Output Feedback Control Approach Research Articles

Observer-based fuzzy adaptive control of nonlinear systems with actuator faults and unmodeled dynamics

In this paper, an adaptive fuzzy output feedback fault-tolerant control scheme is developed for a class of nonlinear systems with actuator faults, unmodeled dynamics and without assuming the states being available for control design. Fuzzy logic systems are employed to approximate unknown nonlinear functions, and a fuzzy state observer is established to estimate the unmeasured states. By combining adaptive backstepping technique with changing supplying function technique and the nonlinear fault-tolerant control theory, a novel adaptive fuzzy fault-tolerant output feedback control approach is developed. It is proved that proposed control approach guarantees that all the variables of the closed-loop system are bounded. The simulation results are provided to illustrate the effectiveness of the proposed control approach. Compared with the existing results, the main contributions of this paper are as follows: (1) the proposed control method removes the restrictive assumption that all the states of the controlled system be measured directly; and (2) the proposed control method cannot only compensate for the actuator faults, but also solve the problem of the unmodeled dynamics.

Observer-based adaptive fuzzy decentralized control for stochastic large-scale nonlinear systems with unknown dead-zones

This paper studies the problem of adaptive fuzzy decentralized output feedback control for a class of uncertain stochastic large-scale nonlinear systems. The considered systems have unknown nonlinear functions, unknown dead-zones, unmodeled dynamics and unmeasurable state variables. Therefore, fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is established to estimate the unmeasurable states. Based on the backstepping design and the supply changing function technique, a robust adaptive fuzzy decentralized output feedback control approach is developed. The proposed control approach can ensure the closed-loop system to be input-state-practically stable (ISpS) in probability, and accommodate the unmodeled dynamics and unknown dead-zones as well. The effectiveness of the proposed control approach is illustrated by a simulation example.

A Robust Linear Field-Oriented Voltage Control for the Induction Motor: Experimental Results

A field-oriented armature-input-voltage output-feedback control approach is proposed for the robust linear controller design on an induction motor. The scheme simultaneously solves an angular-velocity reference-trajectory tracking task and a flux magnitude regulation in the presence of arbitrary time-varying load torques and unknown nonlinearities. The field-oriented input-voltage scheme is combined with linear high-gain asymptotic observers, of the generalized proportional-integral type, and linear active-disturbance-rejection output-feedback controllers. The linear observers online estimate, in a simultaneous manner, the output phase variables and the lumped effects of the following: 1) unknown time-varying load torques and unmodeled frictions and 2) rather complex state-dependent nonlinearities present in the electric and magnetic circuits. The field-oriented part of the scheme uses the classical flux observer or simulator. The proposed control laws naturally decouple, while linearizing, the extended second-order dynamics for the angular velocity and the squared flux magnitude. The proposed control scheme is here tested on an experimental induction motor setup.

Adaptive fuzzy fault-tolerant control of static var compensator based on dynamic surface control technique

The adaptive fuzzy faults tolerant control problem is investigated for a single machine bus system with static var compensator (SVC). This design does not require that speed of the generator rotor and susceptance of the overall system are measured, and also does not require that the parameters of the controlled system are known accurately. The considered faults are modeled as both loss of effectiveness and lock-in-place. A fuzzy state observer is designed to estimate the speed of the generator rotor and susceptance, by combining the adaptive backstepping technique with a dynamic surface control (DSC) approach, a novel adaptive fuzzy output-feedback faults tolerant control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the angle of the generator rotor follows a desired value. Simulation results are presented to show the effectiveness of the approach.

A Combined Backstepping and Stochastic Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control

In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.

Adaptive fuzzy output feedback control of nonlinear uncertain systems with unknown backlash-like hysteresis based on modular design

In this paper, an adaptive fuzzy output feedback control approach is presented for a class of single-input single-output uncertain nonlinear systems with unknown backlash-like hysteresis and unmeasured states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a state filter observer is designed to estimate unmeasured states. Combining the backstepping recursive design with modular design techniques, a new adaptive fuzzy output control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. A simulation is included to illustrate the effectiveness of the proposed approach. The important feature of the proposed control approach is that it can solve the states immeasurable problem of nonlinear systems with unknown backlash-like hysteresis and the problem of unknown virtual control coefficients.

Adaptive Fuzzy Output Feedback Control of MIMO Nonlinear Systems With Unknown Dead-Zone Inputs

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of multi-input and multi-output (MIMO) strict-feedback nonlinear systems with both unknown nonsymmetric dead-zone inputs and immeasurable states. In this research, fuzzy logic systems are utilized to evaluate the unknown nonlinear functions, and a fuzzy adaptive state observer is established to estimate the unmeasured states. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as a system uncertainty, a new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique. It is shown that the proposed control approach can assure that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded. It is also shown that the observer and tracking errors converge to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Adaptive fuzzy backstepping control of static var compensator based on state observer

The robust adaptive fuzzy control problem is investigated for a single machine bus system with static var compensator (SVC). This design does not require that speed of the generator rotor and susceptance of the overall system are measured, and also does not require that the parameters of controlled system are known accurately. The fuzzy logic systems are used to approximate the nonlinear functions of the system, and a fuzzy state observer is designed to estimate the speed of the generator rotor and susceptance. By utilizing the fuzzy state observer, and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the angle of the generator rotor follows a desired value. Simulation results are presented to show the effectiveness of the approach.

Adaptive fuzzy output feedback decentralized control of pure‐feedback nonlinear large‐scale systems

SUMMARYIn this paper, an adaptive fuzzy decentralized output feedback control approach is presented for a class of uncertain nonlinear pure‐feedback large‐scale systems with immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the immeasurable states. On the basis of the adaptive backstepping recursive design technique, an adaptive fuzzy decentralized output feedback is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed‐loop system are semiglobally uniformly ultimately bounded (SUUB), and that the observer and tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation studies are included to illustrate the effectiveness of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

Robust adaptive fuzzy output feedback control for stochastic nonlinear systems with unknown control direction

This paper discusses the problem of adaptive fuzzy output feedback control for a class of uncertain stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear functions, dynamical uncertainties, unknown control direction and unmeasured state variables. In this paper, the fuzzy logic systems are used to approximate the unknown nonlinear functions, and a filter state observer is developed for estimating the unmeasured states. To solve the problems of the dynamical uncertainties and the unknown control direction, the changing supply function and Nussbaum function techniques are incorporated into the backstepping recursive design technique, and a new robust adaptive fuzzy output feedback control approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded in probability, and also that the observer errors and the output of the system can be regulated to a small neighborhood of the origin by choosing design parameters appropriately. A simulation example is provided to show the effectiveness of the proposed approach.

Robust adaptive fuzzy control for a class of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive fuzzy output feedback control approach is developed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, dynamical uncertainties and without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the dynamical signal combined with changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive fuzzy output feedback control scheme is constructed. It is proved that all the solutions of the closed-loop system are bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. Two simulation examples are provided to demonstrate the effectiveness of the proposed control approach.

Fuzzy adaptive dynamic surface control for a single-link flexible-joint robot

In this paper, a fuzzy adaptive controller is proposed for a single-link flexible-joint robot. Fuzzy logic systems are used to approximate unknown nonlinearities, and then a fuzzy state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping design with dynamic surface control (DSC) technique, a fuzzy adaptive output-feedback backstepping control approach is developed. It is proved that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and both the observer and tracking errors converge to a small neighborhood of the origin by appropriate choosing the design parameters. The simulation results are provided to demonstrate the effectiveness of the proposed controller. Two key advantages of our scheme are that (i) the proposed control method does not require that the link velocity and actuator velocity of single-link flexible-joint robot be measured directly, and (ii) the problem of “explosion of complexity” is avoided.

Nonlinear Model-Based Control of a Semi-Industrial Batch Crystallizer Using a Population Balance Modeling Framework

This paper presents an output feedback nonlinear model-based control approach for optimal operation of industrial batch crystallizers. A full population balance model is utilized as the cornerstone of the control approach. The modeling framework allows us to describe the dynamics of a wide range of industrial batch crystallizers. In addition, it facilitates the use of performance objectives expressed in terms of crystal size distribution. The core component of the control approach is an optimal control problem, which is solved by the direct multiple shooting strategy. To ensure the effectiveness of the optimal operating policies in the presence of model imperfections and process uncertainties, the model predictions are adapted on the basis of online measurements using a moving horizon state estimator. The nonlinear model-based control approach is applied to a semi-industrial crystallizer. The simulation results suggest that the feasibility of real-time control of the crystallizer is largely dependent on the discretization coarseness of the population balance model. The control performance can be greatly deteriorated due to inadequate discretization of the population balance equation. This results from structural model imperfection, which is effectively compensated for by using the online measurements to confer an integrating action to the dynamic optimizer. The real-time feasibility of the output feedback control approach is experimentally corroborated for fed-batch evaporative crystallization of ammonium sulphate. It is observed that the use of the control approach leads to a substantial increase, i.e., up to 15%, in the batch crystal content as the product quality is sustained.

Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping

In this paper, an adaptive fuzzy output feedback approach is proposed for a single-link robotic manipulator coupled to a brushed direct current (DC) motor with a nonrigid joint. The controller is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring the measurements of link position. Using fuzzy logic systems to approximate the unknown nonlinearities, an adaptive fuzzy filter observer is designed to estimate the immeasurable states. By combining the adaptive backstepping and dynamic surface control (DSC) techniques, an adaptive fuzzy output feedback control approach is developed. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. Three key advantages of our scheme are as follows: (i) the proposed adaptive fuzzy control approach does not require that all the states of the system be measured directly, (ii) the proposed control approach can solve the control problem of robotic manipulators with unknown nonlinear uncertainties, and (iii) the problem of “explosion of complexity” existing in the conventional backstepping control methods is avoided. The detailed simulation results are provided to demonstrate the effectiveness of the proposed controller.

Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive neural network (NN) backstepping output feedback control approach is proposed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, unmodeled dynamics, dynamical uncertainties and without requiring the measurements of the states. The NNs are used to approximate the unknown nonlinear functions, and a filter observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive NN output feedback control approach is constructed. It is mathematically proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. The simulation example and comparison results further justify the effectiveness of the proposed approach.

Robust adaptive decentralized fuzzy control for stochastic large-scale nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive fuzzy decentralized backstepping output feedback control approach is proposed for a class of uncertain large-scale stochastic nonlinear systems with unknown nonlinear functions, dynamical uncertainties and without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the changing supply function technique is incorporated into the backstepping recursive design technique, and a new robust adaptive fuzzy decentralized output feedback control approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. A simulation example is provided to show the effectiveness of the proposed approach.

Adaptive fuzzy output feedback control of MIMO nonlinear uncertain systems with time-varying delays and unknown backlash-like hysteresis

In this paper, an adaptive fuzzy backstepping output feedback control approach is developed for a class of multi-input and multi-output (MIMO) nonlinear systems with time-varying delays and unknown backlash-like hysteresis. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and an adaptive fuzzy state observer is designed to estimate the unmeasured states. By utilizing the adaptive fuzzy observer, and combining the fuzzy adaptive control theory with backstepping approach, an adaptive fuzzy output feedback control approach is developed. The proposed control approach can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SUUB), and the observer error and tracking error are proved to converge to a small neighborhood of the origin. Two simulation examples are included to illustrate the effectiveness of the proposed approach.

Adaptive fuzzy output feedback control of uncertain nonlinear systems with unknown backlash-like hysteresis

In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain nonlinear systems with unknown backlash-like hysteresis and unmeasured states. The fuzzy logic systems are used to approximate the nonlinear system functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the fuzzy state observer, and combining the adaptive backstepping technique with adaptive fuzzy control design, an observer-based adaptive fuzzy output-feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SUUB), and both observer error and tracking error can converge to a small neighborhood of the origin. Two simulations are included to illustrate the effectiveness of the proposed approach.

Adaptive Fuzzy Output Feedback Tracking Backstepping Control of Strict-Feedback Nonlinear Systems With Unknown Dead Zones

In this paper, an adaptive fuzzy backstepping control approach is considered for a class of nonlinear strict-feedback systems with unknown functions, unknown dead zones, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy filters state observer is designed to estimate the immeasurable states. By using the adaptive backstepping recursive design technique and constructing the dead-zone inverse, a new adaptive fuzzy backstepping output-feedback control approach is developed. It is mathematically proved that all the signals of the resulting closed-loop adaptive control system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin by appropriate choice of design parameters. The proposed approach cannot only solve the problem of the dead zones but also cancel the restrictive assumption in the previous literature that the states are all available for measurement. Two simulation examples are provided to show the effectiveness of the proposed approach.

Adaptive fuzzy backstepping output feedback control of nonlinear uncertain time-delay systems based on high-gain filters

In this paper, an adaptive fuzzy output feedback control approach is developed for a class of SISO uncertain nonlinear strict-feedback systems. The considered nonlinear systems contain unknown nonlinear functions, unknown time-varying delays and unmeasured states. The fuzzy logic systems are first used to approximate the unknown nonlinear functions, and then a high-gain filter is designed to estimate the unmeasured states. Combining the backstepping recursive design technique and adaptive fuzzy control design, an adaptive fuzzy output feedback backstepping control method is developed. It is proved that the proposed adaptive fuzzy control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and both the observer error and tracking error converge to a small neighborhood of the origin. Two key advantages of our scheme are that (i) the high-gain filter is designed to estimate unmeasured states of time-delay nonlinear system, and (ii) the virtual control gains are functions. A simulation is included to illustrate the effectiveness of the proposed approach.