Tracking Control Of Vehicles Research Articles

Simulation and verification of an adaptive S-plane three-dimensional trajectory tracking control for autonomous underwater vehicles

Simulation and verification of an adaptive S-plane three-dimensional trajectory tracking control for autonomous underwater vehicles

Time-Varying Formation Tracking Control for Unmanned Aerial Vehicles with the Leader’s Unknown Input and Obstacle Avoidance: Theories and Applications

This paper investigates the time-varying formation tracking (TVFT) problems for unmanned aerial vehicle (UAV) swarm systems with the leader’s unknown input and collision and obstacle avoidance mechanism. To solve the TVFT control problem which has not been studied extensively, distributed observers are proposed to estimate the leader’s position and a TVFT control protocol is constructed. Stability of control protocol is proved by Lyapunov stability theory. Furthermore, the effectiveness of the TVFT control protocol is verified by numerical simulations and flying experiments, which is more convincing than the theoretical results only.

Research on Automatic Driving Path Tracking Control of Open-Pit Mine Transportation Vehicles with Delay Compensation

The transportation environment of the open-pit mine is complex, the steering actuator of the mine vehicle has a large delay and poor response accuracy, and there are a lot of bumpy roads, large undulating ramps, and narrow-area curves in the mining area. These road sections seriously reduce the tracking accuracy of the mine vehicle path. Tracking control presents great challenges. Therefore, this study first conducts a simulation comparison study on commonly used path tracking methods such as pure pursuit control, Stanley control, and model predictive control (MPC), and then designs a path tracking control strategy for automatic driving of open-pit mine transportation vehicles based on the MPC algorithm. Finally, the proposed control strategy was verified through actual mining vehicle tests. The results showed that the maximum lateral deviation obtained by the MPC-based path tracking control strategy was reduced from 0.55 m to 0.08 m under the C-shaped reference path compared with the traditional method. Under the S-shaped reference path, the lateral deviation is reduced from 0.4 m to 0.16 m.

Encapsulated trajectory tracking control for autonomous vehicles

The motion control of autonomous vehicles with a modular, service-oriented system architecture poses new challenges, as trajectory-planning and -execution are independent software functions. In this paper, requirements for an encapsulated trajectory tracking control are derived and it’s shown that key differences to conventional vehicles with an integrated system architecture exist, requiring additional attention during controller design. A novel, encapsulated control architecture is presented that incorporates multiple extensions and support functions, fulfilling the derived requirements. It allows the application within the modular architecture without loss of functionality or performance. The controller considers vehicle stability and enables the yaw motion as an independent degree of freedom. The concept is applied and validated within the vehicles of the UNICARagil research project, that feature the previously described system architecture to increase flexibility of application by dynamically interconnecting services based on the current use-case.

Formation tracking control for underactuated surface vehicles with actuator magnitude and rate saturations

In this paper, a formation tracking problem with collision avoidance is investigated for underactuated surface vehicles (USVs) with actuator magnitude and rate saturations. The USVs are subject to model uncertainties and external disturbances. A nonlinear extended state observer (NESO) is used to recover the linear velocity and yaw rate as well as unknown uncertainties and disturbances. A distributed control law is designed by artificial potential functions (APFs), the NESO and a backstepping technique. The APFs combined with a strategy are employed to achieve collision avoidance. Additional controllers are employed to deal with the underactuated problem, actuator magnitude and rate saturations. The stability of formation control system and simulation results are performed to illustrate the effectiveness of the proposed strategy.

Adaptive saturated tracking control for solid launch vehicles in ascending based on differential inclusion stabilization

The large-range uncertainties of specific impulse, mass flow per second, aerodynamic coefficients and atmospheric density during rapid turning in solid launch vehicles (SLVs) ascending leads to the deviation of the actual trajectory from the reference one. One of the traditional trajectory tracking methods is to observe the uncertainties by Extended State Observer (ESO) and then modify the control commands. However, ESO cannot accurately estimate the uncertainties when the uncertainty ranges are large, which reduces the guidance accuracy. This paper introduces differential inclusion (DI) and designs a controller to solve the large-range parameter uncertainties problem. When above uncertainties have large ranges, it can be combined with the ascent dynamic equation and described as a DI system in the mathematical form of a set. If the DI system is stabilized, all the subsets are stabilized. Different from the traditional controllers, the parameters of the designed controller are calculated by the uncertain boundaries. Therefore, the controller can solve the problem of large-range parameter uncertainties of in ascending. Firstly, the ascent deviation system is obtained by linearization along the reference trajectory. The trajectory tracking system with engine parameters and aerodynamic uncertainties is described as an ascent DI system with respect to state deviation, which is called DI system. A DI adaptive saturation tracking controller (DIAST) is proposed to stabilize the DI system. Secondly, an improved barrier Lyapunov function (named time-varying tangent-log barrier Lyapunov function) is proposed to constrain the state deviations. Compared with traditional barrier Lyapunov function, it can dynamically adjust the boundary of deviation convergence, which improve the convergence rate and accuracy of altitude, velocity and LTIA deviation. In addition, the correction amplitudes of angle of attack (AOA) and angle of sideslip (AOS) need to be limited in order to guarantee that the overload constraint is not violated during actual flight. In this paper, a fixed time adaptive saturation compensation auxiliary system is designed to shorten the saturation time and accelerate the convergence rate, which eliminates the adverse effects caused by the saturation. Finally, it is proved that the state deviations are ultimately uniformly bounded under the action of DIAST controller. Simulation results show that the DI ascent tracking system is stabilized within the given uncertainty boundary values. The feasible bounds of uncertainty is broadened compared with Integrated Guidance and Control algorithm. Compared with Robust Gain-Scheduling Control method, the robustness to the engine parameters are greatly improved and the control variable is smoother.

Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles

This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed control design, a nominal system is considered for the design of a baseline tracking controller using a conventional control approach. The nominal system also defines the desired behaviour of uncertain autonomous surface vehicles in an obstacle-free environment. Thanks to reinforcement learning, the overall tracking controller is capable of compensating for model uncertainties and achieving collision avoidance at the same time in environments with obstacles. In comparison to traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm using an example of autonomous surface vehicles.

Research on long-wheelbase vehicle path tracking control based on optimal heading angle

Path tracking control technology is a hot topic in the research of intelligent vehicles. It is an important link to realize intelligent transportation to solve the problem of path tracking control for long-wheelbase vehicles. Aiming at the problem that the vehicle body of long wheelbase is prone to collision with the surrounding environment due to the influence of the vehicle axle length in the process of turning, the overall deviation model of vehicle wheelbase and reference path is creatively proposed, and the optimal heading angle of long-wheelbase vehicle in the process of turning is determined by solving the maximum value of the overall deviation model. The MPC path tracking controller is established based on the deviation between the heading angle and the actual vehicle. Eventually established more than a typical working condition in MATLAB and TRUCKSIM joint simulation, to verify the proposed algorithm not only has realized the long-wheelbase vehicle path tracking control and front wheel position closer to the reference curve, effective in reducing long-wheelbase vehicle steering wheel swept area, in the process of reducing long-wheelbase vehicle steering and road environment in the process of collision probability. It improves the trafficability of long-wheelbase vehicles in narrow areas.

Adaptive robust path tracking control for autonomous vehicles with measurement noise

AbstractIn practical autonomous vehicle systems, model uncertainty and measurement noise are two challenging factors that deteriorate path tracking accuracy and system stability. This article proposes an adaptive robust controller to deal with these two factors and enhance path tracking accuracy. First, the path tracking model considering time‐varying uncertainty is built and represented as nominal and uncertain portions. Second, the control law is designed separately for both portions. A linear quadratic regulator controller is utilized to stabilize the nominal system. An additional robust control law is proposed to suppress the matched uncertainty and measurement noise, with an adaptive scheme aimed at estimating the bound outside uncertainty. The path tracking system with the developed control law is proved to possess uniform boundedness, uniform ultimate boundedness properties, and robustness against mismatched uncertainty. Eventually, the effectiveness of the developed controller is validated using both MATLAB/Simulink‐TruckSim co‐simulation and an autonomous vehicle platform. The results demonstrate that the designed adaptive robust control can achieve accurate path tracking in the presence of time‐varying uncertainty and measurement noise.

Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data.

In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal solutions when using quadratic programming (QP). Combined with dynamic sliding mode control (SMC), this model is applied to the dynamic trajectory tracking control of autonomous underwater vehicles (AUVs). First, the computational fluid dynamics (CFD) simulation platform ANSYS Fluent is used to solve for the main hydrodynamic coefficients required to establish the AUV dynamic model. Then, the novel model predictive controller is used to obtain the desired velocity command of the AUV. To reduce the influence of external interference and realize accurate velocity tracking, dynamic SMC is used to obtain the control input command. In addition, stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the trajectory tracking performance of the AUV in an underwater, three-dimensional environment is verified by using the MATLAB/Simulink simulation platform. The results verify the effectiveness and robustness of the proposed control method.

Nonlinearity compensation based robust tracking control of nonlinear nonminimum phase hypersonic flight vehicles

This paper introduces a nonlinearity compensation based robust tracking controller for hypersonic flight vehicles. The controller focuses on robust control design with respect to the nonminimum phase feature and system uncertainties for the control-oriented model. Firstly, following the principle of decomposing the complex control problem into two simpler problems, the original robust tracking problem for nonlinear nonminimum phase hypersonic flight vehicles is decomposed into a simpler robust tracking problem for a linear nonminimum phase system with disturbances and a stabilization problem for a nonlinear system without disturbances. After the problem decomposition, a proportional–integral tracking controller and a feedback linearization controller are designed for the linear system and the nonlinear system, respectively. Then, the addition of the two designed controllers gives the final controller. By compensating for the system nonlinearity using the secondary system and its controller, the proposed control method can satisfy the robust tracking control requirements for nonlinear nonminimum phase hypersonic flight vehicles. The simulation results show, compared with a feedback linearization control method and a composite neural learning control method, the proposed method has superior tracking accuracy and robustness against system uncertainties and external disturbances.

Trajectory tracking control for autonomous underwater vehicles under quantized state feedback and ocean disturbances

For the autonomous underwater vehicle (AUV) platform, state quantization is a widely existing phenomenon as the result of I/O signal conversion between hardware module and control module. This article considers such an adaptive trajectory tracking control problem for AUVs with the effects of quantized state feedback and ocean disturbances. To ensure the stability of the closed-looped system under quantized states and unmeasurable ocean disturbances, a novel quantized extended state observer (QESO) is firstly proposed. Then, an adaptive anti-disturbance control scheme is developed to achieve the trajectory tracking control goal. The main advantage of the proposed algorithm paper lies in that the resulted controller is established without any usage of continuous states. Besides, rigorous stability analysis validates the uniformly ultimately boundedness of the resulting closed-loop system under quantized states. Simulation experiments demonstrate the effectiveness and robustness of the proposed algorithms.

Post-Impact Motion Planning and Tracking Control for Autonomous Vehicles

There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions. To avoid subsequent crash events and to stabilize the vehicle, this paper proposes a post-impact motion planning and stability control method for autonomous vehicles. An enabling motion planning method is proposed for post-impact situations by combining the polynomial curve and artificial potential field while considering obstacle avoidance. A hierarchical controller that consists of an upper and a lower controller is then developed to track the planned motion. In the upper controller, a time-varying linear quadratic regulator is presented to calculate the desired generalized forces. In the lower controller, a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized forces. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.

Rendering bounded error in adaptive robust path tracking control for autonomous vehicles

For the sake of safety, the vehicle path tracking control should not only ensure the stability of the path tracking error containing the lateral offset and the orientation error but also guarantee that both the transient and steady states of the lateral offset are within a specified safe boundary. However, the time-varying uncertainties of a vehicle system make the control design a tough task. This paper develops an adaptive robust control (ARC) which guarantees both the tracking stability and the bounded error property for autonomous vehicles. First, to handle the bounded error requirement, a barrier function based state transformation which converts the constrained lateral offset into an unconstrained state is proposed. Then, the path tracking control task is cast into an equality constraint of the system state. On this basis, a novel adaptive robust constraint-following controller is developed to make the transformed system follow the proposed equality constraint. Through Lyapunov minimax analysis, it is proved that the resulting control guarantees the approximate constraint-following performance and the bounded error property despite the presence of system uncertainties. Finally, the main theoretical results are verified through CarSim-Simulink co-simulation results.

Neural Sliding Mode Tracking Control of Air-breathing Hypersonic Vehicles

This paper proposes a neural sliding mode control method for the tracking problem of the longitudinal dynamics of air-breathing hypersonic vehicles (ABHV). Considering the input/output feedback linearization, a high-order sliding mode law of the elevator deflection and the fuel equivalence ratio is designed. Moreover, the effect of uncertain model and control input disturbances is approximated with a Radial Basis Function Neural Network (RBFNN). The stability of the closed-loop system is analysed based on Lyapunov theorem. Simulation results shows the good tracking performance of the proposed controller and robustness with parameter uncertainties. All the signals are globally bounded and converged in short time.

Finite-Time Adaptive Heading Tracking Control for Surface Vehicles With Full State Constraints

In this brief, the problem of finite-time heading tracking control for surface vehicles with controller faults and full state constraints is studied. The fuzzy logical systems and command filtered backstepping method are utilized to approximate the unknown nonlinear dynamics and construct an adaptive controller, respectively. The Nussbaum function is employed to solve the unknown control direction arising in the system. The new control scheme is proposed based on barrier Lyapunov functions and finite-time control strategy. It is proved that the error between the actual heading angle and the expected one can converge to the region near the origin in a finite time without violating the full state constraints. Finally, the simulation results are presented to show the effectiveness of the proposed finite-time control scheme.

Adaptive Finite-Time Trajectory Tracking Control of Autonomous Vehicles That Experience Disturbances and Actuator Saturation

Adaptive Finite-Time Trajectory Tracking Control of Autonomous Vehicles That Experience Disturbances and Actuator Saturation

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.