Trajectory Tracking Of Vehicle Research Articles

Double-Loop PID-Type Neural Network Sliding Mode Control of an Uncertain Autonomous Underwater Vehicle Model Based on a Nonlinear High-Order Observer with Unknown Disturbance

An unknown nonlinear disturbance seriously affects the trajectory tracking of autonomous underwater vehicles (AUVs). Thus, it is critical to eliminate the influence of such disturbances on AUVs. To address this problem, this paper proposes a double-loop proportional–integral–differential (PID) neural network sliding mode control (DLNNSMC). First, a double-loop PID sliding mode surface is proposed, which has a faster convergence speed than other PID sliding mode surfaces. Second, a nonlinear high-order observer and a neural network are combined to observe and compensate for the nonlinear disturbance of the AUV system. Then, the bounded stability of an AUV closed-loop system is analyzed and demonstrated using the Lyapunov method, and the time-domain method is used to verify that the velocity- and position-tracking errors of AUVs converge to zero exponentially. Finally, the radial basis function (RBF) neural network PID sliding mode control (RBFPIDSMC) and the RBF neural network PID sliding mode control (RBFPDSMC) are compared with this method in two trajectory tracking control simulation experiments. In the first experiment, the average Euclidean distance of the position-tracking error for this method was reduced by approximately 73.6% and 75.3%, respectively, compared to those for RBFPDSMC and RBFPIDSMC. In the second experiment, the average Euclidean distance of the position tracking error for this method was reduced by approximately 86.8% and 88.8%, respectively. The two experiments showed that the proposed control method has a strong anti-jamming ability and tracking effect. The simulation results obtained in the Gazebo environment validated the superiority of this method.

Deep Reinforcement Learning for Autonomous Dynamic Skid Steer Vehicle Trajectory Tracking

Designing controllers for skid-steered wheeled robots is complex due to the interaction of the tires with the ground and wheel slip due to the skid-steer driving mechanism, leading to nonlinear dynamics. Due to the recent success of reinforcement learning algorithms for mobile robot control, the Deep Deterministic Policy Gradients (DDPG) was successfully implemented and an algorithm was designed for continuous control problems. The complex dynamics of the vehicle model were dealt with and the advantages of deep neural networks were leveraged for their generalizability. Reinforcement learning was used to gather information and train the agent in an unsupervised manner. The performance of the trained policy on the six degrees of freedom dynamic model simulation was demonstrated with ground force interactions. The system met the requirement to stay within the distance of half the vehicle width from reference paths.

A Quasi-Velocity-Based Tracking Controller for a Class of Underactuated Marine Vehicles

This paper investigates the trajectory tracking control problem for underactuated underwater vehicles, for which a model is expressed in terms of quasi-velocities arising from the inertia matrix decomposition. The control approach takes into account non-modeled dynamics and external disturbances and is suitable for symmetric vehicles. It is shown that such systems can be diagonalized using inertial quasi-velocities (IQVs). The strategy consists of the velocity controller and two adaptive integral sliding mode control algorithms. The proposed approach, introducing velocity transformation and using backstepping methods and integral sliding mode control, allows trajectory tracking for vehicles in described models with symmetric inertia matrix. Proof of the stability of the closed system was carried out using IQV. The proposed scheme has been verified on two 3 DOF models of underwater vehicles with thruster limitations. A brief discussion of the results is also given.

Dynamic Damping-Based Terminal Sliding Mode Event-Triggered Fault-Tolerant Pre-Compensation Stochastic Control for Tracked ROV

Due to the unknown disturbance caused by the harsh environment in deep water, the stability of Underwater Tracked Remotely Opreated Vehicle (UTROV) trajectory tracking control is affected; especially the resistance forces of random vibrations caused by non-differentiable random disturbance resistance, which has become one of the main problems in controller design. Considering engineering practice, a stochastic model and new dynamic damping-based terminal sliding mode event-triggered fault-tolerant controller were designed in this paper. Firstly, based on the random resistance pre-compensation theory for the first time, a stochastic model was designed for differential drive UTROV. Meanwhile, a new nonsingular terminal sliding mode and dynamic damping reaching law were designed to achieve global finite-time convergence and reduce chattering with better robust response speed. Furthermore, to deal with the wear and tear caused by actuator failure and fixed sampling rate transmission, a new dynamic event trigger mechanism was designed and the faults analyzed. On this basis, combined with the finite-time adaptive on-line estimation technology, it can not only better reduce the transmission frequency, but also the finite-time dynamic active fault-tolerant compensation. The control scheme has semi-globally finite-time stability in probability and is proved by theory, which is compliant with engineering requirements. Then, according to characteristics of innovation, the three groups of simulation of control methods are designed to compare the methods in this paper. Finally the advantages of the method are verified by simulation to achieve the design expectations.

Four-wheel-drive vehicle trajectory tracking control at joint planning layer

Because the real road conditions are complex and changeable, the vehicle cannot drive normally according to the predetermined trajectory. Therefore, for the four-wheel drive electric vehicle, a trajectory tracking control method of joint planning layer is proposed. First, in the process of vehicle trajectory tracking, the obstacle avoidance trajectory is planned for the dynamic obstacle environment. Then, an MPC trajectory tracking controller with a velocity planning module is designed to reduce the lateral acceleration of the vehicle avoiding obstacles at high speed during trajectory tracking. Finally, CarSim/Simulink co-simulation verification and hardware-in-the-loop (HIL) simulation verification are carried out to verify the performance of the controller. The simulation results show that the trajectory planning module can successfully plan the trajectory of obstacle avoidance, and the improved MPC controller can effectively reduce the lateral acceleration of the target vehicle during trajectory tracking; HIL analysis shows that the MPC trajectory tracker with speed planning module can control the vehicle with good driving stability. Trajectory tracking control under urban road conditions will be considered in future research.

Performance-guaranteed fractional-order sliding mode control for underactuated autonomous underwater vehicle trajectory tracking with a disturbance observer

This paper proposes a fractional-order sliding mode control method for an underactuated autonomous underwater vehicle (AUV) with random disturbances. First, a new fractional-order sliding mode disturbance observer (FOSMDO) is designed to estimate the random disturbance and unknown AUV’s model based on adaptive and fixed time . A strategy based on adaptive fractional sliding mode control is designed for motion control, and a line-of-sight guidance law with time-varying look-ahead distance is applied to achieve path following. Then, a new fractional-order sliding model controller is designed by constructing a fractional-order mode surface and proposing a prescribed performance function to ensure the control performance. The closed-loop stability is proved and analyzed, and the effectiveness and robustness of the controller are demonstrated by numeric simulations.

A Consensus-Based Model Predictive Control with Optimized Line-of-Sight Guidance for Formation Trajectory Tracking of Autonomous Underwater Vehicles

A Consensus-Based Model Predictive Control with Optimized Line-of-Sight Guidance for Formation Trajectory Tracking of Autonomous Underwater Vehicles

Research on Lateral and Longitudinal Coordinated Control of Distributed Driven Driverless Formula Racing Car under High-Speed Tracking Conditions

Aiming at the problem that it is difficult to ensure the trajectory tracking accuracy and driving stability of the distributed driven driverless formula racing car under high-speed tracking conditions, a lateral and longitudinal coordinated control strategy is proposed. Based on the adaptive model predictive control theory, the lateral motion controller is designed, and the prediction time domain of the controller is changed in real time according to the change of vehicle speed. Based on the sliding mode variable structure control theory, a longitudinal motion controller is designed to accurately track the desired vehicle speed. Considering the coupling between the lateral and longitudinal controls, the lateral controller inputs the longitudinal speed and displacement of the vehicle, using the feedback mechanism to update the prediction model in real time, the longitudinal controller takes the front wheel angle as the input, the driving torque is redistributed through the differential drive control, and the lateral and longitudinal coordinated control is carried out to improve the trajectory tracking accuracy and driving stability. The typical working conditions are selected for co-simulation test verification. The results show that the lateral and longitudinal coordinated control strategy can effectively improve the vehicle trajectory tracking control accuracy and driving stability.

Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm

Abstract. This paper will study a trajectory tracking control algorithm for electric vehicles based on a terminal sliding mode controller. First, a 3 degrees of freedom nonlinear vehicle model and a controller-oriented 2 degrees of freedom vehicle model are established. The preview time is adaptively adjusted based on the preview model. Then, the vehicle trajectory tracking controller, which uses the terminal sliding mode algorithm, is designed. The radial basis function (RBF) neural network algorithm is used to approximate the system variable parameters in the control model online. At the same time, fuzzy logic is used to control the gain parameters of the controller to reduce the chattering of the control system. Finally, the designed controller is verified by simulation. The maximum deviation of path tracking under different speeds is 0.6 m, and the target path can also be well followed under different road friction coefficients. The simulation results show that the controller designed in this paper can effectively carry out the vehicle trajectory tracking and lateral control and reduce the chattering to a certain extent.

Improving trajectory tracking performance of autonomous farming vehicles using adaptive extended state observer and finite-time technique

The ability of the autonomous farming vehicle to accurately and quickly track complex reference trajectories in the field is critical to crop yields and farmer incomes. The novelty of this paper is to design a new adaptive finite-time trajectory tracking control strategy with an adaptive extended state observer to improve the autonomous farming vehicle’s trajectory tracking accuracy and convergence speed in the complex farm operating environment. The adaptive extended state observer is constructed to deal with the slip disturbance and parameter uncertainty to improve the tracking accuracy. The finite-time control strategy is adopted to improve the convergence speed of the trajectory tracking of the autonomous farming vehicle. The effectiveness and advantages of the proposed control strategy are verified by comparing it with the active disturbance rejection control and traditional PID control strategies under MATLAB/Simulink environment.

NMPC-based trajectory tracking control strategy for unmanned surface vehicle

To solve the unmanned surface vehicle trajectory tracking control problem under model uncertainty and external environmental disturbance, a trajectory tracking control strategy based on nonlinear model predictive control (NMPC) has been proposed. Firstly, a hydrodynamic model considering the uncertainty of model parameters and the influence of external environmental disturbance has been established; then, the fourth-order Lundgren-Kutta method has been used to discretize the nonlinear state space model, and the control quantity, control increment and output quantity of the system have been used as constraints to solve the optimization problem by numerical computation; finally, the reference circle has been used as the preset trajectory, and the trajectory tracking experiment has been carried out under the uncertainty of the model and external environmental disturbance. The experimental results show that the trajectory tracking strategy based on NMPC can better track the preset trajectory and has better anti-interference ability and control effect.

A State-Compensated Deep Deterministic Policy Gradient Algorithm for UAV Trajectory Tracking

The unmanned aerial vehicle (UAV) trajectory tracking control algorithm based on deep reinforcement learning is generally inefficient for training in an unknown environment, and the convergence is unstable. Aiming at this situation, a Markov decision process (MDP) model for UAV trajectory tracking is established, and a state-compensated deep deterministic policy gradient (CDDPG) algorithm is proposed. An additional neural network (C-Net) whose input is compensation state and output is compensation action is added to the network model of a deep deterministic policy gradient (DDPG) algorithm to assist in network exploration training. It combined the action output of the DDPG network with compensated output of the C-Net as the output action to interact with the environment, enabling the UAV to rapidly track dynamic targets in the most accurate continuous and smooth way possible. In addition, random noise is added on the basis of the generated behavior to realize a certain range of exploration and make the action value estimation more accurate. The OpenAI Gym tool is used to verify the proposed method, and the simulation results show that: (1) The proposed method can significantly improve the training efficiency by adding a compensation network and effectively improve the accuracy and convergence stability; (2) Under the same computer configuration, the computational cost of the proposed algorithm is basically the same as that of the QAC algorithm (Actor-critic algorithm based on behavioral value Q) and the DDPG algorithm; (3) During the training process, with the same tracking accuracy, the learning efficiency is about 70% higher than that of QAC and DDPG; (4) During the simulation tracking experiment, under the same training time, the tracking error of the proposed method after stabilization is about 50% lower than that of QAC and DDPG.

Control Algorithm for Trajectory Tracking of an Underactuated USV under Multiple Constraints

A trajectory tracking control method based on the cascade of optimal guidance and adaptive control laws was proposed for trajectory tracking of an unmanned surface vehicle under multiple motion constraints including an ocean current disturbance environment. Considering the velocity and acceleration constraints, angular velocity, and angular acceleration constraints in guidance law design, motion planning was conducted with three degrees of freedom using a motion model and predictive control, while optimal guidance law was designed through a rolling optimization strategy to stabilize the position error under multiple constraints. Moreover, the stability of the guidance law was proven. In control law design, an adaptive observer was introduced to estimate and compensate for ocean current disturbance, which affected the precision of control. The course and velocity control laws were designed with the adaptive sliding mode control technology to stabilize the course angle and longitudinal velocity errors; the stability of these control laws was further proven. Based on the system design, the stability of the entire closed-loop control system was proven with the cascade system and Lyapunov theory. Theoretical analysis and simulation experiments demonstrated the effectiveness and advancement of the proposed algorithm.

Trajectory tracking of in-wheel motor electric vehicles based on preview time adaptive and torque difference control

In order to improve the accuracy of trajectory tracking of in-wheel motor electric vehicles, a preview time adaptive trajectory tracking method based on iterative algorithm and fuzzy control is proposed. Firstly, based on the vehicle’s three-degree-of-freedom model, the vehicle is controlled to track trajectory based on model predictive control (MPC). The preview step size and sampling period of MPC are adjusted by iterative function and fuzzy controller, respectively. Then, In order to optimize MPC active steering control, a differential torque controller is established to realize the trajectory tracking control of differential torque steering. Finally, Carsim/Simulink co-simulation analysis and real vehicle verification are done. The simulation results show that the controller can complete the trajectory tracking control of the in-wheel motor intelligent vehicle, and the stability and steering performance are good. The controller has good robustness and adaptability according to road adhesion conditions and vehicle speed changes. At the same time, the trajectory tracking accuracy of the MPC controller is better than sliding mode variable structure control (SMC). The real vehicle verification results show that when the real vehicle tracking under different speeds, the adaptive preview time controller designed in this paper has good trajectory tracking performance and stability.

Safety-Critical Control With Nonaffine Control Inputs Via a Relaxed Control Barrier Function for an Autonomous Vehicle

When designing a controller for the autonomous vehicle system, safety and trajectory tracking performance are two major concerns. This letter proposes a novel control design for an autonomous vehicle system with nonaffine control inputs that can track the desired trajectories while considering the safety constraint. First, the vehicle dynamics is modeled using the differential flatness approach. The dynamic inversion method is then employed for the trajectory tracking of a nonaffine-in control system, and a control barrier function (CBF) approach is utilized to enforce the safety constraint. The trajectory tracking control is handled as a least-squares optimization problem, while the CBF is considered as a constraint. By relaxing the CBF constraint to the cost function, the novel control design is derived via a dynamic inversion. The safety and the stability of the closed-loop system are analyzed using a singular perturbation method. The proposed method is validated using numerical simulation and the high-fidelity car simulator under realistic driving scenarios.

Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot’s energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. We formulate a linear equivalent representation suitable for DNN-MRFT using feedback linearization. This representation enables the analytical investigation of different controller structures and tuning settings, and captures the non-linearity trends of the system. With this approach, the trade-off between performance and robustness in design was made possible which is convenient for the design of controllers of UAVs operating in uncertain environments. We demonstrate that our approach is adaptive and robust through a set of experiments, where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of wind disturbance. Due to the model-based system design, it was possible to obtain low discrepancy between simulation and experimental results which is beneficial for potential use of the proposed approach for real-time model-based planning and fault detection tasks. We obtained RMSE of 3.59 cm when tracking aggressive trajectories in the presence of strong wind, which is on par with state-of-the-art.

Safe trajectory tracking for underactuated vehicles with partially unknown dynamics

<p style='text-indent:20px;'>Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. The safe tracking control of these vehicles is a substantial aspect for an increasing range of application domains. However, external disturbances and parts of the internal dynamics are often unknown or very time-consuming to model. To overcome this issue, we present a safe tracking control law for underactuated rigid-body dynamics using a learning-based oracle for the prediction of the unknown dynamics. The presented approach guarantees a bounded tracking error with high probability where the bound is explicitly given. With additional assumptions, asymptotic stability of the tracking error is achieved. A numerical example highlights the effectiveness of the proposed learning-based control law.</p>

Nonlinear Tire Model Approximation Using Machine Learning for Efficient Model Predictive Control

Model Predictive Controller (MPC) is widely used as a technique for path tracking control since it allows for dealing with system constraints and future forecasts. However, the performance of MPC is directly affected by the adopted model. A complex dynamic model can guarantee accuracy in path tracking but may not be suitable in computational terms. On the other hand, a simplified model may not capture essential nonlinear aspects. Thus, to cope with these problems, this paper deals with data-driven tire modeling to improve autonomous ground vehicle path tracking control. The main contribution of the present work is to show that neural tires can capture the nonlinearities present in the interaction between lateral and longitudinal vehicle dynamics, with a reduced computational cost for predictive controllers. Simulated and experimental tire data are approximate to design data-driven tire models using radial basis function and multilayer perceptron neural networks. Then, based on ground vehicles with neural tires, model predictive controllers are designed to regulate wheel torque and steering angle inputs. Comparative tests were conducted to compare the proposed data-driven MPC approach with the classical nonlinear MPC controller. The results show that the neural tires approximate nonlinear tire models and experimental data with arbitrary precision in terms of accuracy and error-based metrics. The proposed methodology was successfully applied to perform trajectory and velocity tracking of ground vehicles. In addition, MPC with a neural tire model as prediction inference reduces the computational effort compared to traditional approaches.

Trajectory Tracking of Autonomous Vehicle Based on Model Predictive Control With PID Feedback

The simplified vehicle model often results in inaccuracy with respect to the conventional model predictive control (MPC) as it causes steady error in tracking control, which has negative implications for vehicle cornering. This study presents a trajectory planning and tracking framework, which applies artificial potential to obtain target trajectory and MPC with PID feedback to effectively track planned trajectory. The experimental and simulation results are then presented to demonstrate the improved performance in tracking accuracy and steering smoothness compared to that of the conventional MPC control. Especially during negotiating a curve, its steady state error is close to 0.