Adaptive Trajectory Tracking Control Research Articles

Fuzzy adaptive trajectory tracking control of work-class ROVs considering thruster dynamics

Work-class remotely operated vehicles (ROVs) are widely used in oceanic observation and underwater operation, which are driven by hydraulic power. A fuzzy adaptive controller considering thruster dynamics is proposed to improve the trajectory tracking performance of work-class ROVs in this paper. The dynamics of the hydraulic motor driven thruster is much higher than the position and orientation tracking ability of work-class ROVs. Thus, the system is divided into two separate subsystems: out-loop position and orientation control system, and inner-loop hydraulic control system. A fuzzy adaptive algorithm is employed in the out-loop position and orientation controller, which estimates the system parameters and disturbances respectively. A disturbance observer is used in the inner-loop hydraulic controller to attenuate the uncertainties and disturbances. A second order filter is adopted to synthesize these two controllers, which generates a smooth hydraulic motor speed command with appropriate bandwidth and range. The stability of the whole control system is proved through Lyapunov theory. Simulation results demonstrate the effectiveness of the proposed trajectory tracking control strategy under parameter uncertainties and disturbances.

UAV based adaptive trajectory tracking control with input saturation and unknown time‐varying disturbances

AbstractThis paper proposes an adaptive control method for an underactuated complex nonlinear system, that is, unmanned aerial vehicle (UAV), to realize high‐precision trajectory tracking under input saturation constraint and other disturbances via artificial neural network and anti‐saturation auxiliary system. First, the rigid body motion theory is applied to establish a simplified nonlinear dynamic model of the UAV. Then a Lyapunov function is designed with the error surface to form the preliminary control law based on dynamic surface control. Afterwards, a radical basis function neural network is developed and used to estimate and compensate the disturbance, while the input saturation is also addressed via the designed anti‐saturation auxiliary system. Thus the stability and signal consistency of the closed‐loop UAV control system can be proved via the Lyapunov stability theory with appropriately tuned parameters. Simulation experiments are performed to verify that the proposed control algorithm can be used for UAV high‐precision trajectory tracking under time‐varying disturbances.

Adaptive Variable Threshold Event-Triggered Control for Trajectory Tracking of Autonomous Underwater Vehicles with Actuator Saturation

Abstract This article develops a novel event-triggered sliding mode control (ETSMC) approach with variable threshold to deal with trajectory tracking matters of autonomous underwater vehicles (AUVs) accompanied by actuator saturation and external disturbances, which can effectively reduce the communication burden between the controller and actuator. The proposed scheme will be very practical when some extreme situations occur. First, the closed-loop system is split into two parts: fixed terms determined by the system itself and nonlinear terms caused by uncertain factors. The nonlinear terms are estimated through adaptive technique. Then the apposite event-triggered mechanism, adaptive laws, and modeled actuator saturation characteristics are designed. The correctness of the presented scheme is illustrated via the stability analysis in the sequel, and the Zeno phenomenon is certificated to be excluded simultaneously. Finally, two different reference motion trajectories are adopted in the simulation experiments, which can indicate that the proposed ETSMC possesses performance superiority and only requires to consume a small amount of communication resources in trajectory tracking control of AUVs. Significance Statement Through the research of this article, we propose a novel event-triggered sliding mode control method with variable threshold applied to autonomous underwater vehicles (AUVs). When conducting ocean exploration work, we usually need the AUVs to follow particular trajectories. By using the proposed method, it can greatly reduce the loss of communication resources inside the system.

Guest editorial: Decision making and control for connected and automated vehicles

Guest editorial: Decision making and control for connected and automated vehicles

Tracking control for small autonomous underwater vehicles in the Trans-Atlantic Geotraverse hydrothermal field based on the modeling trajectory

• A multi-parameter high-order extended state observer is designed to estimate lumped uncertainties. • A fixed-time fault-tolerant adaptive trajectory tracking controller is presented. • The desired trajectory is constructed by interpolating the path. • The simulation topography is built according to the TAG active mound. This paper investigates the fixed-time trajectory tracking problem of small autonomous underwater vehicles (S-AUVs) in the Trans-Atlantic Geotraverse (TAG) active mound with ocean current, unknown disturbances, model uncertainties, actuator faults, and input saturations. First, the effect of the near-bottom environment of the mound on S-AUVs is analyzed in detail from three issues. Without the upper bound and gradient of lumped uncertainties, a high-order adaptive extended state observer (ESO) is designed. Then, a continuous fixed-time sliding mode manifold is applied to obtain fast convergence performance. In order to realize the fixed-time convergence of tracking errors, an adaptive fault-tolerant trajectory tracking control law with an auxiliary dynamic system (ADS) is proposed. In addition, the simulated topography of the TAG mound is constructed and the trajectory based on path points is modeled by cubic spline interpolation. The effectiveness and superiority of the proposed algorithm are validated by comprehensive simulations.

Event-triggered finite-time adaptive trajectory tracking control for a class of nonlinear non-strict-feedback systems with input saturation and application to a single-link robot

Event-triggered finite-time adaptive trajectory tracking control for a class of nonlinear non-strict-feedback systems with input saturation and application to a single-link robot

Adaptive neural networks trajectory tracking control for autonomous underwater helicopters with prescribed performance

An autonomous underwater helicopter (AUH) is a type of autonomous underwater vehicle (AUV) that is capable of fixed-point hovering, precise and free take-off, and landing. It is effective for underwater ultra-mobile tasks, including mobile observation networks, resource exploration, and data connection. This paper aims to investigate an AUH trajectory tracking controller based on the prescribed performance method, considering the influence factors such as current disturbance, modeling uncertainty and thruster faults. A novel preset time performance function is designed in which the stable terminal time of the system can be specified explicitly, and the convergence rate of the system's dynamic process may be altered by adjusting the parameters, making it more intuitive for the designer. A speed observer was introduced to address this problem of measuring the required state information from the AUH's sensors in practice, which was enhanced using a radial basis function neural network (RBFNN) to approximate external perturbations and uncertainties. The stability of the closed-loop AUH system is proved by using Lyapunov theory. The feasibility and effectiveness of the proposed algorithm proposed approach were eventually verified by two sets of simulations for different thruster fault forms.

Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems

This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

Adaptive neural network-based trajectory tracking outer loop control for a quadrotor

This manuscript introduces a novel adaptive neural network-based controller for trajectory tracking of quadrotors. This controller is conceived as an outer loop controller that interacts with an inner loop controller in a two-loop configuration. The inner loop in this two-loop configuration is assumed to be inaccessible and unmodifiable, which is a realistic hypothesis in the operation of commercial quadrotors. Under this situation, the proposed controller computes appropriate kinematic input commands for the inner loop to achieve trajectory tracking. One remarkable feature of the proposed algorithm is its robustness against parametric uncertainties from the inner loop. An exhaustive error convergence analysis is provided, thus guaranteeing the convergence of the trajectory tracking error. Experimental results and a comparison using other control schemes demonstrate the competitiveness of the proposed scheme, being the latter the best among the tested adaptive neural network-based schemes.

Formation Control and Tracking of Mobile Robots using Distributed Estimators and A Biologically Inspired Approach.

This paper investigates the formation control problem for multiple nonholonomic wheeled mobile robots using distributed estimators and a biologically inspired approach. The formation pattern of the system adopts leader–follower structure and the communication topology among the multi-robot system is modelled by an undirected graph. In our proposed methodology, first, we develop an adaptive trajectory tracking control for the leader robot to follow the desired trajectory. Second, a distributed estimator is designed for each follower mobile robot, which uses its own information to estimate the leader's states, such as position, orientation, and linear velocity. Then, distributed formation tracking control laws are designed based on the distributed estimator. Furthermore, a bioinspired controller is developed to address the impractical velocity jump problem. The closed-loop system stability is analysed with the Lyapunov stability theory showing that tracking errors are asymptotically converge to zero. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods.

Adaptive faster fixed-time trajectory tracking control for manipulator

Adaptive faster fixed-time trajectory tracking control for manipulator

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.

Constrained Spatial Adaptive Iterative Learning Control for Trajectory Tracking of High Speed Train

This paper proposes a constrain spatial adaptive iterative learning controller (CSAILC) for the displacement-speed trajectory tracking of automatic train control system with unknown parametric/nonparametric uncertainties and speed constraints. First, the nonlinear dynamic model of train operation is transformed from temporal domain into spatial domain utilizing a spatial state differentiator. Besides, the displacement-related parametric/nonparametric uncertainties are updated in the iteration axis. Furthermore, a barrier function is involved to satisfy the speed constraint, and the corresponding convergence analysis of the proposed CSAILC for automatic train control (ATC) is derived based on the spatial composite energy function. In addition, numerical simulations of train tracking control are carried out, and simulation results indicate that the proposed CSAILC achieves good effectiveness in a high-speed train (HST) control system.

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

Event-based adaptive horizon nonlinear model predictive control for trajectory tracking of marine surface vessel

This study investigates the trajectory tracking problem of fully actuated marine surface vessels (MSVs). First, an optimal problem for trajectory tracking of MSV is established. Model uncertainty, time-varying environmental disturbances, and actuator saturation are considered in this problem. For the control algorithm, considering that the traditional nonlinear model predictive control (NMPC) algorithm requires large amounts of computational resources, it is not easy to transmit the control signal to the system in real-time in practice. Meanwhile, due to the limited bandwidth of the system, the large amount of data transmission may lead to congestion and loss of signal transmission. Therefore, a novel event-based adaptive horizon nonlinear model predictive control (EAHNMPC) scheme is proposed to relieve the computation burden and reduce the signal transmission frequency for the MSV's trajectory tracking. Moreover, stability proof of adaptive predictive horizon control is given, and simulation experiments under different cases are performed. The results show that the proposed scheme has better control performance and computational performance than other approaches.

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.

Deep Reinforcement Learning-Based Adaptive Controller for Trajectory Tracking and Altitude Control of an Aerial Robot

This research study presents a new adaptive attitude and altitude controller for an aerial robot. The proposed controlling approach employs a reinforcement learning-based algorithm to actively estimate the controller parameters of the aerial robot. In dealing with highly nonlinear systems and parameter uncertainty, the proposed RL-based adaptive control algorithm has advantages over some types of standard control approaches. When compared to the conventional proportional integral derivative (PID) controllers, the results of the numerical simulation demonstrate the effectiveness of this intelligent control strategy, which can improve the control performance of the whole system, resulting in accurate trajectory tracking and altitude control of the vehicle.

Adaptive state-constrained trajectory tracking control of unmanned surface vessel with actuator saturation based on RBFNN and tan-type barrier Lyapunov function

To promote the vigorous development of the marine technology, more and more scientific research institutes have begun to develop unmanned surface vehicles (USVs) with advanced control performance. This paper addresses trajectory tracking problem for state-constrained USV under the influence of actuator saturation and general disturbance uncertainties. An anti-windup system with auxiliary variables is employed to cope with actuator saturation. Besides, full-state constrains are not violated by combining the tan-type barrier Lyapunov function and backstepping method. Moreover, RBFNN is adopted to approximate some unknown uncertain function terms. By utilizing adaptive estimation technique, the unknown upper boundaries of disturbance uncertainties, input saturation difference, as well as RBFNN approximation error are compensated. The semi-global asymptotic convergence is guaranteed by rigorous theoretical analysis and Lyapunov proof. Finally, simulations results are given to verify the feasibility of the proposed algorithm.

Robust adaptive trajectory tracking for wheeled mobile robots based on Gaussian process regression

In this paper, we propose a novel learning-based robust adaptive trajectory tracking controller for wheeled mobile robots (WMR) subject to velocity input uncertainties. Gaussian process regression (GPR) is employed in view of its powerful estimation ability and wide scope of applications as a nonparametric regression method. The velocity uncertainties are estimated online using the real-time measured data. The prediction mean and variance of the GPR are used to counteract the effect of the uncertainties and to design the robust control term, respectively. A continuous robust controller is proposed which has the advantage of achieving asymptotic convergence of the trajectory tracking error instead of uniformly ultimately boundedness. Comparisons with the neural network based adaptive controller and a state of the art GPR-based design demonstrate the effectiveness of the proposed control strategy.

Backstepping Adaptive Trajectory Tracking Control of Manipulator with Uncertainties of Model and State

In this paper, a backstepping adaptive control method based on Kalman data fusion (KDF-BAC) is proposed to solve parametric and non-parametric as well as state variables in manipulator dynamics model. Firstly, the non-parametric uncertainties are regarded as internal disturbances to simplify the dynamics model of the manipulator. Secondly the backstepping control law is derived using the idea of recursive design, and the adaptive method is used to identify the uncertain parameters to provide the estimated value of unknown parameters for the backstepping control law. Finally, the Kalman data fusion method is used to reduce the uncertainty of the velocity information, and the most reliable estimate value is obtained, which is fed back to the controller. The simulation results show that the proposed KDF-BAC can reduce the influence of manipulator uncertainties on trajectory tracking performance, and has better control effect than adaptive control and backstepping control.