Adaptive Tracking Control Method Research Articles

Adaptive tracking control method for shearer cutting trajectory based on the combined strategy

Adaptive tracking control method for shearer remains challenging caused by the contradiction between coal recovery rate and equipment safety under current technological limitations, has drawn more and more attention over the past decades due to its important role in reducing personnel, increasing safety, and improving efficiency for underground mining. Therefore, a novel adaptive tracking control method for shearer cutting trajectory based on the combined strategy is proposed, whose core is the switching method of the sub-strategies based on the linear quadratic regulator (LQR) and error band control (EBC) with switching threshold determined by the Multi-strategy Marine Predator Algorithm. The performance of the proposed method is demonstrated by comparing with the existing methods reported. The experimental results reveal that the proposed method maintains a high recovery rate while improving coal mining efficiency.

Adaptive Finite-Time-Based Neural Optimal Control of Time-Delayed Wheeled Mobile Robotics Systems.

For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton-Jacobi-Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed.

Reinforcement learning-based adaptive tracking control for mismatched non-affine nonlinear systems with coupled uncertainties

This paper studies a reinforcement learning-based adaptive non-affine tracking control method for a class of uncertain mismatched non-affine nonlinear systems. The considered system is not only affected by external mismatched disturbances and internal uncertainties, but also influenced by the non-affine control structures. Firstly, an auxiliary integral system is developed for the purpose of isolating the non-affine control input. Secondly, by designing the actor-critic networks to evaluate the system control performance and generate the reinforcement signal, the unknown internal uncertainties can be handled. Thirdly, based on the output of reinforcement learning network, several disturbance compensation laws are constructed to address the adverse impact of matched and mismatched disturbances. As a result, a novel intelligent adaptive non-affine controller is proposed by integrating actor-critic reinforcement learning framework, disturbance compensation and adaptive laws. It has been proved that closed-loop system are stable and the tracking errors are bounded. The numerical simulation results show the effectiveness and superiority of the proposed method.

RBFNN-Based Adaptive Integral Sliding Mode Feedback and Feedforward Control for a Lower Limb Exoskeleton Robot

In this paper, an adaptive trajectory tracking control method combining proportional–integral–derivative (PID) control, Radial Basis Function neural network (RBFNN)-based integral sliding mode control (ISMC), and feedforward control, i.e., the PIDFF-ISMC method, is proposed. The PIDFF-ISMC method aims to deal with the dynamic uncertainties, disturbances, and slow response in lower limb exoskeleton robot systems. Firstly, the Lagrange function is utilized to establish dynamic models that include frictional force and unmodeled dynamics. Secondly, the feedback controller is composed of PID and RBFNN-based ISMC to improve tracking performance and decrease the chattering phenomenon. The feedforward controller is adopted to reduce the response time by employing inverse dynamic models. Finally, the Lyapunov function proves the stability of the proposed control method. The experimental results show that the proposed control method can effectively reduce the trajectory tracking error and response time at two different speeds while alleviating control input chattering.

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

An adaptive dynamic surface trajectory tracking control method based on the Nussbaum function is proposed for a class of quadrotor UAVs encountering unknown external disturbances and unidentified nonlinearities. By transforming controller expressions into numerical solutions, the challenge of overly complex controller design expressions is addressed, simplifying the overall controller design process and enhancing the efficiency of simulation programs. Additionally, an adaptive controller based on Nussbaum gain is introduced to effectively resolve actuator saturation issues. This approach mitigates complexities associated with traditional control design and ensures smooth operation of the quadrotor UAVs. The proposed methodology offers promising prospects for enhancing the robustness and performance of quadrotor UAVs under uncertain operating conditions. Finally, to validate the effectiveness of the proposed control scheme, a hardware-in-the-loop experimental setup is constructed. The dynamic model of the quadrotor UAVs and the proposed controller scheme are implemented on the Rapid Control Prototype (RCP) and Real-Time Simulator (RTS), respectively. This facilitates a semi-physical simulation experiment, providing a basis for the subsequent application of the control scheme to actual aerial vehicles. The concluding experimental results affirm the effectiveness of the proposed control scheme and highlight its potential for practical applications.

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.

Learning-Observer-Based Adaptive Tracking Control of Multiagent Systems Using Compensation Mechanism

This paper focuses on the adaptive consensus tracking problem for nonlinear multiagent systems (MASs) with unmeasurable state and input saturation. For unmeasurable state and nonlinearities, a learning-observer inspired by the idea of extended state observer (ESO) and the learning ability of neural network (NN) is established to observe the reconstructed system. In view of input saturation, a nonlinear auxiliary system with the same order is constructed to obtain the compensation signals. Different from the previous auxiliary system, the proposed method simultaneously compensates the change of nonlinearity caused by input saturation. Subsequently, a consensus tracking error is designed to ensure that the follower tracks the neighbor compensated output. Moreover, an adaptive consensus tracking control method based on learning-observer and compensation mechanism is proposed under the backstepping framework. The stability analysis is given by virtue of the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through the simulation of unmanned air vehicles (UAVs) with one virtual leader and four followers.

Adaptive radial basis function neural network sliding mode control of robot manipulator based on improved genetic algorithm

ABSTRACT Since the trajectory-tracking control performance of multi-joint robot manipulator may be degraded due to modeling errors and external disturbances, this paper designs a new adaptive robot manipulator trajectory tracking control method through improved genetic algorithm and radial basis function neural network sliding mode control (IGA-RBFNNSMC). Firstly, the genetic algorithm (GA) is improved by establishing superior populations centered on individuals with high fitness values and selecting individuals in the superior populations for crossover and variation. Secondly, the improved genetic algorithm (IGA) is used for the optimization of the center vector and width vector of the Gaussian basis function in radial basis function (RBF) neural network. Then, based on the dynamics model of the robot manipulator, the modeling errors are approximated by RBF neural network and eliminated by sliding mode control (SMC), and the Lyapunov theorem is used to prove the stability and convergence of the control system. Finally, a two-joint robot manipulator is taken as the research objective and the simulation results show that IGA can significantly reduce the solution time on the basis of guaranteed accuracy and IGA-RBFNNSMC can make the trajectory tracking control accurate and more efficient, which proves the effectiveness of the proposed control method.

Tracking controller of extended chained nonholonomic systems with matched disturbance and input saturation

AbstractThis paper addresses the tracking control for an extended chained nonholonomic system under input saturation and matched disturbances. Specifically, the tracking error dynamic system is split into two subsystems in a cascade form. An integral sliding mode saturated controller is designed in combination with a finite‐time observer (FTDO) to ensure the finite‐time stability of the first subsystem, thereby simplifying the design of the second subsystem. A backstepping‐based adaptive saturated tracking control method is proposed for the second subsystem by incorporating the filter technology. The stability of the resulting closed‐loop system is demonstrated, and the tracking errors can be arbitrarily minimized through the appropriate design parameter selection. The effectiveness of the proposed scheme is validated through simulation and experimental results.

Non‐singular fixed‐time fuzzy adaptive control for nonlinear systems subject to actuator faults

AbstractThis paper proposes a non‐singular fixed‐time fault‐tolerant adaptive tracking control method for a kind of multi‐input and multi‐output nonlinear system, which is in form of non‐strict feedback and is subject to actuator faults. The major contribution, compared to other practical fixed‐time fault‐tolerant control methods, is that the fixed time can be calculated precisely. By applying fuzzy logic systems, the identification of unknown nonlinearities is achieved, and by the boundness of fuzzy basis functions, the algebraic loop problem is avoided. Based on the fixed‐time stability theory, the practical fixed‐time fault‐tolerant adaptive tracking control method is proposed with adaptive back‐stepping control technology, in which the control singular problem is overcome by applying the adding power integration method. By the Lyapunov function theory, the practical fixed‐time stability of the closed‐loop system is theoretically proved. And even in the case of unknown actuator faults, the tracking error of the controlled system for the given command signal can still converge to a small neighbourhood of zero within a fixed time, regardless of the system initial state. Finally, the proposed control method is successfully implemented on a multi‐motor control platform, proving its effectiveness, and multiple comparative simulations are carried out, demonstrating its superiority.

Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone

In this paper, an adaptive fuzzy tracking control method is proposed to address the issues of dead-zone and unobservable states in a flexible robotic arm system. The control design process begins with the utilization of a fuzzy logic system to approximate the nonlinear functions present in the flexible robotic arm system. To estimate the unobservable states of the system, a state observer is then designed. To alleviate the computational complexity during controller design, the command filtering technique is introduced. Additionally, the Nussbaum function is incorporated to address the unknown control gain problem. The stability of the system can be verified through the design of a Lyapunov function. This study’s simulation results demonstrate that the designed control system can closely track the specified reference signals. The closed-loop system effectively controls the flexible robotic arm, as verified through experimentation.

Adaptive tracking control for a class of stochastic nonlinear systems with input delay using multidimensional Taylor network

AbstractIn this paper, an adaptive tracking control strategy based on multidimensional Taylor network (MTN) is designed for a class of stochastic nonlinear systems with input delay, in which MTN is mainly introduced to approximate uncertain nonlinear functions. Moreover, the effect of input delay is compensated by exploiting an appropriate auxiliary system with the same order as the considered system. By utilizing the backstepping technique and classical adaptive approach, an MTN‐based adaptive tracking control method is proposed, which guarantees that all the signals in the closed‐loop system remain bounded in probability. Furthermore, unlike most existing approaches, the tracking error can be bounded by an explicit expression of design parameters in the mean quartic sense. Finally, two examples are given to verify the validity of the presented scheme.

Event trigger based adaptive neural trajectory tracking finite time control for underactuated unmanned marine surface vessels with asymmetric input saturation

An adaptive finite time trajectory tracking control method is presented for underactuated unmanned marine surface vessels (MSVs) by employing neural networks to approximate system uncertainties. The proposed algorithm is developed by combining event-triggered control (ETC) and finite-time convergence (FTC) techniques. The dynamic event-triggered condition is adopted to avert the frequent acting of actuators using an adjustable triggered variable to regulate the minimal inter-event times. While solving the system uncertainties and asymmetric input saturation, an adaptive neural networks based backstepping controller is designed based on FTC under bounded disturbances. In addition, via Lyapunov approach it is proved that all signals in the closed-loop system are semi-global uniformly ultimately bounded. Finally, simulations results are shown to demonstrate the effectiveness of this proposed scheme.

Event-triggered adaptive finite time trajectory tracking control for Underactuated Vessel considering unknown time-varying disturbances

Abstract This paper aims to solve the finite-time trajectory tracking problem of underactuated surface ships under the influence of dynamic uncertainty, unknown external time-varying interference and limited communication resources, an event-triggered robust adaptive finite-time trajectory tracking control method for underactuated ships is designed by combing the existing trajectory tracking control methods and engineering needs in actual navigation. It can obviously improve the tracking accuracy of the ship, and complete the tracking task safely and efficiently. This scheme transforms the composite uncertain vector synthesized by uncertain parameters and external disturbances into a linear parameterized form. Next, considering the limitation of communication resources, a relative threshold event triggering mechanism is introduced to further extend the triggering time interval on the basis of the fixed threshold triggering scheme. Then, an event-triggered robust adaptive finite-time trajectory tracking control scheme is designed for underactuated ships, and a rigorous stability proof is provided for the finite-time trajectory tracking control scheme designed above through Lyapunov stability theory. Simulation experiment is carried out in MATLAB and the results show that the finite-time trajectory tracking control scheme proposed in this paper can effectively solve the problems of dynamic uncertainty, unknown time-varying interference from the outside world, and limitation of communication resources. This paper can provide theoretical support for the autonomous navigation of intelligent ships.

A predefined time decentralized adaptive tracking control method for interconnected nonlinear systems

SummaryIn this paper, the problem of predefined time adaptive decentralized tracking control for a class of interconnected nonlinear systems is investigated. Firstly, a time‐varying constraint function is introduced to convert the tracking control problem into one with a constraint on tracking error, which can successfully remove the restriction of initial conditions. Secondly, the asymmetric constraint requirements on tracking error are achieved via defining a novel coordinate transformation function. Then, based on the proposed constraint function and the backstepping method, a decentralized adaptive controller is constructed. Furthermore, it is proved that all signals of the closed‐loop system are globally uniformly ultimately bounded (GUUB) via Lyapunov stability theory. Finally, the effectiveness of the proposed scheme is confirmed by two simulation examples.

Closed-loop Control of Biaxial Fatigue Loading of Wind Turbine Blades Based on Visual Inspection

Based on the inertial vibration excitation device, a biaxial fatigue loading system for wind turbine blades is established by using visual inspection technology and virtual spindle decoupling to control the vibration excitation motion of wind turbine blades. The image feature point tracking method is used to detect the blade vibration state. Considering the system vibration frequency characteristics, the phase difference between the exciting force and the vibration displacement is detected based on the phase detector, and the system resonance frequency is adaptively tracked to achieve accurate control of the double-free amplitude of the blade. The fatigue test shows that the control system can maintain the amplitude error of the blade, the adaptive tracking control method is reliable and effective, and the accuracy and reliability of the fatigue test are improved.

Adaptive Output Feedback Fuzzy Fault-Tolerant Control for Nonlinear Full-State-Constrained Switched Systems.

In this article, an output feedback adaptive fuzzy tracking control method for a class of switched uncertain nonlinear systems with actuator failures and full-state constraints is proposed under an arbitrary switching signal combining the dynamic surface technique. Since the state variables of the system under study are not measurable, a fuzzy observer is constructed to identify the unmeasured states. The actuator failures are considered in the system. To compensate this failure, a fault-tolerant controller is proposed. Moreover, each state needs to be kept within the constraints, so the tangent Barrier Lyapunov function is selected to solve the full-state constraint problem, and the unknown nonlinear function is approximated by fuzzy-logic systems (FLSs). We also proved that all signals in the closed-loop system are bounded. Furthermore, the states can be kept within the predetermined range even if the actuator fails. Finally, a simulation example is given to verify the effectiveness of the proposed control strategy.

Command Filtered Adaptive Fuzzy Control for Induction Motors With Iron Losses and Stochastic Disturbances via Reduced-Order Observer

This brief presents a fuzzy adaptive tracking control method based on command filtering and reduced-order observer for induction motor (IM) with iron losses and stochastic disturbances. Firstly, the rotor position and angular velocity of IM are estimated by a reduced-order observer. Then, the stochastic nonlinear functions are approximated by the fuzzy logic systems. In addition, the complexity of computation in traditional backstepping design is overcome by exploiting the command filtered technique. The results of the simulation and experiment shown that the presented control method can track the desired signal effectively.

Event-triggered adaptive fuzzy asymptotic control design for uncertain stochastic nonlinear systems

The adaptive fuzzy asymptotic tracking control (AFATC) method is presented for uncertain stochastic nonlinear systems by virtue of event-triggered mechanism. The fuzzy logic systems are borrowed to address system uncertainties. By applying the backstepping technique, the AFATC strategy is recursively constructed. Different from the existing approximation-based methods, the developed controller can achieve asymptotic convergence feature. Moreover, the event-triggered mechanism is integrated into the controller design process to reduce data transmission and unnecessary resource waste. By applying the Lyapunov analysis and several helpful lemmas, the stability and convergence of the considered systems are ensured. Finally, the availability of the theory analysis is verified by a chemical reactor system.

Adaptive Tracking Event-Triggered Control of Quarter-Car Bioinspiration Active Suspension Systems

In this article, an adaptive tracking control method for the bionic active suspension system with the event-triggered mechanism is proposed. In order to achieve the ideal control objectives, a nonlinear suspension system structure is proposed based on the biological inspiration. In the existing research, the suspension system tracking control methods are only aimed at a single part of the mass, respectively. But this article adopts a novel tracking method for the vertical displacement difference, which can track the ideal reference model more accurately. For the sake of alleviating the problem of limited resources in the process of vehicle communication, a control method combined with the events triggering of the relative threshold is proposed. The design of the controller makes the vertical displacement and vertical moving speed of the bionic suspension system close to zero. It can effectively improve the communication efficiency between the actuator and the controller. In the design of this system, all the signals involved are bounded. The Zeno behavior is successfully avoided among the event-triggered control mechanism. Finally, the feasibility and rationality of this method are verified by the simulation analysis of the bionic suspension system.