Output Feedback Tracking Control Research Articles

Adaptive Decentralized Output Feedback Tracking Control for Large-Scale Interconnected Systems with Time-Varying Output Constraints

This paper investigates a novel adaptive output feedback decentralized control scheme for nonstrict feedback large-scale interconnected systems with time-varying constraints. A decentralized linear state observer is designed to estimate the unmeasurable states of subsystems. Time-varying barrier Lyapunov functions are designed to ensure outputs are not violating constraints. A variable separation approach is applied to deal with the nonstrict feedback problem. Moreover, dynamic surface control and minimal parameter learning technologies are adopted to reduce the computation burden, and there are only two parameters for every subsystem to be updated online. The proof of stability is obtained by the Lyapunov method. Finally, simulation results are given to show the effectiveness of the proposed control scheme.

Global adaptive output feedback tracking for a class of nonlinear systems with quantized input

In this paper, we consider the quantized control of a class of nonlinear systems whose nonlinear functions satisfy more general growth conditions. Based on a reduced-order observer and a dynamic gain, an adaptive output feedback tracking controller is designed. It is proved that by suitably choosing design parameters and functions, the tracking error can converge to an arbitrarily small residual set while all the other signals of the closed-loop system are bounded.

Adaptive Output Feedback Tracking Control for a Class of Nonlinear Time-Varying State Constrained Systems With Fuzzy Dead-Zone Input

This article proposes an adaptive fuzzy controller for a class of uncertain strict-feedback nonmatching nonlinear single-input single-output systems with fuzzy dead zone and full time-varying state constraints. The states considered here are immeasurable and full states of the systems are constrained in a bounded set with time-varying regions. Following the adaptive backstepping design framework, the tangent barrier Lyapunov functions are introduced to the integrated design to address the problems in such systems. Fuzzy logic systems are used to identify the unknown smooth functions and unknown parameters. An input-driven observer is designed to estimate the immeasurable states. To distinguish the conventional deterministic dead zone models, the output of dead zone is uncertainty. The form of indeterminate dead zone as a combination of a liner and a disturbance-like term is extended by the fuzzy algorithms. Even though the output of dead zone is fuzzy and adopting the integrated design, the proposed fuzzy controller can ensure that all the signals in the closed-loop systems are semiglobal uniformly ultimately bounded and guarantee the tracking performance. Finally, simulation results are shown to verify the effectiveness and reliability of the proposed approach.

Output feedback tracking control for lower‐triangular nonlinear time‐delay systems with asymmetric input saturation

SummaryIn this paper, the problem of output feedback tracking control is investigated for lower‐triangular nonlinear time‐delay systems in the presence of asymmetric input saturation. A novel design program based on a dynamic high gain design approach is proposed to construct an output feedback tracking controller. The innovation here is that the problem of constructing tracking controller can be transformed into the problem of constructing two dynamic equations, with one being utilized to deal with the nonlinear terms and the other one being applied to analyze the influence of asymmetric input saturation. It is proved by an appropriate Lyapunov‐Krasovskii functional that the proposed tracking controller subject to saturation can ensure that all the signals of the closed‐loop system are globally bounded and the tracking error is prescribed sufficiently small when time is long enough. A practical example is given to illustrate the effectiveness of the proposed method.

Adaptive Fuzzy Tracking Control of Autonomous Underwater Vehicles With Output Constraints

In this article, the trajectory tracking control is considered for an autonomous underwater vehicle (AUV) subjected to model uncertainties and output constraints. The tan-type barrier Lyapunov function (BLF) is adopted to deal with the requirement of output constraints. In order to cope with the existing system uncertainties, the adaptive fuzzy is employed to approximate the unknown system parameters of an AUV. Both state feedback tracking controller and output feedback tracking controller are proposed in this article. The state feedback tracking control scheme is designed by comprehensive application of tan-type BLF and adaptive control design provided that all system states are measurable. The output feedback tracking control scheme is proposed by employing a high-gain observer. Through analysis, it can be shown that the designed control laws are able to achieve the desired output constraints. In addition, all the signals of the resulting closed-loop system are semiglobally uniformly bounded. Finally, simulation is carried out to demonstrate the validity and feasibility of the designed control strategies.

Adaptive Fuzzy Output Feedback Simultaneous Posture Stabilization and Tracking Control of Wheeled Mobile Robots With Kinematic and Dynamic Disturbances

This article proposes a simultaneous posture (i.e., position and heading direction angle) stabilization and tracking control for differential-drive wheeled mobile robots (WMRs) using an adaptive fuzzy output feedback tracking control (AFOFTC) method and a fuzzy basis function network (FBFN). Existing studies on simultaneous posture stabilization and tracking control of WMRs do not compensate for the uncertainties and disturbances caused by kinematics and dynamics (i.e., kinematic and dynamic disturbances). To inhibit the performance degradation caused by these disturbances, an FBFN-based adaptive fuzzy output feedback posture stabilization and tracking control of WMRs is proposed considering the unavailable velocities of the WMR. In this approach, i) kinematic error equations are derived in polar coordinates, ii) a nonlinear velocity observer for WMRs containing the kinematic and dynamic uncertainties is designed, iii) a backstepping-like feedback linearization controller is designed based on the kinematics in polar coordinates to generate a reference trajectory, and iv) an AFOFTC law is proposed based on the dynamics to ensure that the WMR follows the generated reference trajectory. The proposed method can maintain satisfactory posture stabilization and tracking control performance despite the kinematic and dynamic disturbances. A stability analysis as well as the simulation and experimental results are provided to validate the proposed scheme.

Anti-Disturbance Output Feedback Tracking Control for Switched Stochastic Systems With Multiple Disturbances via Mode-Dependent Average Time Method

The anti-disturbance output feedback tracking control problem is addressed for switched stochastic systems with multiple disturbances in this article. To deal with unknown disturbances and unmeasurable states, composite disturbance observer and state observer are designed simultaneously. Then, an output feedback tracking controller is designed based on outputs of two observers. By employing multiple Lyapunov function method, some sufficient conditions are given to analyse the mean square exponential stability with weighted H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance for the closed-loop system. Furthermore, the controller and observer gains are solved by linear matrix inequalities. At last, the validity of the presented scheme is demonstrated via two examples.

Reinforcement Q-Learning Incorporated With Internal Model Method for Output Feedback Tracking Control of Unknown Linear Systems

This paper investigates the output feedback (OPFB) tracking control problem for discrete-time linear (DTL) systems with unknown dynamics. With the approach of augmented system, the tracking control problem is first turned into a regulation problem with a discounted performance function, the solution of which relies on the Q-function based Bellman equation. Then, a novel value iteration (VI) scheme based on reinforcement Q-learning mechanism is proposed for solving the Q-function Bellman equation without knowing the system dynamics. Moreover, the convergence of the VI based Q-learning is proved by indicating that it converges to the Q-function Bellman equation and it brings out no bias of solution even under the probing noise satisfying the persistent excitation (PE) condition. As a result, the OPFB tracking controller can be learned online by using the past input, output, and reference trajectory data of the augmented system. The proposed scheme removes the requirement of initial admissible policy in the policy iteration (PI) method. Finally, effectiveness of the proposed scheme is demonstrated through a simulation example.

Observer-Based Adaptive Finite-Time Tracking Control for a Class of Switched Nonlinear Systems With Unmodeled Dynamics

This paper investigates the adaptive finite-time tracking control problem for a class of switched nonlinear systems with unmodeled dynamics. In practical applications, switched systems usually possess unfavourable factors, such as unmeasured states and unmodeled dynamics both of which are taken into account in this paper. A dynamic signal defined with a special property is introduced in this paper to improve control performance while garanteeing stability of the controlled system. By designing an observer, a finite-time adaptive output-feedback tracking controller is constructed via the backstepping technique. Then, the finite-time stability problem of the considered systems is studied. It is shown that all the signals in the closed-loop system are semi-globally uniformly finite-time bounded (SGFUB), and the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing appropriate parameters. It is noted that, the design process is less complex than some existing results on tackling control problems of nonlinear systems with unmodeled dynamics. In the example, the simulation result testifies the effectiveness of the proposed method.

Multi-Dimensional Taylor Network-Based Adaptive Output-Feedback Tracking Control for a Class of Nonlinear Systems

In this paper, the output feedback adaptive multi-dimensional Taylor network (MTN) tracking control for a class of nonlinear systems with unmeasurable states is investigated. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states, and then an adaptive MTN-based output-feedback control approach is developed via backstepping technique. Secondly, in view of the simple structure of MTN, the controller based on MTN has the advantages of simple structure and fast calculation speed. Thirdly, in order to avoid the “differential explosion” problem inherited from the backstepping design, dynamic surface control (DSC) technique is introduced in the process of controller design. The results demonstrate that this scheme guarantees the stability and tracking performance of the closed-loop system. Finally, simulation examples are given to reveal the viability of the proposed method.

Decentralized static output tracking control of interconnected and disturbed Takagi–Sugeno systems

Abstract This article describes a new procedure for the design of decentralized output-feedback tracking controllers for a class of interconnected Takagi–Sugeno (TS) fuzzy systems with external bou...

A robust neural network approximation-based prescribed performance output-feedback controller for autonomous underwater vehicles with actuators saturation

A robust neural network approximation-based output-feedback tracking controller is proposed for autonomous underwater vehicles (AUVs) in six degrees-of-freedom in this paper. The prescribed performance technique is employed to obtain some pre-defined maximum overshoot/undershoot, convergence speed and ultimate tracking accuracy for the tracking errors. A high-gain observer is used to approximate unavailable velocity vector which is crucial to design the output-feedback controller. A robust multi-layer neural network and adaptive robust techniques are combined to simultaneously compensate for the unmodeled dynamics, system nonlinearities, exogenous kinematic and dynamic disturbances, and reduce the risk of the actuator saturation. Then, the uniform ultimate boundedness stability of the closed-loop control system is proved via a Lyapunov-based stability synthesis. It is demonstrated that the posture tracking errors converge to a vicinity of the origin with a guaranteed prescribed performance during the tracking mission without velocity measurements. Finally, simulation results with a comparative study verify the theoretical findings.

Observer-Based Adaptive Finite-Time Quantized Tracking Control of Nonstrict-Feedback Nonlinear Systems With Asymmetric Actuator Saturation

This paper investigates the output feedback adaptive finite-time quantized tracking problem for a class of uncertain nonstrict-feedback nonlinear systems with asymmetric actuator saturation, where signals including the measurable output and the control input are quantized by the sector-bounded quantizers. In order to overcome the technical difficulties arising from asymmetric actuator saturation and sector-bounded quantization errors, the combination of some novel techniques, the Gaussian error function-based smooth model, and fuzzy logic system approximation-based method are utilized. Moreover, an observer involving the quantized output signal and the saturated input signal is designed to reconstruct the unknown states. Then an adaptive output feedback tracking control strategy is developed for this class of systems, so that the system output follows satisfactorily a desired reference signal in finite time while all signals in the closed-loop systems are semi-global practical finite-time stable. Finally, two illustrative simulation examples are provided to validate the effectiveness and applicability of the proposed design.

GPIO‐based robust control of magnetic levitation system subject external disturbance

This article considers the robust control problem of non-linear magnetic levitation system with parameter uncertainties and external disturbance. To obtain a simple and practical controller that does not depend on the exact model information, a generalised-proportional-integral observer is constructed to estimate the velocity and disturbance. Then, based on mild assumptions and the estimated values, an output feedback tracking controller is proposed such that the reference position tracking task could be realised. The obtained control approach is confirmed strictly by theoretical analysis. Finally, simulation results are provided to show the effectiveness of the proposed control method.

Adaptive fuzzy output-feedback fault-tolerant tracking control of a class of uncertain nonlinear switched systems

ABSTRACT This paper considers the output-feedback fault-tolerant tracking control problem for a class of uncertain nonlinear switched systems with nonlinear faults and strict-feedback form, where the faults which are nonaffine occur on the actuator. As a kind of specialised function approximating tool, fuzzy logic systems (FLSs), are employed to approximate the unknown smooth nonlinear functions. A switched fuzzy observer is designed to address the problem of unmeasurable states, filtered signals are used to address algebraic loop problem and the average dwell time (ADT) method is further utilised to prove the stability of the resulting closed-loop systems under a type of slowly switching signals. Based on the backstepping recursive design technique and Lyapunov function method, an adaptive fuzzy output-feedback control scheme is developed. The developed control method can ensure all the signals are semi-globally uniformly ultimately bounded (SGUUB) and the system output tracks the reference signal tightly even if unknown fault occurs. A simulation carried on an example demonstrates the validity of the obtained control scheme.

Reduced-order GPIO based dynamic event-triggered tracking control of a networked one-DOF link manipulator without velocity measurement

In networked robot manipulators that deeply integrate control, communication and computation, the controller design needs to take into consideration the limited or costly system resources and the presence of disturbances / uncertainties. To cope with these requirements, this paper proposes a novel dynamic event-triggered robust tracking control method for a one-degree of freedom ( DOF ) link manipulator with external disturbance and system uncertainties via a reduced-order generalized proportional-integral observer ( GPIO ) . By only using the sampled-data position signal, a new sampled-data robust output feedback tracking controller is proposed based on a reduced-order GPIO to attenuate the undesirable influence of the external disturbance and the system uncertainties. To save the communication resources, we propose a discrete-time dynamic event-triggering mechanism ( DETM ) , where the estimates and the control signal are transmitted and computed only when the proposed discrete-time DETM is violated. It is shown that with the proposed control method, both tracking control properties and communication properties can be significantly improved. Finally, simulation results are shown to demonstrate the feasibility and efficacy of the proposed control approach.

Adaptive optimal output feedback tracking control for unknown discrete‐time linear systems using a combined reinforcement Q‐learning and internal model method

This study addresses the novel output feedback-based reinforcement Q-learning algorithms for optimal linear quadratic tracking problem of unknown discrete-time systems. An augmented system composed of the original controlled system and the reference trajectory dynamic is first constructed. Then, learning algorithms including on-policy and off-policy approaches are both developed to solve the optimal tracking control problem with unknown augmented system dynamics. In both the optimal tracking control policies, a two-stage framework is proposed composed of two controllers, where the internal model controller is used to collect some data for the next process, and then the output feedback Q-learning scheme is able to learn the optimal tracking controller online by using the past input, output, and reference trajectory data of the augmented system. Finally, simulation results are provided to verify the effectiveness of the proposed scheme.

Observer-based adaptive neural control for switched stochastic pure-feedback systems with input saturation

The purpose of this paper is to investigate the problem of adaptive neural network (NN) output-feedback tracking control for input saturated switched stochastic nonlinear systems in pure-feedback form with unmeasured states. In order to facilitate the controller design process, a dead zone-based model of saturation is implemented and radial basis function NNs (RBFNNs) are employed to approximate unknown nonlinear functions and to construct an NN switched nonlinear observer to cope with difficulties raised by the presence of immeasurable state variables. Based on the adaptive backstepping technique and Lyapunov function method, an adaptive NN output feedback control scheme is developed. Furthermore, it is proved that the proposed controller can provide that, under arbitrary deterministic switching, all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a small neighborhood of the origin. Finally, simulation examples are presented to validate the effectiveness of the proposed adaptive NN control approach.

Adaptive neural output feedback tracking control for a class of nonlinear systems

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.

Practical tracking control for stochastic nonlinear systems with polynomial function growth conditions

This paper mainly focuses on an output feedback practical tracking controller design for a class of stochastic nonlinear systems with polynomial function growth conditions. Mostly, there are some studies on an output feedback tracking control problem for general nonlinear systems with polynomial function growth conditions in existing achievements. Moreover, we extend it to stochastic nonlinear systems and construct an output feedback practical tracking controller based on dynamic and static phase combined, ensuring that all the states of the stochastic nonlinear system are bounded and the system tracking error can be made arbitrarily small after some large enough time. Finally, a simulation example is provided to illustrate the efficiency of the theoretical results.