Adaptive Tracking Control Research Articles

Distributed model free adaptive fault-tolerant consensus tracking control for multiagent systems with actuator faults

The model free fault-tolerant consensus tracking control problem is studied for the multiagent systems with actuator faults. To reduce the impact of fault, an adaptive estimation law is developed to estimate the fault information online, which doesn't need an additional training and can reduce the computational cost. With the help of the adaptive fault compensation, a distributed model free adaptive fault-tolerant consensus tracking mechanism is designed to guarantee that agents are able to track the expected signal. Furthermore, only the input/output data of agents are employed throughout the design process, the system dynamics are not required. The effectiveness of developed approach is illustrated through the simulation examples.

A new Q‐function structure for model‐free adaptive optimal tracking control with asymmetric constrained inputs

SummaryThis article aims to design a model‐free adaptive tracking controller for discrete‐time nonlinear systems with unknown dynamics and asymmetric control constraints. First, a new Q‐function structure is designed by introducing the control input into the tracking error of the next moment, in order to eliminate the final tracking error, avoid the steady control, and ignore the discount factor. Second, via system transformation, a general performance index is developed to overcome the challenge caused by asymmetric constraints of implicit control inputs. By this operation, the constrained tracking problem is converted to an unconstrained optimal tracking problem without the traditional nonquadratic performance function that is only applicable to explicit control inputs. Then, a value‐iteration‐based Q‐learning (VIQL) algorithm is derived to seek the optimal Q‐function and the optimal control policy by using offline data rather than the mathematical model. Next, the convergence, monotonicity, and stability properties of VIQL are investigated to demonstrate that the iterative Q‐function sequence can converge to the optimal Q‐function under ideal conditions. To realize the VIQL algorithm, the critic neural network is employed to approximate the Q‐function. Finally, simulation results and comparative experiments are conducted to demonstrate the validity and effectiveness of the present VIQL scheme.

A reinforcement learning approach for adaptive tracking control of a reusable rocket model in a landing scenario

This work explores the application of reinforcement learning to control the descend/landing phase of a reusable rocket model, where the rocket must track a nominal/planar reference path with adequate attitude. To tackle the control problem, which is multivariable, a cascade control architecture is proposed, consisting of three single-input single-output controllers. The inner control loop is responsible for controlling the attitude using a reference for attitude provided by the outer loop and the thrust vectoring, where the gimbal angle is the manipulated variable. The position error is handled by the external loop, that handles the altitude error, used to adjust thrust. Several configurations, using Linear Quadratic Gaussian (LQG) control (LQ regulator with a Kalman observer), PID control and adaptive control based on Reinforcement Learning (optimal control with learning capability based on a linear regression model), are evaluated and compared.

Command filtered adaptive fuzzy tracking control for uncertain stochastic nonlinear systems with event-triggered input

Command filtered adaptive fuzzy tracking control for uncertain stochastic nonlinear systems with event-triggered input

Adaptive Neural Tracking Control of a Class of Hyperbolic PDE With Uncertain Actuator Dynamics.

This article investigates the adaptive neural tracking control problem for a class of hyperbolic PDE with boundary actuator dynamics described by a set of nonlinear ordinary differential equations (ODEs). Particularly, the control input appears in the ODE subsystem with unknown nonlinearities requiring to be estimated and compensated, which makes the control task rather difficult. It is the first time to consider tracking control of such a class of systems, rendering our contributions essentially different from the existing literature that merely focus on the stabilization problem. By formulating a virtual exosystem to generate a reference trajectory, we propose a novel design of the adaptive geometric controller for the considered system where neural networks (NNs) are employed to approximately estimate nonlinearities, and finite and infinite-dimensional backstepping techniques are leveraged. Moreover, rigorously theoretical proofs based on the Lyapunov theory are provided to analyze the stability of the closed-loop system. Finally, we illustrate the results through two numerical simulations.

Vision-Based Adaptive Trajectory Tracking Control of Wheeled Mobile Robot With Unknown Translational External Parameters

In this article, the vision-based trajectory tracking control problem of wheeled mobile robot is addressed, and different from the traditional vision-based adaptive control methods, the case that the camera is not installed in the center of the mobile robot is considered. In this case, the visual model needs to consider the influence of unknown translational external parameters. To solve this problem, a novel adaptive vision-based trajectory tracking control method is proposed, and a two-stage switching trajectory tracking control law is designed, where the adaptive control method is used to compensate the unknown external parameters. The asymptotic convergence of tracking errors are strictly proved by applying the Lyapunov method. Simulation and experimental results show that proposed approach can effectively control the mobile robot to track the desired trajectory in the presence of unknown external translational parameters.

Adaptive Neural Asymptotic Tracking Control for PMSM Systems Under Current Constraints and Unknown Dynamics

Adaptive Neural Asymptotic Tracking Control for PMSM Systems Under Current Constraints and Unknown Dynamics

Adaptive Compensation Tracking Control for Parallel Robots Actuated by Pneumatic Artificial Muscles With Error Constraints

As pneumatic artificial muscles (PAMs) are similar to biological muscles in structure and movement mechanisms, parallel robots actuated by PAMs have development prospects in rehabilitation and industry, with advantages such as compliance, high safety, strong bearing capacity, and satisfactory dynamic performance. However, the parameter uncertainties and model complexity related to inherent characteristics of parallel robots actuated by PAMs (e.g., time-varying, coupling, hysteresis, creep, and high nonlinearity), bring challenges to accurate dynamic modeling and controller design. Therefore, to achieve satisfactory tracking performance, this paper presents an adaptive compensation tracking controller with error constraints for parallel robots actuated by PAMs. The proposed controller deals with parameter uncertainties by estimating system parameters to ensure accurate tracking, which is indicated as an effective solution for a combination of PAMs and parallel robots. Furthermore, using desired trajectory signals in the complicated regression matrix, the online computational burden is significantly reduced. Moreover, to improve operation safety further, an auxiliary term with a theoretical demonstration guarantees that the tracking errors are maintained within allowable ranges. Then, the closed-loop stability is demonstrated by Lyapunov techniques. As far as we know, it is the first time that the challenges of parameter uncertainties, computational burdens, and error constraints of parallel robots actuated by PAMs are simultaneously addressed, which has both theoretical significance and practical value. Finally, the hardware experiments are implemented under different scenarios, and the results indicate that the proposed method achieves satisfactory tracking performance.

Adaptive Wavelet Tracking Control of Dual-Linear-Motor-Driven Gantry Stage With Suppression of Crossbeam Rotation

Adaptive Wavelet Tracking Control of Dual-Linear-Motor-Driven Gantry Stage With Suppression of Crossbeam Rotation

Adaptive Neural Network-Quantized Tracking Control of Uncertain Unmanned Surface Vehicles With Output Constraints

Adaptive Neural Network-Quantized Tracking Control of Uncertain Unmanned Surface Vehicles With Output Constraints

Cooperative Time-Varying Formation Fuzzy Tracking Control of Multiple Heterogeneous Uncertain Marine Surface Vehicles With Actuator Failures.

This article addresses the cooperative time-varying formation fuzzy tracking control problem for a cluster of heterogeneous multiple marine surface vehicles subject to unknown nonlinearity and actuator failures. The proposed cooperative control scheme consists of two parts: 1) a distributed time-varying formation observer and 2) a decentralized adaptive fuzzy tracking controller. The distributed observer is designed to obtain a predefined time-varying formation pattern under a directed communication topology. Subsequently, based on the states of the distributed observer, a decentralized fuzzy tracking control law is developed using fuzzy-logic systems and the adaptive approach. Lyapunov functions are constructed to guarantee that the controlled marine vehicles attain the desired time-varying formation with asymptotical stability of tracking errors. Finally, simulation results are presented to validate the efficacy of the proposed control methodology.

Robust switching adaptive tracking control for uncertain high-order fully actuated systems based on fully actuated system approaches

This paper investigates the switching adaptive tracking control problem for high-order fully actuated (HOFA) systems with time-varying uncertainty. A type of special differentiable functions, named as pulse-like functions, are introduced firstly. Based on the function and the HOFA system approaches, this paper designs a new robust switching adaptive tracking (RSAT) controller, which guarantees that the state vector and its derivatives of the HOFA system are globally bounded, and the system tracking error vector converges into a preseted neighborhood of equilibrium. Most importantly, the preseted neighborhood is completely unrelated to the system initial values and the uncertainty. Furthermore, different from traditional switching adaptive control (SAC) methods, of which the controllers are often discontinuous, the RSAT controller designed in this paper is continuous (even differentiable), which is a key improvement to enhance dramatically the practicability of SAC. Finally, three simulation examples including single-link robot illustrate the effectiveness and the practicability of our proposed control method.

Global Prescribed Performance Control for Strict Feedback Systems Pursuing Uncertain Target.

In this work, an online solution for reconstructing and predicting the uncertain target trajectory in real-time is proposed based on general regression neural network (GRNN). On this basis, an adaptive tracking control scheme guaranteeing prescribed performance is suggested for a class of strict-feedback systems with unknown control directions. In contrast to existing trajectory reconstruction methods, the one presented in this note does not require prior modeling of the uncertain target or offline training. Contrary to most current state-of-the-art prescribed performance control (PPC) technology, a novel time-varying scaling function and its corresponding translation function are introduced such that no strict constraints on initial conditions are needed, that is, global stability is achieved. The proposed control scheme allows the output of the system to chase the predicted value of the uncertain target, and the tracking error converges to a prescribed small set within a preassigned time, despite unmatched uncertainties and unknown control directions. The benefits of the proposed control scheme are confirmed by numerical simulations.

Composite Learning Adaptive Tracking Control for Full-State Constrained Multiagent Systems Without Using the Feasibility Condition.

This article proposes a distributed consensus tracking controller for a class of nonlinear multiagent systems under a directed graph, in which all agents are subject to time-varying asymmetric full-state constraints, internal uncertainties, and external disturbances. The feasibility condition generally required in the existing constrained control is removed by using the proposed nonlinear mapping function (NMF)-based state reconstruction technology, and the Lipschitz condition usually needed in the consensus tracking is also canceled based on the adaptive command-filtered backstepping framework. The composite learning of the neural network-based function approximator (NN-FAP) and the finite-time smooth disturbance observer (DOB) provides a novel scheme for handling internal and external uncertainties simultaneously. One advantage of this scheme is that the use of online historical data of the closed-loop system strengthens the excitation of NN's learning. Another advantage is that the DOB with NN-FAP embedding realizes that the finite-time observation for external disturbance in the case of the system dynamics is unknown. A complete controller design, sufficient stability analysis, and numerical simulation are provided.

Adaptive tracking control for a class of nonlinear systems with intermittent actuator faults under prescribe output tracking performance

AbstractThis paper investigates the adaptive tracking control problem of a class of nonlinear systems with intermittent actuator faults and prescribed performance. In the backstepping design process, a performance function is incorporated to ensure that the tracking error adheres to the prescribed performance criteria. Simultaneously, multi‐dimensional Taylor networks are used to approximate the unknown nonlinear functions in the system. Subsequently, to avoid jumps in the improved Lyapunov function when faults occur, a boundary estimation method is presented, which effectively compensates for the impact of intermittent actuator faults. Ultimately, based on Lyapunov stability theory, it is proven that all signals of the closed‐loop system are bounded, and the tracking error can satisfy the prescribed transient and steady‐state performance. Simulation results demonstrate the effectiveness of the proposed scheme.

Fault‐tolerant adaptive tracking control for spacecraft attitude with asymptotic convergence

AbstractIn this paper, we present a novel spacecraft attitude adaptive tracking control strategy that addresses the challenges posed by unknown inertia parameters, bounded unstructured uncertainties, and various actuator faults. To mitigate the adverse effects of these multiple uncertainties, our proposed control algorithm utilizes a simple tracking‐error feedback and compensation term, which only requires two adaptive parameters. This approach ensures asymptotic tracking convergence by employing an infinitely integrable inequality that incorporates saturation functions. As a result, the complexity of controller design and computational burden are significantly reduced. Finally, several numerical simulations are conducted to validate the feasibility and superiority of our proposed controller.

Predefined‐time adaptive fuzzy tracking control for switched nonlinear systems

AbstractThe paper focuses on the issue of predefined‐time adaptive fuzzy tracking control for switched nonlinear systems. The complicated unknown nonlinear function is approximated by utilizing the fuzzy logic system. A novel adaptive fuzzy control scheme is proposed by using the common Lyapunov function and backstepping technology on the basis of the definition of predefined‐time stability, which can preset the setting time of tracking errors. The findings indicate that the presented controller can ensure that all signals remain bounded in the closed‐loop system under arbitrary switching conditions, and the tracking error can converge to the neighborhood near the origin within a predefined time. The feasibility of the developed control approach is further proved through an example of numerical simulation.

An adaptive trajectory tracking control scheme for a novel fully actuated multirotor unmanned aerial vehicle without using velocity signals

SummaryThis paper introduces a novel fully actuated multirotor unmanned aerial vehicle (UAV) and develops an adaptive control scheme for it without relying on velocity signals. Conventional multirotors are limited by their strong coupling between translation and rotation motion, which makes it difficult to operate one motion without affecting the other. However, this novel multirotor's fully actuated design allows for independent translation and rotation motion, leading to better operational performance and expanded application scope. Additionally, velocity measurement signals can be prone to noise pollution and signal drift, making them unreliable for control purposes. Therefore, this proposed control scheme is developed without using velocity signals. The study utilizes a control performance improving method and guarantees stability of the closed‐loop system using the direct Lyapunov method. Finally, numerical simulations are employed to demonstrate the effectiveness of the proposed control scheme.

Adaptive prescribed‐time tracking control for uncertain nonlinear systems with full state constraints

SummaryThis paper investigates the adaptive tracking control in a prescribed‐time for a class of uncertain nonlinear systems with a full state constraint. Firstly, in order to restrict the state constraints, the barrier Lyapunov function is employed and parameter uncertainty is handled based on linear‐in‐the‐parameters (LIP). A prescribed‐time adaptive controller is then designed by backstepping to ensure that the closed‐loop system can reach zero in a prescribed finite time under any given initial conditions. The prescribed‐time is independent of the design of the parameters. Finally, two simulation examples verify the effectiveness and usability of the proposed method.

Adaptive fuzzy anti-saturation tracking control for uncertain nonlinear systems with external disturbance

In this paper, a predictive-based adaptive anti-saturation tracking control scheme is designed for uncertain multiple-input and multiple-output (MIMO) nonlinear systems subject to input constraints and external disturbance. Taking the safety of the MIMO nonlinear systems into account, a safety saturation threshold, which is less than the physical device saturation threshold, is given. In addition, since all signals of the auxiliary systems are not immediately employed to the systems when the input saturation happens, a new predictive-based auxiliary system is designed. Furthermore, to estimate the compound disturbance, with the help of the fuzzy control technology, the nonlinear disturbance observer (NDO) is used. Then, the stability of the closed-loop systems is proved under the designed controller. Finally, a simulation example is given to illustrate the feasibility of the designed anti-saturation controller.