Adaptive Output Feedback Tracking Research Articles

Global adaptive output-feedback tracking with prescribed performance for uncertain nonlinear systems

Global adaptive output-feedback tracking with prescribed performance for uncertain nonlinear systems

Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate

Article Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate Manman Yuan 1,2,*, and Wei Qian 1,2,* 1 School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China 2 Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China * Correspondence: ymmamhappy@163.com; qwei@hpu.edu.cn Received: 4 September 2023 Accepted: 7 November 2023 Published: 26 March 2024 Abstract: In this paper, the problem of adaptive output feedback tracking is considered for a class of nonlinear systems with lower-triangular structures. A novel dynamic gain is introduced to deal with the unknown growth rate. By coupling the dynamic gain with the observer and the controller, an adaptive output tracking controller is developed, which can guarantee that all signals of the closed-loop system are globally bounded. Finally, the effectiveness of the presented control scheme is illustrated by a numerical example.

Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate

Article Adaptive Output Feedback Tracking Control for Nonlinear Systems with Unknown Growth Rate Manman Yuan 1,2,*, and Wei Qian 1,2,* 1 School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China 2 Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China * Correspondence: ymmamhappy@163.com; qwei@hpu.edu.cn Received: 4 September 2023 Accepted: 7 November 2023 Abstract: In this paper, the problem of adaptive output feedback tracking is considered for a class of nonlinear systems with lower-triangular structures. A novel dynamic gain is introduced to deal with the unknown growth rate. By coupling the dynamic gain with the observer and the controller, an adaptive output tracking controller is developed, which can guarantee that all signals of the closed-loop system are globally bounded. Finally, the effectiveness of the presented control scheme is illustrated by a numerical example.

Prescribed-time adaptive output feedback tracking control for nonlinear systems with quantized input

This study concentrates on the prescribed-time output feedback tracking problem for nonlinear systems with unknown control gains and quantized input. A modified observer with the first-order scale function handles the unmeasurable problem of states while achieving accurate observation in the prescribed time. A simple controller is constructed by a new function transformation in which the computation burden is greatly reduced. The resulting controller not only drives that tracking error converges to zero but also ensures that all signals in the closed-loop system are bounded. Finally, the effectiveness of the control scheme is demonstrated by a numerical simulation and a practical simulation.

Adaptive neural network prescribed performance control for dual switching nonlinear time-delay system

This paper investigates the adaptive neural network prescribed performance control problem for a class of dual switching nonlinear systems with time-delay. By using the approximation of neural networks (NNs), an adaptive controller is designed to achieve tracking performance. Another research point of this paper is tracking performance constraints which can solve the performance degradation in practical systems. Therefore, an adaptive NNs output feedback tracking scheme is studied by combining the prescribed performance control (PPC) and backstepping method. With the designed controller and the switching rule, all signals of the closed-loop system are bounded, and the tracking performance satisfies the prescribed performance.

Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.

Output Feedback Tracking Control for a Class of Nonlinear Systems With Sensor Uncertainties

In this article, we consider adaptive output feedback tracking for a class of nonlinear systems with uncertain parameters, multiplicative and additive sensor uncertainties, and external disturbances. A state observer and an adaptive controller are constructed based on gain scaling technique by which a dynamic gain is designed to compensate for the effects of the sensor uncertainties. The adaptive controller is linear-like and, therefore, is simple and easy to implement. It is shown that with this proposed scheme, all states of the closed-loop system are bounded and the real tracking error can converge to an arbitrarily small residual set. Simulation and experiment are presented to illustrate the effectiveness of the proposed scheme.

Hybrid Adaptive Output Feedback Tracking for Stable Systems With Unknown Input Constraints

The problem of global output tracking is addressed for stable minimum phase systems, with constant uncertain parameters and constant disturbances belonging to a known compact set, in the presence of input constraints such as amplitude saturation. The relative degree, the system order, and the sign of the high frequency gain are assumed to be known. A hybrid adaptive output feedback control is designed to achieve global asymptotic output tracking, provided that there exists an input within the input constraints capable of tracking exactly the output reference signal. By virtue of the hybrid control approach, the closed-loop system is linear within each time interval between parameter updating. Moreover, no information on input saturation is required by the controller. In addition to the standard assumptions in adaptive control design when inputs are unconstrained, further requirements on the reference signals, on system stability and on parameter bounds, are needed when inputs are constrained.

Adaptive output-feedback tracking for nonlinear systems with unknown control direction and generic inverse dynamics

Adaptive output-feedback tracking for nonlinear systems with unknown control direction and generic inverse dynamics

Observer-based Fixed-time Fuzzy Adaptive Output Feedback Tracking Control for a Class of Nonlinear Systems

Observer-based Fixed-time Fuzzy Adaptive Output Feedback Tracking Control for a Class of Nonlinear Systems

Nonlinear observer-based adaptive output feedback tracking control of underactuated ships with input saturation

Nonlinear observer-based adaptive output feedback tracking control of underactuated ships with input saturation

Adaptive output feedback tracking for time-delay nonlinear systems with unknown control coefficient and application to chemical reactors

In this paper, the problem of global adaptive output feedback tracking is considered for a class of highly nonlinear time-delay systems with unknown control coefficient and relaxed lower-triangular growth constraints. Based on the non-separation principle, a non-identifier-based adaptive output feedback controller is proposed by designing a novel update law of the dynamic gain in observer and controller. The novel gain can simultaneously compensate the uncertain parameters, the output-polynomial growth rate and the unknown time-varying delay. The proposed controller is well universal attributed to using the non-identification adaptive mechanism, and all design parameters are easy to get by simple calculation. With the help of Lyapunov–Krasovskii functionals and Barbalat’s lemma, it is shown that the solutions of the resulting closed-loop system are globally uniformly ultimately bounded by improving the backstepping design and dynamic high-gain scaling approach; the adaptive tracking is achieved under any small pre-given tracking error. Finally, the effectiveness of the proposed controller is illustrated by two examples.

Observer-Based Adaptive Neural Output Feedback Constraint Controller Design for Switched Systems Under Average Dwell Time

Aiming at a class of switched uncertain nonlinear strict-feedback systems under the action of average dwell time switching signal, this paper proposes a novel adaptive neural network output feedback tracking control based on the consideration of the full state constraints. The controller is proposed based on neural networks. One of the key characteristics of the system discussed is that the state variables cannot be measured and the system states need to be kept within the constraint ranges. For the sake of estimating the unmeasured states, the observer is constructed. In order to ensure all states which are within the time-varying boundary, the tangent barrier Lyapunov function (BLF-Tan) is selected in the design process. The boundedness of the closed-loop signals with average dwell time is guaranteed by the designed controllers and all the states limit in their constrained sets. It has been proved that the output tracking error converge to a small neighborhood of zero. In addition, the significance of the presented control strategy is verified and tested by a simulation example.

Fuzzy adaptive output-feedback tracking control for nonlinear strict-feedback systems in prescribed finite time

The current paper addresses the fuzzy adaptive tracking control via output feedback for single-input single-output (SISO) nonlinear systems in strict-feedback form. Under the situation of system states being unavailable, the system output is used to set up the state observer to estimate the real system states. Furthermore, the estimation states are employed to design controller. During the control design process, fuzzy logic systems (FLSs) are used to model the unknown nonlinearities. A novel observer-based finite-time tracking control scheme is proposed via fuzzy adaptive backstepping and barrier Lyapunov function approach. The suggested fuzzy adaptive output feedback controller can force the output tracking error to meet the pre-specified accuracy in a fixed time. Meanwhile, all the closed-loop variables are bounded. Compared to some existing finite-time output feedback control schemes, the developed control strategy guarantees that the settling time and the error accuracy are independent of the uncertainties and can be specified by the designer. At last, the effectiveness and feasibility of the proposed control scheme are demonstrated by two simulation examples.

Neural adaptive output feedback tracking control of underactuated AUVs

This paper addresses the three-dimensional tracking and stabilization problems simultaneously for underactuated autonomous underwater vehicles (AUVs) subjected to parametric uncertainties, external disturbances, unmeasured states and input saturation. A modified error state formulation is introduced to produce auxiliary virtual controls for the underactuated kinematics. A new high-gain observer is proposed to estimate unavailable velocities which doesn't need the position and orientation of AUVs to be bounded. Based on the backstepping technique and combinations of high-gain observer and radial-basis-function neural network (RBFNN) approximations, the kinematic and dynamic controllers are designed to force underactuated AUVs to track a time-varying trajectory or/and a stationary point, and guarantees the tracking errors convergent to a bounded neighborhood of the origin. Some comparative simulations are presented to validate the effectiveness and performance of the proposed control scheme.

Adaptive output feedback tracking for a class of nonlinear systems with quantized input and output

AbstractIn this paper, an adaptive output feedback tracking for a class of uncertain nonlinear systems with input and output quantization is investigated. A new controller structure is introduced in which a modified output quantizer is proposed so that its first‐order derivative exists, and an observer is designed with quantized input and output as its input signals. Then, with dynamic surface control as a tool, an adaptive recursive design is developed which can avoid higher‐order derivatives of the quantized output signal. It is proved that, with the proposed scheme, coarser input and output quantization can be achieved and the tracking error can converge to a small residual set by appropriately choosing some design parameters.

Adaptive prescribed performance neural network control for switched stochastic pure-feedback systems with unknown hysteresis

This paper investigates the problem of neural network (NN) prescribed performance tracking control for a class of switched stochastic nonlinear pure-feedback systems which contain unknown nonlinear functions, unmeasured state variables, and unknown hysteresis input. It provides an adaptive NN controller which is not restricted to a particular type of hysteresis input. A general mathematical model is introduced to describe two kinds of hysteresis nonlinearities and to utilize in the control design producer. Other focus of this paper is on the performance constraint problem to avoid performance degradation and system damage in practical control systems. Prescribed performance control (PPC) and backstepping technique are thus synthesized to develop an adaptive NN output feedback tracking control scheme under deterministic switching signal. Regarding this concern, to cope with the cause of non-differentiable difficulties and complex deductions in traditional PPC, a new asymmetry error transformation is employed. Moreover, radial basis function NNs (RBFNNs) are applied to approximate the unknown nonlinear functions and to construct a NN nonlinear observer to estimate the immeasurable state variables. Based on Lyapunov stability theory, it is demonstrated that the proposed controller can guarantee that 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 with the prescribed performance bounds. Finally, two simulation examples are provided to confirm the advantages of the presented control design approach.

Adaptive output‐feedback quadratic tracking control of continuous‐time systems via value iteration with its application

This study is dedicated to develop an adaptive output-feedback (OPFB) tracking control scheme for continuous-time linear systems to achieve the infinite-horizon linear quadratic tracking (LQT) solution. The existence conditions of the OPFB control for LQT solution are proposed and an upper bound is found for the discount factor to assure the stability of the OPFB solution. To develop an online learning solution without knowing the system drift dynamics, a novel value iteration (VI) algorithm is presented based on the integral reinforcement learning technique which requires measured augmented system states. Moreover, a convergence analysis is proposed for the VI algorithm. Compared to the policy iteration method, the VI algorithm relaxes the initial stabilising control policy requirement. Specifically, in order to further obviate the knowledge of system states, a neural network-based adaptive observer is used during learning and it is no longer needed after the online learning algorithm converges. Effectiveness of the proposed learning scheme is illustrated through an interesting application into a single-phase grid-connected PV power inverter.

Adaptive Neural Output Feedback Tracking Control of Underactuated Ships Against Uncertainties in Kinematics and System Matrices

Adaptive neural output feedback tracking control is developed for underactuated ships with uncertainties in kinematics (i.e., kinematic uncertainties) and uncertain off-diagonal terms in the matrices of their system dynamics (i.e., dynamic uncertainties), where radial basis function networks are incorporated. As the kinematic uncertainties unavoidably exist in the off-diagonal terms of system matrices and only the posture information (position variables and yaw angle) of the ship is available, the velocities should be observed and the kinematic and dynamic uncertainties should be simultaneously compensated to achieve the satisfactory ship tracking control performance. Therefore, the kinematic and dynamic uncertainties of the underactuated ship are rigorously derived. The kinematic equations with uncertainties are then derived such that the proposed nonlinear velocity observer can be used to estimate the combined velocities and kinematic uncertainties using the posture information irrespective of the ship dynamics. The dynamic uncertainties are also derived while taking into consideration the uncertainties in the system matrices. An adaptive neural uncertainty observer is then proposed to estimate the dynamic uncertainties. In this way, the proposed adaptive neural output feedback control law can compensate for the uncertainties in both the kinematics and dynamics. The estimation errors in the combined velocities and kinematic uncertainties and the dynamic uncertainties are rigorously considered in the proposed controller design. A stability analysis and the simulation results of the ship tracking control system are provided to demonstrate the validity of the proposed method.

Adaptive output-feedback tracking control for a class of nonlinear systems with input saturation: a multi-dimensional Taylor network-based approach

For the nonlinear systems with input saturation constraint, a novel MTN-based output-feedback control strategy is developed via backstepping in this paper. Firstly, an MTN-based nonlinear state observer is designed to estimate the unmeasured states. Secondly, multi-dimensional Taylor networks (MTNs) are used to deal with the unknown nonlinear functions, based on this, the procedure of the adaptive MTN output-feedback controller design is developed by combining backstepping approach and dynamic surface control technique, and then the stability of the closed-loop system is proved based on the principle of Lyapunov stability theory. Finally, the theoretical results of this paper are demonstrated by two examples.