Adaptive Trajectory Tracking 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

Neural pinning control for adaptive trajectory tracking of complex dynamical networks

In this paper, a pinning control scheme for adaptive trajectory tracking of networked nonlinear systems is proposed. The desired trajectory tracking objective is achieved via a novel adaptive control strategy, in which the existing concept of pinning control is extended to an adaptive pinning control. The adaptive control is based on a recurrent high‐order neural network identification strategy for unknown pinned nodes dynamics. The combined pinning‐neural features in tracking control constitute the novelty of this approach. The proposed control law operates properly with a different reference along with unknown dynamics and coupling variations. In contrast to the linearization approach, less restrictive assumptions for node dynamics are imposed. Moreover, matrix conditions that include network topology and control properties for both pinned and non‐pinned nodes are obtained. The applicability of this control scheme is illustrated via simulations.

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.

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.

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.

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

Fixed-Time Prescribed Performance Adaptive Trajectory Tracking Control for a QUAV

This brief focuses on the problem of fixed-time adaptive trajectory tracking control for a quadrotor unmanned aerial vehicle (QUAV) subject to error constraints. By virtue of the fixed-time command filter, the phenomenon of “explosion of complexity” (EOC) existing in the conventional backstepping method is successfully eliminated, meanwhile the effect of filtered error is skillfully removed via a new fractional power error compensation mechanism. By combining the prescribed performance control and backstepping design method together with command filter technique, a fixed-time adaptive control strategy is established. It is proved that all signals of the closed-loop system are fixed-time bounded, and the position and attitude tracking errors of the QUAV approach to an arbitrarily small region of the original point within the predefined performance bounds in a fixed time. Finally, simulation results are given to show the availability of the presented fixed-time control algorithm.

Robust adaptive self‐structuring neural networks tracking control of unmanned surface vessels with uncertainties and time‐varying disturbances

AbstractThe trajectory tracking control problem of unmanned surface vessels (USVs) with uncertainties and time‐varying disturbances is investigated. A robust adaptive trajectory tracking control scheme is proposed based on finite‐time control and a self‐structuring neural networks (SSNN) identifier, which can obtain satisfactory performance with an norm‐bounded, expected attenuation level within a finite time. The SSNN is developed to approximate USVs system uncertainties and external disturbances by online learning. Most importantly, a balance is achieved between the optimal number of neurons and the expected performance, which saves significant network resources. The Lyapunov stability analysis shows that the scheme ensures convergence of the tracking error to a small neighborhood around zero in finite time, while all the other closed‐loop signals remain bounded. Moreover, the application of a high‐gain observer effectively reduces the cost of velocity sensors. The feasibility and effectiveness of this control scheme are verified by theorem analysis and numerical simulations.

Segmented guidance law for single moving mass controlled reentry vehicle with multiple constraints

This paper addresses the problem of low guidance accuracy when the traditional guidance method is adopted on the single moving mass controlled reentry vehicle (SMMCRV) with multiple constraints. More specifically, the range and the velocity cannot be controlled accurately by the single bank angle. To solve the aforementioned problems, the segmented guidance law is employed for the reentry phase to achieve the goal of considerable range adjustment and meeting multiple constraints, which consists of the reference trajectory planning & tracking method and the modified rolling guidance law, making full use of the range adjustment capability of the high-altitude phase and the deceleration capacity of the low-altitude phase. For the high-altitude phase, it is difficult to guarantee the precise control of the velocity and range with the bank angle. Therefore, the varying-weight trajectory planning method and adaptive trajectory tracking method considering the combination between tracking errors are employed. In addition, to decrease the influence caused by the lateral motion and the multiple changing rolling direction, the lateral logic and improved B-spline fitting method are also proposed. For the low-altitude phase, the lateral deviation is counteracted by the high speed rotation. Considering the influence on the stability of the SMMCRV caused by the too high rotation, the nonlinear error angle command is designed and the finite-time guidance law is proposed to obtain the high guidance accuracy. Compared with the traditional method, lower terminal velocity dispersion, higher terminal accuracy and lower rotation speed can be obtained, which can be also demonstrated by the numerical simulation.

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System.

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

Adaptive on-Line Approximator-Based Finite-Time Trajectory Tracking Control for the Surface Vessel

An adaptive finite-time trajectory tracking scheme is proposed to solve the problem of trajectory tracking of underactuated surface ship affected by dynamic uncertainties and external disturbances. In this scheme, underactuated transformation is performed by using kinematic virtual control law transformation and bounded constraint. By designing adaptive control to approach the upper bound of uncertainty and unknown disturbance, the problem of parameter uncertainty and external disturbance are solved. It is proven by Lyapunov theory that the error signals in the system can converge quickly to the stable region in finite time. Finally, by comparing the simulation results, it is verified that the proposed control scheme can make the underactuated ship track the desired trajectory in finite time. Compared to the traditional control method, the convergence rate of the system error is faster, and it exhibits strong robustness in the face of unknown external disturbances. This has a certain reference value for practical engineering applications.

Adaptive Trajectory Tracking Algorithm of a Quadrotor with Sliding Mode Control and Multilayer Neural Network

To improve the trajectory tracking accuracy, the anti‐jamming performance, and the environment adaptability of a quadrotor, the paper proposes a new adaptive trajectory tracking algorithm with multilayer neural network and sliding mode control method. The major difference between other related approaches is that the paper uses the multilayer neural network in the system and the neural network is online computing in the whole process. Firstly, the paper establishes the quadrotor dynamic model and introduces the conception of Sigma‐Pi neural network. Then, the paper adds the neural network to the attitude and trajectory tracking control loop. Moreover, the paper designs the adaptive neural network control law. At last, to illustrate the stability of the adaptive control law, the paper gives the Lyapunov stability analysis. Finally, to demonstrate the effectiveness of the method, the paper gives different types of simulation. Comparing with different cases, when increasing the layer of the neural network, the trajectory tracking performance becomes better. In addition, introducing multilayer neural network into the system could improve the anti‐interference ability of the system and has a high‐precision in tracking the desire trajectory.

Data-Based and Opportunistic Integral Concurrent Learning for Adaptive Trajectory Tracking During Switched FES-Induced Biceps Curls.

Hybrid exoskeletons, which combine functional electrical stimulation (FES) with a motorized testbed, can potentially improve the rehabilitation of people with movement disorders. However, hybrid exoskeletons have inherently nonlinear and uncertain dynamics, including combinations of discrete modes that switch between different continuous dynamic subsystems, which complicate closed-loop control. A particular complication is the uncertain muscle control effectiveness associated with FES. In this work, adaptive integral concurrent learning (ICL) motor and FES controllers are developed for a hybrid biceps curl exoskeleton, which are designed to achieve opportunistic and data-based learning of the uncertain human and electromechanical testbed parameters. Global exponential trajectory tracking and parameter estimation errors are proven through a Lyapunov-based stability analysis. The motor effectiveness is assumed to be unknown, and, to help with fatigue reduction, FES is enabled to switch between multiple electrodes on the biceps brachii, further complicating the analysis. A consequence of switching between the different uncertain subsystems is that the parameters must be opportunistically learned for each subsystem (i.e. each electrode and the motor), while that subsystem is active. Experiments were performed to validate the developed ICL controllers on twelve healthy participants. The average (± standard deviation) position tracking errors across each participant were 1.44 ± 5.32 deg, -0.25 ± 2.85 deg, and -0.17 ± 2.66 deg across biceps Curls 1-3, 4-7, and 8-10, respectively, where the average across the entire experiment was 0.28 ± 3.53 deg.

Robust Adaptive Trajectory Tracking for Wheeled Mobile Robots Based on Gaussian Process Regression

Robust Adaptive Trajectory Tracking for Wheeled Mobile Robots Based on Gaussian Process Regression

Barrier Lyapunov function-based robot control with an augmented neural network approximator

PurposeConsidering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes.Design/methodology/approachAn augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error.FindingsLyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme.Originality/valueAn enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

Terrain Adaptive Trajectory Planning and Tracking on Deformable Terrains

In this work, a novel single-level adaptive trajectory planner and tracking controller is developed for off-road autonomous vehicles operating on deformable terrains. Trajectory planning and tracking algorithms often rely on a simplified vehicle model to predict future vehicle states based upon control inputs, hence requiring accurate modeling and parameterization. On off-road deformable terrains this is a challenging task due to unknown terrain parameters and the complex interactions at tire-terrain interfaces, which pose issues in continuous differentiability, operating conditions, and computational time. To address these difficulties, in this paper, a neural network deformable terrain terramechanics model and its implementation within a terrain adaptive model predictive control algorithm is presented to improve vehicle safety and performance through more accurate prediction of the plant response. It is shown in simulations that the neural network is able to predict the lateral tire forces accurately and efficiently compared to the Soil Contact Model as a state-of-the-art model and is able to yield accurate bicycle model predictions. It is demonstrated that the implementation of the neural network within model predictive control can outperform both a baseline Pacejka-based and a rapidly exploring random tree controller by improving performance and allowing for more severe maneuvers to be completed that otherwise lead to failure when terrain deformations are not explicitly taken into account. The improved performance achieved through estimating terrain parameters online in an adaptive controller is highlighted against the nonadaptive realization. Finally, it is shown the algorithm is conducive to real-time implementation.

Adaptive trajectory tracking control of vector propulsion unmanned surface vehicle with disturbances and input saturation

Adaptive trajectory tracking control of vector propulsion unmanned surface vehicle with disturbances and input saturation

Robust adaptive asymptotic trajectory tracking control for underactuated surface vessels subject to unknown dynamics and input saturation

Robust adaptive asymptotic trajectory tracking control for underactuated surface vessels subject to unknown dynamics and input saturation