller, the proposed FSMC-DO not only offers a faster convergence rate but also a higher tracking accuracy and robustness for the trajectory tracking control of AUVs. Simulation results are provided to show the superior performance of the proposed FSMC-DO.

Neural network‐based tracking control of autonomous marine vehicles with unknown actuator dead‐zone

AbstractThis article studies the neural‐network based backstepping control problem for autonomous marine vehicles perturbed by external disturbances. The actuator dead‐zone phenomenon, which is a common non‐smooth property caused by the complicated operating environment of autonomous marine vehicles, is also considered. To cope with the issue of “complexity explosion” and further decrease the tracking errors, a command filtering compensation strategy is also proposed, which guarantees satisfactory tracking performance and boundedness of the closed‐loop system signals. Finally, simulation studies are given to further demonstrate the effectiveness of the proposed control method.

Event-Triggered Formation Tracking Control for Unmanned Aerial Vehicles Subjected to Deception Attacks

This study investigates the time-varying formation tracking (TVFT) control problem for multiple unmanned aerial vehicle (multi-UAV) systems under deception attacks by utilizing an event-triggered mechanism (ETM). First, for the sake of alleviating the communication burden, an effective ETM is designed in this paper. Second, to deal with deception attacks in the communication network, a random deception attack model under the designed ETM is constructed. Finally, a novel formation tracking control scheme for multi-UAV systems under deception attack combining the ETM is proposed to achieve the expected TVFT. The stability analysis of the formation control system is given by using the Lyapunov stability theory and linear matrix inequality (LMI) technique. Simulations are conducted to verify the effectiveness of the proposed formation control scheme.