Systems In Nonstrict-feedback Form Research Articles

Decentralized fuzzy DSC for uncertain large-scale interconnected switched systems in non-strict feedback form with input saturation

This paper demonstrates a fuzzy decentralized dynamic surface control (DSC) scheme for switched large-scale interconnected nonlinear systems under arbitrary switching, which contains non-strict feedback form and unknown input saturation uncertainties. An auxiliary design system is established to handled input saturation. Uncertainties of non-strict feedback form are learned by fuzzy logic systems (FLSs) approximators, DSC method is designed to conquer “explosion of complexity” inherented by repeated differential of virtute controller in backstepping approach. Ii is shown that based on common Lyapunov function (CLF) design and analysis scheme, all the closed-loop systems signals are uniformly ultimately bounded (UUB), simulation results are provided to demonstrate the effectiveness of this proposed strategy.

Fixed-Time Fuzzy Adaptive Tracking Control for Output-Constrained Uncertain Nonlinear Systems in Nonstrict-Feedback Form

Fixed-Time Fuzzy Adaptive Tracking Control for Output-Constrained Uncertain Nonlinear Systems in Nonstrict-Feedback Form

Adaptive fuzzy constraint control for switched nonlinear systems in nonstrict feedback form

SummaryIn this paper, the adaptive fuzzy controller design problem is investigated for a class of switched nonlinear systems in nonstrict feedback form, in which the unknown functions are considered and are approximated. Moreover, the system states are constrained in corresponding compact. By using Barrier Lypunov function method and backstepping technique, the adaptive fuzzy controller is designed such that all the signals in the closed‐loop system are bounded, the system output can track the desired signal to small compact, and all the system states satisfy the constraint conditions. Finally, the simulation results show the effectiveness of the proposed method.

Adaptive finite‐time consensus tracking control for nonlinear multiagent systems in nonstrict feedback form with full‐state constraints

SummaryIn this article, the fuzzy adaptive finite‐time consensus tracking control problem for nonstrict feedback nonlinear multiagent systems with full‐state constraints is studied. The finite‐time control based on command filtered backstepping is proposed to guarantee the finite‐time convergence and eliminate the explosion of complexity problem caused by backstepping process, and the errors in the filtering process are compensated by using error compensation mechanism. Furthermore, based on the fuzzy logic systems, the uncertain nonlinear dynamics are approximated and the problem of state variables in nonstrict feedback form is solved by using the property of basis functions. The barrier Lyapunov functions are introduced to guarantee that all system states and compensated tracking error signals are constrained in the designed regions. A simulation example is given to verify the superiority of the proposed algorithm.

Adaptive neural network fault-tolerant control for MIMO systems with dead zone inputs

In this paper, an adaptive neural network fault-tolerant control (FTC) strategy is proposed for a class of multi-input and multi-output (MIMO) nonlinear systems in nonstrict-feedback form, in which sensor faults and nonsymmetric dead zone inputs are considered. Based on the backstepping method, the desired controller is designed. The adaptive neural network control approach is proposed to handle the problem of sensor faults. The effect of dead zone inputs on the system performance can be compensated by virtue of the boundedness of dead zone slopes. Furthermore, the dynamic surface control technique is introduced to prevent an “explosion of complexity. The proposed control method ensures that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and that the tracking errors converge to a compact set around the origin. Finally, simulation results are provided to demonstrate the effectiveness of the proposed approach.

Adaptive fuzzy output feedback FTC for nonstrict-feedback systems with sensor faults and dead zone input

This paper solves the fault-tolerant control (FTC) problem of uncertain nonlinear systems in nonstrict-feedback form. The controlled plants considered in this paper are more complicated than existing ones, which is composed of the unknown nonlinearities, unmeasured states, and sensor faults and unknown dead zone input nonlinearity. Fuzzy logic systems and a fault-estimation-based state observer are used for identifying the unknown nonlinearities and obtaining the unmeasured states, respectively. By designing the sensor faults compensation and the dead zone inverse compensation methods, an observer-based fault-tolerant compensation control scheme is developed under backstepping recursive design frame. And it is testified that the closed-loop signals are bounded, and the tracking performance is satisfied even when the controlled systems are not free of sensor faults and unknown dead zone input nonlinearity. An electromechanical system is used to test the effectiveness of the developed control strategy.

Adaptive Fuzzy Output-Feedback Tracking Control for Switched Nonstrict-Feedback Nonlinear Systems with Prescribed Performance

In this article, an adaptive fuzzy output-feedback tracking control scheme is proposed for a class of single-input and single-output uncertain switched nonlinear systems in nonstrict-feedback form with prescribed performance and arbitrary switching. By using the concept of prescribed performance, we mean that the tracking error converges to the predefined performance bounds. The unknown nonlinear functions are approximated by utilizing the fuzzy logic systems. For the systems under study, unavailable states are taken into account, which are estimated by constructing a fuzzy state observer. In order to overcome the difficulty arising from the nonstrict-feedback form, an effective adaptive parameter is introduced. By using the common Lyapunov function method and backstepping design approach, an adaptive control algorithm is presented for the considered system. The designed controller can guarantee that all the signals within closed-loop systems are semi-globally uniformly ultimately bounded. The effectiveness of the control strategy put forward in this paper is demonstrated by the simulation results.

Prescribed performance adaptive DSC for a class of time-delayed switched nonlinear systems in nonstrict-feedback form: Application to a two-stage chemical reactor

This study deals with the tracking problem for a class of nonstrict-feedback switched nonlinear systems (SNSs) with unknown time-delay and unknown functions under arbitrary switching. To achieve this goal, an adaptive neural network-based dynamic surface control (DSC) based on backstepping approach is proposed. A neural network (NN) approximator based on radial basis functions (RBFs) is utilized to approximate unknown functions. Considering properties of Gaussian basis function in RBFNNs, an adaptive neural network DSC for nonstrict-feedback structure has been developed. A Lyapunov-krasovskii functional is applied to compensate the effect of unknown delay terms. Furthermore, a prescribed performance bound (PPB) control strategy is utilized to retain the tracking error within a predefined bound. Finally, a practical example is provided to prove the effectiveness of the proposed method.

Simultaneous assessment of cortisol levels in saliva, hair and fingernail specimens in depressive and healthy people

This paper concentrates on the adaptive tracking control problem for a class of switched nonlinear systems in nonstrict-feedback form with uncertain actuator backlash. The switching signal considered in our model is arbitrary. The adaptive tracking controller design procedure is divided into three parts. Firstly, a novel fuzzy backlash model is presented to describe the uncertain backlash. Then, the fuzzy backlash is defuzzified and approximated by the linear combination of a linear term and a disturbance-like term without constructing a backlash inverse. Finally, an adaptive backstepping-based tracking controller via common Lyapunov function (CLF) method is presented to guarantee the tracking error asymptotically converges to an adjustable region of zero. The stability analysis and simulation examples are presented at the end of this paper to further verify effectiveness of our control scheme.

Adaptive Fuzzy Output Feedback Control for High-Order Switched Systems with Fuzzy Dead Zone

In this paper, an observer-based adaptive control problem for a class of high-order switched nonlinear systems in non-strict feedback form with fuzzy dead zone and arbitrary switchings is investigated. Fuzzy logic system was utilized to model the unknown nonlinear function with the universal approximation ability. An adaptive high-order observer is constructed to estimate unavailable state variables. The effect of dead zone can be eliminated by a Nussbaum function. By using the Lyapunov stability theory and backstepping design procedure, the proposed adaptive controller can guarantee all the variables in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Simulation results are exhibited to demonstrate the effectiveness of the proposed control scheme.

Observer-based adaptive neural optimal control for discrete-time systems in nonstrict-feedback form

This paper considers the problem of observer-based adaptive near-optimal control for a class of nonstrict-feedback discrete-time nonlinear systems with non-symmetric dead zone. In order to compensate the effect of dead-zone on the control performance, an adaptive auxiliary signal is constructed to estimate the unknown dead-zone parameters. For the unknown nonlinear functions, neural networks (NNs) are introduced to identify them and to estimate the unknown parameters. To resolve the difficulty resulting from the unavailable state variables, an NN-based observer is designed. Moreover, according to the framework of adaptive control, a novel reinforcement learning algorithm is developed to guarantee that the near-optimal control performance is achieved. Finally, some simulation results are provided to illustrate the effectiveness of the proposed control algorithm.

Adaptive tracking control for switched nonlinear systems with fuzzy actuator backlash

This paper concentrates on the adaptive tracking control problem for a class of switched nonlinear systems in nonstrict-feedback form with uncertain actuator backlash. The switching signal considered in our model is arbitrary. The adaptive tracking controller design procedure is divided into three parts. Firstly, a novel fuzzy backlash model is presented to describe the uncertain backlash. Then, the fuzzy backlash is defuzzified and approximated by the linear combination of a linear term and a disturbance-like term without constructing a backlash inverse. Finally, an adaptive backstepping-based tracking controller via common Lyapunov function (CLF) method is presented to guarantee the tracking error asymptotically converges to an adjustable region of zero. The stability analysis and simulation examples are presented at the end of this paper to further verify effectiveness of our control scheme.

Sampled-data control of a class of switched nonlinear systems under asynchronous switching

This paper focuses on an output feedback stabilization problem for a class of switched nonlinear systems in non-strict feedback form under asynchronous switching via sampled-data control. Since the output of the considered systems is measurable only at the sampling instants, an observer is designed with a tunable scaling gain to estimate the state, and then a sampled-data controller is constructed with the sampled estimated state. As a distinctive feature, a merging virtual switching signal is introduced to describe the asynchronous switching generated by detecting the activation of the subsystem. By choosing an appropriate Lyapunov function, it is proved that the constructed controller with dwell time constraint can globally stabilize the considered systems under asynchronous switching. Finally, the effectiveness of the proposed method is illustrated by two examples.

Neural Networks-Based Adaptive Tracking Control for a Class of High-Order Stochastic Nonlinear Time-Delay Systems

In this paper, neural networks (NNs)-based adaptive backstepping control problem is investigated for uncertain high-order stochastic nonlinear time-delay systems in nonstrict-feedback form. The control design problems appeared in our considered system are (1) high-order nonstrict-feedback structure; (2) completely unknown nonlinear functions; (3) full-state time delays; and (4) stochastic disturbances. The NNs are directly utilized to cope with the completely unknown nonlinear functions and stochastic disturbances existing in systems. The problem raised by full-state time delays is addressed by combining the appropriate Lyapunov-Krasovskii functional with hyperbolic tangent functions. In addition, the variable separation technique is employed to handle the nonstrict-feedback structure of the system. At last, on the basis of stochastic Lyapunov function method, an adaptive neural controller is developed for the considered system. It is shown that the designed adaptive controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded (SGUUB) and the desired signal can be tracked with a small domain of the origin. The simulation results are offered to illustrate the feasibility of the newly designed control scheme.

Fuzzy Adaptive Control Design Strategy of Nonlinear Switched Large-Scale Systems

The decentralized adaptive fuzzy control problem for switched nonstrict-feedback nonlinear large-scale systems is investigated in this paper. Fuzzy logic systems are adopted to identify the unknown nonlinear system functions and unknown interconnected nonlinearities. A linear state observer is designed to solve the problem of unmeasured states, and a new observer-based output-feedback decentralized control scheme is developed based on adaptive fuzzy control principle and bacsktepping design technique. The proposed control strategy not only solves the control design problem of more general form nonlinear switched systems in nonstrict-feedback form, but also does not require the switched signals to meet the restriction of average dwell time. The stability of fuzzy control systems under arbitrary switchings is proven based on the common Lyapunov function method. Simulation results of numerical examples are presented to further demonstrate the effectiveness of the proposed control strategy.

Finite-Time Filter Decentralized Control for Nonstrict-Feedback Nonlinear Large-Scale Systems

This paper solves the finite-time decentralized control problem for uncertain nonlinear large-scale systems in nonstrict-feedback form. The considered controlled plants are different from the previous results of finite-time control systems, which are the nonstrict-feedback large-scale systems with the unknown functions consisting of all states, interactions, and immeasurable states. Fuzzy logic systems and a filter-based state observer are utilized to model uncertain systems and deal with the immeasurable states, respectively. By combining the backstepping recursive design with Lyapunov function theory, a finite-time adaptive fuzzy decentralized control approach is raised. It is testified that the developed control strategy can guarantee that the closed-loop signals are bounded, and the outputs of systems have satisfactory tracking performance in a finite time. A quadruple-tank process system is given to testify the effectiveness and applicability of the proposed approach.

Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults.

The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.

Adaptive neural control of switched nonstrict-feedback nonlinear systems with multiple time-varying delays

This paper focuses on the problem of adaptive neural tracking control for a class of uncertain switched nonlinear time-varying delay systems in nonstrict-feedback form with arbitrary switchings. Radial basis function neural networks are used to model the unknown redefined continuous functions derived from Young’s inequalities. By combining bounding functions’ monotonously increasing property and variable separation technique, the uncertain system functions with nonstrict-feedback structure are dealt with such that iterative adaptive backstepping approach can be carried out. A newly developed Lyapunov–Krasovskii functional is utilized to compensate for the uncertainties of multiple time-varying delays, which makes the delay nonlinearities free from any assumptions. By introducing novel continuous functions, the problem of circular construction of controller is overcome deduced from employing one common tuning law. It is proved that the tracking error of adaptive neural control systems is semi-globally uniformly ultimately bounded with common Lyapunov function method. Finally, a simulation example is presented to show the effectiveness of the suggested control scheme.

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure.

This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.

Adaptive Neural Control of Uncertain Nonstrict-Feedback Stochastic Nonlinear Systems with Output Constraint and Unknown Dead Zone

An approximation-based adaptive neural controller is constructed for uncertain stochastic nonlinear systems in nonstrict-feedback form appearing dead-zone and output constraint. Neural networks (NNs) are directly utilized to approximate the unknown nonlinear functions existing in systems. A barrier Lyapunov function is introduced to ensure that the trajectory of output is limited within a predetermined range. By integrating NNs into the backstepping technique, an adaptive neural controller is designed to guarantee all variables existing in the considered closed-loop system are semi-globally uniformly ultimately bounded, and by appropriately tuning several design parameters online, the tracking error can be converged to a small neighborhood of the origin. Simulations on a numerical example are given to demonstrate the effectiveness of the method proposed in this paper.