Adaptive output-feedback control design with prescribed performance for switched nonlinear systems

In this paper, a hybrid fuzzy adaptive output feedback control design approach is proposed for a class of multiinput and multioutput strict-feedback nonlinear systems with unknown time-varying delays, unmeasured states, and input saturation. First, fuzzy logic systems are employed to approximate unknown nonlinear functions in the system. Next, a smooth function is used to approximate the input saturation and an adaptive fuzzy state observer is constructed to solve the problem of unmeasured states. Based on the designed adaptive fuzzy state observer, a serial-parallel estimation model is established. By applying adaptive fuzzy dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws is developed based on Lyapunov-Krasovskii functional. It is proved that all variables of the closed-loop system are bounded and the system outputs can follow the given bounded reference signals as close as possible. A simulation example is provided to further show the effectiveness of this novel control scheme.

Data-based adaptive neural network optimal output feedback control for nonlinear systems with actuator saturation

The paper presents an approach to adaptive output feedback controller design for single-input-single-output (SISO) nonlinear systems based on the use of the singular perturbation technique. The presented approach guarantees desired output transient performance indices by inducing of two-time-scale motions in the closed-loop system. It has been shown that stability conditions imposed on the fast and slow modes and sufficiently large mode separation rate between fast and slow modes can ensure that the full-order closed-loop nonlinear system achieves the desired properties in such a way that the output transient performances are desired and insensitive to external disturbances and plant's parameter variations. The high-frequency-gain online identification and adaptive gain tuning are incorporated in the control system in order to maintain the two-time-scale structure in the closed-loop system trajectories and stability of fast-motion transients for a large range of plant's parameter variations. Numerical example and simulation results are presented.

This paper deals with a design problem of an adaptive output feedback tracking control for systems with a parallel feedforward compensator (PFC) introduced in order to realize the output feedback exponential passive (OFEP) augmented control system. It is well known that one can easily design an adaptive output feedback control system for nonlinear systems satisfying the OFEP conditions. We adopt introduceing a PFC to make a non-OFEP nonlinear system OFEP and propose an adaptive control system design scheme which improves the control performance of the practical control system without the PFC. The effectiveness of the proposed method will be confirmed through experiments of a magnetic levitation system.

This article presents a fuzzy finite-time command filtering output feedback control method for a class of nonlinear systems. A fast convergent output feedback control algorithm based on backstepping finite-time command filtering is developed. Fuzzy logic system is used to estimate uncertain functions in nonlinear systems. A fuzzy state observer is designed to measure the unknown state. The developed finite-time command filtering feedback control method overcomes well the computational complexity problem due to the calculation of the derivatives of virtual control signals. A compensation mechanism is also introduced to compensate for the error caused by the filter. The proposed method ensures not only that all signals in the closed-loop system are finite-time bound, but also that the tracking error converges to a small neighborhood around the origin. The effectiveness of the proposed method is demonstrated in the simulation results.

This paper considers augmentation of an existing inertial damping mechanism by neural network-based adaptive control, for controlling a micromanipulator that is serially attached to a macromanipulator. The approach is demonstrated using an experimental test bed in which the micromanipulator is mounted at the tip of a cantilevered beam that resembles a macromanipulator with its joint locked. The inertial damping control combines acceleration feedback with position control for the micromanipulator so as to simultaneously suppress vibrations caused by the flexible beam while achieving precise tip positioning. Neural network-based adaptive elements are employed to augment the inertial damping controller when the existing control system becomes deficient due to modeling errors and uncertain operating conditions. There were several design challenges that had to be faced from an adaptive control perspective. One challenge was the presence of a nonminimum phase zero in an output feedback adaptive control design setting in which the regulated output variable has zero relative degree. Other challenges included flexibility in the actuation devices, lack of control degrees of freedom, and high dimensionality of the system dynamics. In this paper we describe how we overcame these difficulties by modifying a previous augmenting adaptive approach to make it suitable for this application. Experimental results are provided to illustrate the effectiveness of the augmenting approach to adaptive output feedback control design.

It is well recognized that an adaptive output feedback control based on almost strictly positive real‐ness (ASPR) or output feedback strictly passivity (OFSP) property of the system has strong robustness with respect to disturbances and uncertainty. However, most practical systems do not have such properties. In order to realize an augmented ASPR system for designing adaptive output feedback controller, a kind of adaptive parallel feedforward compensator (PFC) design schemes have been proposed for linear systems. In this paper, we propose an adaptive PFC design scheme by Kernel method for nonlinear systems and provide output feedback passivity based adaptive output feedback control for nonlinear systems. The effectiveness of the proposed method will be confirmed through numerical simulations. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.

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This paper investigates the global output feedback sampled-data control of a class of nonlinear systems with mismatched disturbances. A linear state observer is constructed to estimate the states. A disturbance observer is designed to estimate the mismatched disturbances. The sampled-data controller is proposed with the estimated states and the estimated disturbances. The output feedback controller can ensure that the nonlinear system is globally stable. Finally, an example is given to show the effectiveness of the proposed control scheme.

A general class of uncertain nonlinear systems with uncertain input unmodeled dynamics is considered. The system structure includes a core nominal subsystem of triangular structure with additive uncertain nonlinear functions and coupled with a set of uncertain nonlinear appended dynamics as well as uncertain dynamic input nonlinearities. The dynamic input nonlinearities include an uncertain non-affine function and a dynamic perturbation through an uncertain input unmodeled dynamics subsystem. The input unmodeled dynamics subsystem is coupled with the entire system state including the unmeasured state of the uncertain nonlinear appended dynamics coupled with the nominal triangular system. The control design is based on dual controller/observer dynamic high-gain scaling with an additional dynamic scaling based on a singular-perturbation-like redesign to address the non-affine and uncertain nature of the input appearance into the system dynamics. The proposed approach yields a global robust adaptive output-feedback control design that is robust to the dynamic input uncertainties and to uncertain nonlinear functions allowed throughout the system structure.

Traffic congestion control for Aw–Rascle–Zhang model

In this study, the fault-tolerant control problem is investigated for a class of non-affine output-constrained multi-input multiple-output non-linear systems with actuator faults. The controlled systems contain unknown non-linear functions, unknown external disturbance and immeasurable states. The fuzzy logic systems and a robust compensation function are employed to deal with the unknown non-linear functions and non-affine non-linear actuator fault problems. Then, the state observer and the non-linear disturbance observer are developed for estimating the immeasurable states and unknown compounded disturbance, respectively. By using the barrier Lyapunov function method and in the unified framework of adaptive backstepping output feedback control design, a novel adaptive fuzzy fault-tolerant controller design scheme is developed. It is shown that all the variables of the closed-loop system are semi-globally uniformly bounded. Moreover, the system outputs cannot violate their predefined bounds in the presence of the non-affine non-linear faults. A simulation is given to validate the effectiveness of the proposed approach.

In this article, an observer-based fuzzy adaptive inverse optimal output feedback control problem is studied for a class of nonlinear systems in strict-feedback form. The considered nonlinear systems contain unknown nonlinear dynamics and their states are not measured directly. Fuzzy logic systems are applied to identify the unknown nonlinear dynamics and an auxiliary nonlinear system is constructed. Based on this auxiliary system, a fuzzy state observer is first designed to estimate the immeasurable states. By using the inverse optimal principle and adaptive backstepping design theory, an observer-based fuzzy adaptive inverse optimal output feedback control scheme is then developed. The proposed inverse optimal control scheme need not assume that the states are measurable. It also guarantees that the closed-loop system is semiglobally uniformly ultimately bounded, and achieves the optimal control objective as well. Finally, two simulation examples are provided to check the validity of the presented control method.

We propose an adaptive output-feedback control design technique for feedforward systems with Input-to-State Stable (ISS) appended dynamics and disturbance inputs. The design is based on our recent results on the application of dynamic high-gain scaling to state-feedback and output-feedback control of feedforward systems. Unlike previous approaches to the control of feedforward systems, the dynamic high-gain scaling technique provides robustness to additive disturbances and enabled the first output-feedback controller design for feedforward systems. In this paper, we further investigate the robustness properties of the dynamic high-gain scaling approach by introducing exogenous disturbance inputs and ISS appended dynamics coupled with all the system states and the input. The designed adaptive output-feedback controller achieves BIBS stability with respect to disturbance inputs and also provides a disturbance attenuation result. This provides the first results for feedforward systems with ISS appended dynamics and disturbance inputs.