Output Tracking Performance Research Articles

Nonlinear hysteresis system control based on sliding mode neural network and observer

In this paper, a sliding mode neural network controller with observer is presented and employed in a nonlinear hysteresis system to eliminate the system’s unknown hysteresis and uncertainties. First, a sliding mode controller is proposed for trajectory tracking, which simplifies the computational complexity and ensures robustness. Second, radial basis function neural network is applied for approximating unknown nonlinear function in the control system. Then, an observer is utilized to estimate and observe the hysteresis nonlinearity and cancel the effect of the hysteresis phenomenon. Based on the proposed control scheme, the influence of hysteresis is suppressed, and the system has robust stability and achieves high output tracking performance. Finally, theoretical analysis and numerical simulations confirm the effectiveness of the designed control law.

[formula omitted] output tracking control for polynomial parameter-varying systems via homogeneous Lyapunov methods

This paper investigates the H∞ output tracking control for polynomial parameter-varying systems—a class of nonlinear parameter-varying systems. Based on the homogeneous polynomial Lyapunov function (HPLF), a state feedback controller is presented to make the augmented system (composed of the original system and the tracking error system) robust exponentially stable, so that the output of the original system can track the reference signal and satisfy the H∞ output tracking performance. The conservativeness of the theoretical results is reduced due to the following facts: (1) the HPLF can depend on the full states and time-varying parameters; and (2) there are no constraints on the input coefficient matrix and the inverse of the Lyapunov matrix. In particular, when the system states and time-varying parameters are confined to a compact set, local results are obtained based on the generalized S-procedure. The solvable conditions are given in terms of state-and-parameter-dependent linear matrix inequalities, which can be solved by sum of squares (SOS) techniques. Finally, the feasibility and effectiveness of the proposed method are verified by a numerical example and the heading control of unmanned surface vehicle.

Adaptive neural network finite‐time control for fractional‐order nonlinear systems with external disturbance

AbstractThis paper is concerned with the finite‐time tracking control problem of fractional‐order nonlinear systems (FONSs) with uncertainty and external disturbance. A novel design scheme of the adaptive neural network finite‐time controller (ANNFTC) is developed by utilizing the theory of finite‐time stability and fractional‐order dynamic surface control (DSC) scheme combined with backstepping method. Radial basis function neural networks (RBF NNs) are employed to estimate the unknown nonlinear function. Furthermore, an auxiliary function is introduced to approximate the unknown upper bounds of the approximation error in RBF NNs and external disturbance. The ANNFTC ensures the finite‐time boundedness of all signals in FONSs and enhances the system output's tracking performance. The effectiveness of the proposed approach is demonstrated through a simulation example, providing empirical evidence to support the theoretical framework presented in this paper.

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.

Observer-based robust preview tracking control for a class of continuous-time Lipschitz nonlinear systems

<p>In this paper, a novel observer-based robust preview tracking controller design method is proposed for a class of continuous-time Lipschitz nonlinear systems with external disturbances and unknown states. First, a state observer is designed to reconstruct unknown system states. Second, using differentiation, the state lifting technique, the differential mean value theorem, and several ingenious mathematical manipulations, an augmented error system (AES) containing the previewable information of a reference signal is constructed, thereby transforming the tracking control problem into a robust $ H_{\infty} $ control problem. Based on linear parameter-varying (LPV) system theory, a sufficient condition for asymptotic stability of a closed-loop system with a robust $ H_{\infty} $ performance level is established in terms of the linear matrix inequality (LMI). Furthermore, a tracking controller, which includes observer-based feedback control, integral control, and preview feedforward compensation, is established for the original system. In particular, the tracking controller design is simplified by computing the observer and tracking controller gains simultaneously via only a one-step LMI algorithm. Finally, numerical simulation results demonstrate that the proposed controller leads to superior improvement in the output tracking performance compared with the existing methods.</p>

Disturbance-observer-based control for non-homogeneous semi-Markov jump systems with time-varying delays

This paper discusses the issue of disturbance-observer-based control (DOBC) for a kind of continuous-time non-homogeneous semi-Markov jump systems (SMJSs) with time-varying delays. Unlike the existing results, the system examined in this article is about non-homogeneous SMJSs, which indicates that its transition rates (TRs) is time-varying. The versions of TRs matrices are selected by a switching signal, which is independent of the semi-Markov process that decides on the system modes. The time-varying TRs are established to be of two situations: stochastic process and arbitrary switching, respectively. First, we construct a novel class of Lyapunov function that is related to both system mode and switching signal mode, thus it can make more use of information about the system and help reduce conservatism. Second, some conditions are given to guarantee the exponentially mean square stable of the SMJSs and H∞ output tracking performance index. Finally, two examples are provided to demonstrate the efficacy of research methods.

Weak disturbance decoupling of high‐order fully actuated nonlinear systems

SummaryIt is well known that all practical control systems are subject to external disturbances. Therefore, a feedback controller should be designed so that the closed‐loop system has some desired properties such as stability, good tracking performance, at the same time, the impact of external disturbances on the performance of the closed‐loop system is attenuated to some degree. On the other hand, almost all models for real fully actuated systems, which are derived based on the physical laws, are described by a set of high order differential equations. The traditional method for controlling these fully actuated systems is to convert the high order differential equations to first order differential equations, which are called the state equations, and then to design feedback controllers by using the state‐space design methods. Such conversion causes some inconveniences. To overcome such inconveniences, a high‐order fully actuated (HOFA) design method has been proposed to control HOFA systems. The main goal of this paper is to design a weak disturbance decoupling controller for HOFA nonlinear systems to guarantee the stability and desired output tracking performance of the closed‐loop system and to attenuate the impact of disturbances.

Two networked predictive control methods for output tracking of networked systems with plant-model mismatch

This paper addresses the output tracking control problem for networked systems with plant-model mismatch, where two-channel random network delays are considered. According to the different ways of obtaining future control commands, two networked predictive control (NPC) methods are designed. One is the NPC method using one-way time delays, called ONPC, and the other is the NPC method using round-trip time delays, called RNPC. Furthermore, they are compared in terms of the way of compensation for two-channel delays, prediction process, and communication load, respectively. Then, the closed-loop stability conditions of the RNPC system and the ONPC system for the plant-model mismatch and match cases are derived, and their output tracking performance is also theoretically analysed. Finally, the results of the theoretical analysis are verified through numerical simulation that illustrates the effectiveness of the proposed two NPC methods as well as their separate advantages.

Output tracking control for state-dependent switched systems with input delay

This paper investigates the H∞ output tracking control problem for state-dependent switched systems with delays, where the existence of input delay is fully considered. Based on hysteresis switching strategy, a state-dependent switching law is designed through dividing the whole state space. In contrast to general time-dependent switching, the switching instants need not be predetermined and all subsystems can be unstable under state-dependent switching. Meanwhile, we design a delayed state-feedback controller that takes into account the effect of input delay. In the framework of Lyapunov–Krasovskii (L-K) functional method, some sufficient conditions for ensuring the H∞ output tracking performance of the switched system with delays are obtained by employing state-dependent switching and delayed state-feedback control. Moreover, the information of input delay and state delay are both included in linear matrix inequalities (LMIs). In addition, the obtained results are further extended to the system with parameter uncertainties by establishing the corresponding state-dependent switching law and some sufficient conditions. The effectiveness of the proposed results is demonstrated by two examples and their simulations.

Active input design for simultaneous fault estimation and fault-tolerant control of LPV systems

This paper considers the problem of integrating fault estimation (FE) and fault-tolerant control (FTC) for linear parameter varying (LPV) systems with inexact scheduling variables from a new perspective. Unlike the conventional passive FE approaches that maintain the performance of FE by only adjusting parametric matrices, the proposed method guarantees the precision and optimality of FE by actively designing optimal inputs at each time instant under the set-theoretic framework, which is named as active fault estimation (AFE). Meanwhile, by constructing a quadratic Lyapunov function, the stability of augmented estimation-error dynamics can be guaranteed based on H∞ setting by solving a group of matrix inequalities. In addition, the optimal inputs are also designed to implement the objective of integrated AFE and FTC for LPV systems, which leads to a balance between the goal of AFE and the reference output tracking performance. Although the original optimization problem computing the optimal inputs is not convex, it is proved that this problem can be equivalently transformed to a family of convex quadratic programming problems with linear constraints. At the end, a physical vehicle model is used to verify the effectiveness of our proposed integrated AFE and FTC method.

Memory‐event‐trigger‐based output tracking control for networked switched interval type‐2 fuzzy systems against deception attacks

AbstractThis paper investigates the problem of event‐triggered output tracking control for networked switched interval type‐2 (IT2) fuzzy systems under deception attacks. Different from the existing event‐triggered method, a new type of event‐triggered scheme which called the memory event‐triggered scheme (METS) is proposed to save the network resources, under which some past released triggering events are used to generate the new events. Also, the network communication may be corrupted by deception attacks and the tracking control is meaningful. We give some conditions to guarantee the output tracking performance of the closed loop memory event‐triggered networked switched IT2 fuzzy systems under deception attacks in secure sense. Finally, a simulation example validates the proposed technique.

Networked Tracking Control Based on Internal Model Principle and Adaptive Event-Triggering Mechanism

In this paper, the non-static error tracking control issue for the networked control system is considered. First, the unstable modes of the external disturbance signal and the output signal of the reference system are established to form a new series system with the controlled objects. Then, an internal model compensation controller is presented to achieve non-static tracking error. Furthermore, an adaptive event-triggering mechanism is introduced as a communication method to improve network resource utilization. By formulating the tracking control problem of the networked control system as the stabilization problem of a time-varying time-delay system, a sufficient condition such that the closed-loop system is asymptotically stable and satisfies the H∞ output tracking performance is derived. Finally, the simulation examples illustrate the feasibility and effectiveness of the proposed approach.

Composite Adaptive Synchronous Control of Dual-Drive Gantry Stage With Load Movement

In gantry systems, load usually moves according to different production tasks. The continuous change of the beam centroid position is a non-negligible factor for achieving high-performance synchronous control. Therefore, it cannot be simply treated as an external disturbance. In addition, advanced controller design and model parameter identification are very important for precision motion control systems. This article addresses the synchronous control of dual-drive gantry platforms with load movement to improve their steady-state and transient performance. Based on the analysis of the rigid and flexible characteristics of gantry mechanical components, a rigid-flexible coupling dynamic model is proposed, which provides a theoretical basis for the control system design. To ensure zero steady-state tracking error and parameter convergence in the presence of parameter uncertainties and unknown disturbances, a composite adaptive control method integrating excellent output tracking performance and parameter estimation is presented. Furthermore, the bridge between load movement and centroid position is established through frequency domain identification, which facilitates reasonable thrust distribution. Comparative experimental results are presented that confirm the effectiveness and advantages of the proposed synchronous control scheme.

Covert Attack Detection and Secure Control for Cyber Physical Systems

This paper addresses the problem of monitoring and secure control for cyber physical systems subject to cyber covert attacks. In this scenario, the attacker can manipulate sensor measurements and control actions while bypassing the classical monitoring schemes. Attack detection is investigated by inserting judicious external signals into the transmission network. An output feedback secure controller based on adaptive observer is then designed to ensure an output tracking performance level. The adaptive observer is used to suppress (not estimate) the attack signals, hence the control effect does not depend on the estimation precision. Moreover, there is no assumption on the boundedness of the attack signals. Finally, numerical simulations are provided to show the effectiveness of the proposed scheme.

Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control for Nonlinear Systems With Full-State Constraints and Application to a Single-Link Robot.

This study proposes the time-/event-triggered adaptive neural control strategies for the asymptotic tracking problem of a class of uncertain nonlinear systems with full-state constraints. First, we design a time-triggered strategy. The effect caused by the residuals of the estimation via radial basis function (RBF) neural networks (NNs), and the reasonable upper bounds on the first derivative of the reference signal and the derivative of each virtual control, can be eliminated by designing appropriate adaptive laws and utilizing the basic properties of RBF NNs. Moreover, the construction of the barrier Lyapunov functions (BLFs) in this work ensures the compliance of the full-state constraints and also holds the asymptotic output tracking performance. Then, based on the time-triggered strategy, we further design a relative threshold event-triggered strategy. The proposed event-triggered adaptive neural controller can solve the main control objective of this work, that is: 1) the full-state constraint requirements of the system are not violated and 2) the output signal asymptotically tracks the reference signal. Compared with the traditional method, the event-triggered strategy can improve the utilization of communication channels and resources and has greater practical significance. Finally, an example of single-link robot under the proposed two strategies illustrates the validity of the constructed controllers.

Robust adaptive precision motion control of tank horizontal stabilizer based on unknown actuator backlash compensation

Backlash nonlinearity inevitably exists in the actuator of tank horizontal stabilizer and has adverse effect on the system control performance, however, how to effectively eliminate its effect remains a pending issue. To solve this problem, a robust adaptive precision motion controller is presented in this paper to address uncertainties and unknown actuator backlash of tank horizontal actuator. The controller handles the modeling uncertainties including parameter uncertainties and unmodeled disturbances by integrating adaptive feedforward compensation and continuous nonlinear robust law. Based on the backstepping method, a smooth backlash inverse model is constructed by combining the adaptive idea. Meanwhile, the unknown backlash parameters of the system can be approximated through the parameter adaptation, and the impact of the actuator backlash nonlinearity is effectively compensated via the inverse operation, which can availably improve the tracking performance. Moreover, the adaptive law can update the disturbance ranges of tank horizontal stabilizer online in real time, which enhances the feasibility in practical engineering applications. Furthermore, the stability analysis based on Lyapunov function shows that with the existence of unmodeled disturbances and unknown actuator backlash, the designed controller guarantees excellent asymptotic output tracking performance. Extensive comparative results verify the effectiveness of the proposed control strategy.

Output Tracking Control Performance of Discrete Networked Systems Over Erasure Channel With Model Uncertainty

For networked control systems, it is known that various communication parameters in the channel will pose some fundamental limitations on output tracking control (OTC) performance. In this study, we mainly discuss the limitations resulting from model uncertainties, involving channel and plant. Through using the bivariate stochastic process to model packet loss, and the assumption that channel noise is additive white Gaussian noise (AWGN), two explicit expressions of output tracking performance limitations are derived with the single-degree-of-freedom (SDOF) and two-degree-of-freedom (TDOF) control structure, which shows that the performance of OTC is closely related to the inherent characteristics of the plant, as well as the packet loss rate and power spectral density (PSD) of AWGN. Finally, by considering an illustrative example, the simulation results are verified and analyzed to ensure the effectiveness of treatment methods and results.

Asymptotic output tracking control with prescribed transient performance of nonlinear systems in the presence of unknown dynamics

AbstractIn this article, we address the problem of asymptotic output tracking control with prescribed transient performance for a class of nonlinear systems in the presence of unknown dynamics. By fusing the funnel control approach and the tool of barrier Lyapunov function, a smooth adaptive tracking control strategy is developed in a recursive manner. In the proposed design, a new barrier Lyapunov function is adopted to eliminate the effects of unknown nonlinear dynamics, ensuring the prescribed transient behavior of the output tracking. The distinctive characteristic of the proposed method is that the designed control algorithm can guarantee not only prescribed output tracking performance but also global asymptotic stability without incorporating any prior knowledge of nonlinear dynamics or even corresponding bounding functions. Three illustrative examples are provided to testify the effectiveness of the proposed method.

Expansive Errors-Based Fuzzy Adaptive Prescribed Performance Control by Residual Approximation

This article is concerned with an expansive errors (EE)-based fuzzy adaptive prescribed performance control of a class of multiple-input and multiple-output nonlinear systems in the presence of unknown interconnection nonlinearities using fuzzy residual approximation technique. Based on an newly defined expansive error, residual nonlinearities’ fuzzy approximation scheme is proposed. The merits of designed control can be presented as twofold: 1) by incorporating an EE-based technique into pioneering prescribed performance control methodology, the defined intermediate and output tracking errors are kept within the regions represented by preassigned functions and control parameters simultaneously and 2) expansive errors-related residual nonlinearity is approximated by fuzzy logic systems, which further ameliorates the output tracking performance by compensating the affects raised by composite inner and interconnection nonlinearities among subsystems. With the designed expansive errors-based fuzzy adaptive prescribed performance control, all the closed-loop signals are kept bounded in the sense of Lyapunov stability theorem. Comparative simulation studies are presented to verify the effectiveness and advantages of theoretical findings.

Fractional order robust adaptive control design for actuator failure compensation in dual ball-beam system

This paper is to proposes a fractional order adaptive control scheme design for actuator failure compensation in order to balance of a ball-beam system with redundant actuators. The proposed controller is based on a fractional order sliding surface from which an adaptive actuator failure compensation control scheme is obtained. In order to control the dual ball-beam system with redundant motors, a linearized model is derived, which is subjected to actuator failures. A set of numerical simulation tests representing few selected types of actuator failures scenarios are carried out to assess the output tracking performance of the proposed controller design when dealing with such failures