Observer-Based Adaptive Output-Feedback Quantized Control for Switched Stochastic Nonlinear Time-Delay Systems

Based on the backstepping technique, an adaptive neural network (NN) based output feedback controller is proposed to achieve a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict-feedback form. The neural networks are utilized to approximate unknown functions in the systems. The adaptive output feedback controller needs only to adjust less adaptive parameters, therefore it is clear that the proposed approach can reduce on-line computation burden. It is proven that all the signals in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. A simulation example is used to verify the effectiveness of the proposed approach.

This paper is concerned with the problem of adaptive neural output-feedback control for a class of nonlinear strict-feedback systems. In this research, radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions and a state observer is constructed to estimate the immeasurable state variables. An adaptive neural output feedback control scheme is proposed via backstepping technique. The proposed design method can guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system, and ensures the observer errors converge to a small neighborhood. At last, a simulation example is included to show the effectiveness of the proposed method.

This paper is concerned with the problem of output feedback adaptive tracking control for a class of uncertain nonlinear time-delay systems (NTDS) with both uncertain output function and unknown control directions. By introducing a linear state transformation, the original system is transformed into a new system for which output feedback control design becomes feasible. Without the precise information on output function, an input-driven high-gain state observer is presented to estimate the unavailable states of the new system. Moreover, some radial basis function neural networks (RBFNNs) are employed to approximate the lumped unknown nonlinear functions during the procedure of control design. Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems. As a result, all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error can remain in a small neighbourhood of zero. Finally, two simulation examples are given to validate the effectiveness and applicability of the proposed design method.

This paper focuses on the problem of adaptive output feedback tracking control for a class of non-strict-feedback nonlinear systems with unknown control coefficients. By employing a linear state transformation, the original system is transformed to a new system for which control design becomes feasible. Then, after the introduction of a simple input-driven observer, an adaptive output feedback controller which contains only one parameter is developed for such systems by using backstepping technique and variable separation method. The designed controller ensures that all the signals in the closed-loop systems are Semi-globally Uniformly Ultimately Bounded (SGUUB).

Switched-observer-based adaptive DSC design of nonstrict-feedback switched stochastic nonlinear time-delay systems

This paper investigates adaptive output feedback control problem for switched uncertain nonlinear systems with input quantization, unmeasured system states and state constraints. Firstly, fuzzy logic systems are introduced to identify system uncertainties, then the fuzzy based observer is constructed to estimate unavailable states. Secondly, combing the backstepping technique and the barrier Lyapunov function method, an adaptive fuzzy output feedback control law is designed, which guarantees that all signals in the closed-loop system are bounded, the system output tracks the reference signal, and system states satisfy their corresponding constraint conditions. Finally, simulation results further show the good performances of the proposed control scheme.

This paper focuses on the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown control coefficients and quantized input. The difficulty from the unknown control direction is solved by using the linear state transformation and the Nussbaum gain function(NGF) approach. Based on the combination of input-driven observer, backstepping technique, neural network(NN) parametrization and variable separation method, a novel adaptive output feedback quantized control scheme involving only one adaptive parameter is developed for such systems. The designed quantized controller ensures that all signals of closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to an adjustable neighborhood of the origin. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control design.

In this paper, the adaptive output feedback control problem is investigated using multilayer neural networks (MNNs) for a class of general nonlinear systems. The adaptive output feedback controller is developed based on a high-gain observer which is used to estimate the time derivatives of the system output. The Lyapunov stability of the resulting closed-loop system is guaranteed and the tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed controller is illustrated through an example of composition control for a continuously stirred tank reactor (CSTR) system.

Distributed adaptive output-feedback tracking control of non-affine multi-agent systems with prescribed performance

This article studies the adaptive output feedback control problem of a class of uncertain nonlinear systems with unknown time delays. The systems considered are dominated by a triangular system without zero dynamics satisfying linear growth in the unmeasurable states. The novelty of this article is that a universal-type adaptive output feedback controller is presented to time-delay systems, which can globally regulate all the states of the uncertain systems without knowing the growth rate. An illustrative example is provided to show the applicability of the developed control strategy.

In this paper, an adaptive fuzzy robust output feedback control problem is considered for a class of single-input and single-output nonlinear systems in a strict-feedback form. The considered systems possess the unstructured uncertainties, unknown dead zone, and the dynamics uncertainties, and they do not assume the states being available for the controller design. In the controller design, fuzzy logic systems are first used to approximate the unstructured uncertainties, and by utilizing the information of the bounds of the dead-zone slopes and treating the time-varying inputs coefficients as a system uncertainty, a fuzzy state observer is designed to estimate the unmeasured states. By combining a backstepping technique with a nonlinear small-gain approach, a new adaptive fuzzy robust output feedback control has been developed. It is proved that the proposed fuzzy adaptive control approach can guarantee the semiglobal uniform ultimate boundedness for all the solutions of the closed-loop systems. Simulation studies and comparisons with previous methods are included to illustrate the effectiveness of the proposed approach.

In this paper, a new continuous output feedback control mechanism is developed for output tracking for a class of high-order multi-input nonlinear systems with an input gain matrix that is positive definite but non-symmetric. The controller yields semiglobal uniformly ultimately bounded (SGUUB) tracking while compensating for unstructured uncertainty in both the drift vector and the input matrix. First, a fullstate feedback controller is designed based on limited assumptions on the structure of the system nonlinearities and the controller is proven to yield SGUUB tracking through a Lyapunov-based analysis. Then, an output feedback control design based on a high gain observer is proposed. A comprehensive stability analysis of the closed-loop system under output feedback is carried out and a recovery of the state feedback SGUUB result is demonstrated for the output feedback control system. Neural network estimation method is employed in both state and output feedback control design to feedforward compensate for the nonlinear system uncertainty.

In this paper, a new 3‐D trajectory tracking problem for an uncertain high fidelity six‐degree‐of‐freedom (6‐DOF) aerodynamic system is considered. Instead of designing controllers for each subsystem separately, an integrated trajectory tracking control algorithm is proposed to exploit beneficial relationships among interacting subsystems. The high‐order aerodynamic model is first transformed into a quasi‐strict‐feedback form. Then, backstepping technique is utilized to resolve the coupling effect problem of three control channels resulting from the bank‐to‐turn (BTT) control mode. In addition, command filters are introduced to handle state and actuator constraints caused by the physical limitations and the coordinated turn requirement. Furthermore, the uncertain aerodynamic force and moment coefficients are reconstructed by using the B‐spline neural network approximation and adaptive learning approaches. With Lyapunov stability analysis, all the states in the closed‐loop system are shown to be semi‐globally uniformly ultimately bounded (SUUB), and the tracking errors will asymptotically converge into a small compact set around zero by properly adjusting the control parameters. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

This paper focuses on the adaptive output feedback control problem for nonlinear stochastic systems with unknown measurement sensitivity based on dynamic event-triggered mechanism (ETM). Different from the existing works, a novel adaptive output feedback control algorithm is proposed for unknown measurement sensitivity (its sign and bounds are unknown) by means of Nussbaum-type function. First, a reduce-order dynamic gain K-filter is proposed to reconstruct the unmeasurable state variable. Second, a tangent-type barrier Lyapunov function with a predefined-time performance function is established to constrain system output into the given region in a predefined time. Third, a dynamic ETM is put forward to reduce trigger times, and then the controller is designed accordingly. Based on the Lyapunov stability theory, it is proved that system state variables converge to zero in probability and other signals of the closed-loop system are bounded in probability. Finally, the validity of the proposed algorithm is demonstrated by the numerical simulation on a single-link manipulator.

Adaptive output control of uncertain nonlinear systems with non-symmetric dead-zone input