Design of memory-based adaptive integral sliding-mode controller for fractional-order T-S fuzzy systems and its applications

This study investigates the passivity analysis of fractional-order Takagi-Sugeno (T-S) fuzzy systems subject to external disturbances and nonlinear perturbations under an adaptive integral sliding mode control (AISMC) methodology. To better accommodate the features of the T-S fuzzy dynamical model, a novel fractional-order memory-based integral-type sliding manifold function is defined, which is different from the existing sliding manifold function. With the help of Caputo fractional-order derivative properties and quadratic Lyapunov functional, some linear matrix inequality (LMI)-based sufficient criteria are derived to ensure the asymptotic stability conditions of resulting sliding mode dynamics with passive performance index. Besides that, an adaptive sliding mode control law is designed for the addressed systems to guarantee the system state variables onto the predefined integral sliding manifold. Finally, the effectiveness of the proposed controller is validated based on derived sufficient conditions with two practical models, such as fractional-order interconnected power systems and fractional-order permanent-magnet synchronous generator (PMSG) model, respectively.     

Adaptive neural network decentralized stabilization for nonlinear large scale interconnected systems with expanding construction

A backstepping-based adaptive neural network decentralized stabilization approach is presented for the expanding construction of a class of nonlinear large scale interconnected systems in this paper. The expanding construction of large scale interconnected systems is to add some new subsystems into the original system during the operation of the original system. For stabilization of the expanding system, it is more realistic to keep the decentralized control laws of the original subsystems unchanged. And the decentralized control laws of the new subsystems must be designed to stabilize both itself and the resultant large scale system. In this paper, neural networks are used to approximate the unknown nonlinear functions in the new subsystems and the unknown nonlinear interconnection functions. The decentralized control laws and the parameter adaptive laws of the new subsystems are designed by using backstepping technique for the expanding construction of the large-scale interconnected system. Based on Lyapunov stability theory, the uniform and ultimate boundedness of all signals in the closed-loop system is proved. Two illustrative examples show the feasibility of the presented approach.     

Decentralized low-complexity tracking of uncertain interconnected high-order nonlinear systems with unknown high powers

A global decentralized low-complexity tracker design methodology is proposed for uncertain interconnected high-order nonlinear systems with unknown high powers. It is assumed that interconnected nonlinearities are bounded by completely unknown nonlinearities, rather than, a linear combination of high-ordered state variables. Compared with the existing decentralized results for interconnected nonlinear systems with known high powers, the decentralized robust controller, which achieves the pre-designable transient and steady-state tracking performance for each subsystem, is designed by employing nonlinear error surfaces with time-varying performance functions, regardless of unknown nonlinear interactions and high powers related to virtual and actual control variables. The proposed decentralized continuous robust low-complexity tracker is realized without the use of any adaptive or function approximation techniques for estimating unknown parameters and nonlinearities. The stability and preassigned tracking performance of the resulting decentralized low-complexity control system are thoroughly analyzed in the Lyapunov sense. Finally, simulation results on coupled underactuated mechanical systems are provided to show the effectiveness of the proposed theoretical result.     

New fractional-order integral inequalities: Application to fractional-order systems with time-varying delay

In this paper, the problem of delay-dependent stability analysis of fractional-order systems with time-varying delay is investigated. First, a class of novel fractional-order integral inequalities for quadratic functions by constructing appropriate auxiliary functions is proposed, which has been proven to be useful in analyzing fractional-order systems with time-varying delay. Based on these proposed inequalities, the Lyapunov–Krasovskii functions are designed to deal with the time-varying delay terms, reducing the conservatism of the stability criteria. Furthermore, delay-dependent criteria are derived to achieve asymptotic stability of fractional-order systems with time-varying delay. Finally, two examples are provided to illustrate the effectiveness and feasibility of the proposed stability criteria.     

Decentralized optimal controller design for multimachine power systems using successive approximation approach

In this paper, we propose to develop algorithmically and implement a nonlinear decentralized optimal control for multimachine power systems, based on a successive approximation approach for designing the optimal controller with respect to quadratic performance index. The advantage of this approach is to transform the high order coupling nonlinear two-point boundary value (TPBV) problem into a sequence of linear decoupling TPBV problem, which uniformly converges to the optimal control for nonlinear interconnected large scale systems. We apply this approach to a 3-machine power system which generators are strongly nonlinear interconnected, and containing possible uncertainties on the parameters. We demonstrate clearly via advanced simulations that this approach brings better performances than other decentralized controller, improving effectively transient stability of these power systems in few iterative sequences for different cases of perturbations.     

Sampled observer-based adaptive decentralized control for strict-feedback interconnected nonlinear systems

This paper investigates the decentralized tracking control problem for a class of strict-feedback interconnected nonlinear systems with unknown parameters, where the system states are unmeasurable and the interconnections are unknown. Different from the existing results, where the output is available all the time, we consider the case that the output is only available at the sampled instants, which means the failure of existing methods. By introducing a kind of sampled observer for each subsystem, the system states and unknown parameters are jointly estimated. Based on which, a totally decentralized output feedback control scheme is developed to achieve the desired tracking performance by applying backstepping technique, where a compensation mechanism is utilized to address the unknown interconnections from other subsystems. Subsequently, by using Lyapunov stability theory, it is proved that all the signals in the closed-loop system are bounded and the tracking errors converge to an adjustable neighbourhood of the origin. Finally, an example is used to illustrate the effectiveness of the proposed method.     

Fractional-order partial pole assignment for time-delay systems based on resonance and time response criteria analysis

In this paper, a novel fractional-order partial pole assignment (FPPA) control algorithm is proposed for systems with time-delay. The FPPA control algorithm is essentially an extension of the original pole assignment, which could change undesired pole locations into desired pole locations. The presented control scheme can be used on open loop poorly damped or unstable systems, which is superior to most other time-delay compensation schemes. The discussion on choosing desirable pole locations is presented based on stability and resonance conditions in the frequency domain. The controlled system is also studied in the time domain based on different transient performance indicators, namely overshoot, settling time, and rising time. In addition, the parameters of the proposed FPPA control algorithm are tunable, thus the control scheme can be used to satisfy different control requirements. Simulation results of stable and unstable fractional-order plants with time-delay are shown to verify the effectiveness and practicability of the FPPA control algorithm.     

Barrier Lyapunov function-based decentralized adaptive neural control for uncertain high-order stochastic nonlinear interconnected systems with output constraints

Decentralized adaptive neural backstepping control scheme is developed for uncertain high-order stochastic nonlinear systems with unknown interconnected nonlinearity and output constraints. For the control of high-order nonlinear interconnected systems, it is assumed that nonlinear system functions are unknown. It is for the first time to control stochastic nonlinear high-order systems with output constraints. Firstly, by constructing barrier Lyapunov functions, output constraints are handled. Secondly, at each recursive step, only one adaptive parameter is updated to overcome over-parameterization problems, and RBF neural networks are used to identify unknown nonlinear functions so that the difficulties caused by completely unknown system functions and stochastic disturbances are tackled. Finally, based on the Lyapunov stability method, the decentralized adaptive control scheme via neural networks approximator is proposed, ultimately reducing the number of learning parameters. It is shown that the designed controller can guarantee all the signals of the resulting closed-loop system to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking errors for each subsystem are driven to a small neighborhood of zero. The simulation studies are performed to verify the effectiveness of the proposed control strategy.     

Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems

This paper presents a novel approach to address the decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems. The overall system is composed of a number of discrete-time interconnected nonlinear subsystems at the presence of multiple faults occurring at unknown time-instants. In order to deal with the unknown interconnection effects and changes in model dynamics due to multiple faults, both passive and active fault tolerant control design are considered. In the Active fault tolerant case an online approximation algorithm is applied to estimate the unknown interconnection effects and changes in model dynamics due to multiple faults. Besides, the decentralized control strategy is implemented for each subsystem with the model predictive control algorithm subject to some constraints. It is showed that the proposed method guarantees input-to-state stability characterization for both local subsystems and the global system under some predetermined assumptions. The simulation results are exploited to illustrate the applicability of the proposed method.     

Decentralized sampled-data fuzzy controller design for a VTOL UAV

This study focuses on a sampled-data fuzzy decentralized tracking control problem for a quadrotor unmanned aerial vehicle (UAV) under the variable sampling rate condition. To this end, the overall dynamics of the quadrotor is expressed as a decentralized Takagi–Sugeno (T–S) fuzzy model interconnected with each other. Although the proposed decentralized control technique divides the overall UAV control system into attitude and position subsystems, the stability of the entire control system is guaranteed. Besides, in this paper, the model uncertainty, interconnection, and reference trajectory are considered as disturbances acting on the tracking error. To attenuate these disturbances, a novel sampled-data tracking control design technique is derived based on a linear reference model to be tracked and the time-dependent Lyapunov–Krasovskii functional (LKF). By doing so, both the stability of the tracking error dynamics and the minimization of tracking performance are guaranteed. Also, the proposed tracking control design method is derived as a linear matrix inequality (LMI)-based optimal problem. Finally, a simulation example is provided to demonstrate the effectiveness and feasibility of the proposed design methodology.     

Decentralized adaptive output-feedback control of interconnected nonlinear time-delay systems using minimal neural networks

This paper presents a minimal-neural-networks-based design approach for the decentralized output-feedback tracking of uncertain interconnected strict-feedback nonlinear systems with unknown time-varying delayed interactions unmatched in control inputs. Compared with existing approximation-based decentralized output-feedback designs using multiple neural networks for each subsystem in lower triangular form, the main contribution of this paper is to provide a new recursive backstepping strategy for a local memoryless output-feedback controller design using only one neural network for each subsystem regardless of the order of subsystems, unmeasurable states, and unknown unmatched and delayed nonlinear interactions. In the proposed strategy, error surfaces are designed using unmeasurable states instead of measurable states and virtual controllers are regarded as intermediate signals for designing a local control law at the last step. Using Lyapunov stability theorem and the performance function technique, it is shown that all signals of the total controlled closed-loop system are bounded and the transient and steady-state performance bounds of local tracking errors can be preselected by adjusting design parameters independent of delayed interactions.     

Fractional-order follower observer design for tracking consensus in second-order leader multi-agent systems: Periodic sampled-based event-triggered control

This paper investigates the tracking consensus problem for the second-order leader systems by designing fractional-order observer, where a periodic sampled-based data event-triggered control is employed. In order to track the position information of leader, observers for followers are designed by fractional-order system, where only the relative position information is available. Furthermore, in the process of observers design, a sampled-based event-triggered strategy is proposed so that observers use the event-triggered sampled-data, to reduce the overall load of the network. In our proposed event-triggered strategy, the event detection only works at every sampling time instant which determines whether the sampled-data should be discarded or used. Under this control strategy, the Zeno-behavior is absolutely excluded since the minimum of inter-event times is inherently lower bounded by one sampling period. It is found that the followers can track state of the leader if fractional-order observers are appropriately designed and relevant parameters are properly selected. By using the generalized Nyquist stability criterion, a necessary and sufficient condition for the observer tracking consensus of the second-order leader systems is derived. The results show that the real and imaginary parts of the eigenvalues of the augmented Laplacian matrix, and fractional-order α of observer play a vital role in reaching consensus.     

Robust stability testing function for a complex interval family of fractional-order polynomials

The robust stability of a family of interval fractional-order systems with complex coefficients is investigated in this study. The concept of “a family of interval fractional-order systems with complex coefficients” means that the characteristic function of a control system can be of both commensurate and non-commensurate orders, the coefficients of the characteristic function can be uncertain parameters, and may be complex numbers. At first, a simple graphical procedure is presented for robust stability analysis. The “robust stability testing function” is then extended to look at the robust conditions. To the best of the authors’ knowledge, no auxiliary function for analyzing the robust stability of the systems under investigation has been introduced until now. Moreover, lower and upper frequency bounds are provided which are useful to improve the computational efficiency of testing the robust stability conditions. Eventually, to verify the results, analytical examples and numerical simulations are provided.     

Event-triggered decentralized output-feedback control for interconnected nonlinear systems with input quantization

In this paper, the event-triggered decentralized control problem for interconnected nonlinear systems with input quantization is investigated. A state observer is constructed to estimate the unmeasurable states, and the state-dependent interconnections are accommodated by presenting some smooth functions. Then by employing backstepping technique and neural networks (NNs) approximation capability, a novel decentralized output feedback control strategy and an event-triggered mechanism are designed simultaneously. It is proved through Lyapunov theory that the closed-loop system is stable and the tracking property of all subsystems is guaranteed. Finally, the effectiveness of the proposed scheme is illustrated by an example.     

Consensus control of incommensurate fractional-order multi-agent systems: An LMI approach

This paper concentrates on the distributed consensus control of heterogeneous fractional-order multi-agent systems (FO-MAS) with interval uncertainties. Unlike previous methods, no restrictive assumptions are considered on the fractional-orders of the agents and they can have non-identical fractional-orders. Therefore, the closed-loop system becomes an incommensurate fractional-order system and its stability analysis is not easy. It makes consensus control more challenging. To design a systematic controller, new Lyapunov-based Linear Matrix Inequality (LMI) conditions are proposed which are suitable to determine the state feedback controller gains. Then, the consensus of heterogeneous fractional-order agents with an observer-based controller is provided. Finally, some numerical examples are provided to verify the effectiveness of our results.     

Finite-time fractional-order adaptive intelligent backstepping sliding mode control of uncertain fractional-order chaotic systems

This paper precedes chaos control of fractional-order chaotic systems in presence of uncertainty and external disturbances. Based on some basic properties on fractional calculus and the stability theorems, we present a hybrid adaptive intelligent backstepping-sliding mode controller (FAIBSMC) for the finite-time control of such systems. The FAIBSMC is proposed based on the concept of active control technique. The asymptotic stability of the controller is shown based on Lyapunov theorem and the finite time reaching to the sliding surfaces is also proved. Illustrative and comparative examples and simulation results are given to confirm the effectiveness of the proposed procedure, which consent well with the analytical results.     

Design of a H∞ model reference tracking control for interconnected nonlinear systems by decentralized dynamic output-feedback

This study investigates the problem of robust tracking control for interconnected nonlinear systems affected by uncertainties and external disturbances. The designed H_∞ dynamic output-feedback model reference tracking controller is parameterized in terms of linear matrix inequalities (LMIs), which is formulated within a convex optimization problem readily implementable. The resolution of such a problem, guarantying not only the quadratic stability but also a prescribed performance level of the resulting closed-loop system, enables to calculate concurrently the robust decentralized control and observation gain matrices. The established LMI conditions are computed in a single-step resolution to obtain all the controller/observer parameters and therefore to overcome the problem of iterative algorithm based on a multi-stage resolution leading in most cases to conservative and suboptimal solutions. Numerical simulations on diverse applications ranging from a numerical academic example to coupled inverted double pendulums and a 3-strongly interconnected machine power system are provided to corroborate the merit of the proposed control scheme.     

Chaotic synchronization between different fractional-order chaotic systems

Based on the idea of tracking control and stability theory of fractional-order systems, a novel synchronization approach for fractional order chaotic systems is proposed. We prove that the synchronization between drive system and response system with different fractional order q can be achieved, and the synchronization between different fractional-order chaotic systems with different fractional order q can be achieved. Two examples are used to illustrate the effectiveness of the proposed synchronization method. Numerical simulations coincide with the theoretical analysis.     

New finite-time stability for fractional-order time-varying time-delay linear systems: A Lyapunov approach

The primary goal of this paper is to examine the finite-time stability and finite-time contractive stability of the linear systems in fractional domain with time-varying delays. We develop some sufficient criteria for finite-time contractive stability and finite-time stability utilizing fractional-order Lyapunov-Razumikhin technique. To validate the proposed conditions, two different types of dynamical systems are taken into account, one is general time-delay fractional-order system and another one is fractional-order linear time-varying time-delay system, furthermore the efficacy of the stability conditions is demonstrated numerically.     

Intermittent control strategy for synchronization of fractional-order neural networks via piecewise Lyapunov function method

In this paper, the synchronization problem of fractional-order neural networks (FNNs) with chaotic dynamics is investigated via the intermittent control strategy. Two types of intermittent control methods, the aperiodic one and the periodic one, are applied to achieve the synchronization of the considered systems. Based on the dynamic characteristics of the intermittent control systems, the piecewise Lyapunov function method is employed to derive the synchronization criteria with less conservatism. The results under the aperiodically intermittent control show more generality than the ones via the periodically intermittent control. For each of the aperiodic and periodic cases, a simple controller design process is presented to show how to design the corresponding intermittent controller. Finally, two numerical examples are provided to demonstrate the effectiveness of the obtained theoretical results.