Decentralized control design for large-scale systems with strong interconnections using neural networks

We propose a decentralized neural network (NN) controller for a class of large-scale nonlinear systems with the strong interconnections. The NNs are used to approximate the unknown subsystems and interconnections. Due to the functional approximation capabilities of NNs, the additional precautions are not required to be made for avoiding the possible control singularity problems. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero. Furthermore, the issue of transient performance of the subsystems is also addressed under an analytical framework.     

Decentralized backstepping output-feedback control for stochastic interconnected systems with time-varying delays using neural networks

This paper addresses the decentralized adaptive output-feedback control problem for a class of interconnected stochastic strict-feedback uncertain systems described by It $\hat{\hbox{o}}$ differential equation using neural networks. Compared with the existing literature, this paper removes the commonly used assumption that the interconnections are bounded by known functions multiplying unknown parameters, and all unknown interconnections are lumped in a suitable function which is compensated by only a neural network in each subsystem. So, the controller is simpler even than that for the strict-feedback systems described by the ordinary differential equation. Moreover, the circle criterion is applied to designing nonlinear observers for the estimates of system states. A simulation example is used to illustrate the effectiveness of control scheme proposed in this paper.     

Decentralized adaptive output-feedback control for a class of nonlinear large-scale systems with unknown time-varying delayed interactions

In this paper, the authors investigate a decentralized adaptive output-feedback controller design for large-scale nonlinear systems with input saturations and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions including all states of subsystems. This point is a main contribution of this paper compared with previous output-feedback control approaches which assume that the time-delayed bounding functions only depend on measurable output variables. The bounding functions are compensated by using appropriate Lyapunov–Krasovskii functionals and the function approximation technique based on neural networks. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.     

Decentralized sliding-mode output-feedback control of interconnected discrete-delay systems

Global decentralized discrete sliding mode control of a class of interconnected system using only output information is considered in this paper. Matched and unmatched uncertainties as well as known and unknown interconnections are treated. Bounded time-varying delays are considered within the subsystems and through interconnections. The stability of the reduced-order interconnected systems is analyzed using the Lyapunov–Kraszovskii approach and a novel reachability condition of the composite sliding surfaces is established. The resultant dynamics are proved to be globally asymptotically stable. Simulations show the efficacy of the proposed scheme.     

Adaptive output-feedback control of nonlinear systems with unknown nonlinearities

In this paper, we consider global adaptive output-feedback control of nonlinear systems in output-feedback form, without a priori knowledge of system nonlinearities. Our proposed adaptive controller is a high-gain linear controller (since we have no knowledge on system nonlinearities), with the high-gain parameter tuned online via a switching logic. Global stability results of the closed-loop system have been proved.     

Decentralized output-feedback control of large-scale nonlinear systems with sensor noise

This paper presents a new tool for decentralized output-feedback control design of large-scale nonlinear systems in the presence of non-smooth sensor noise. Through a recursive control design approach, the closed-loop decentralized system is transformed into a network of input-to-state stable (ISS) systems and the influences of the sensor noise are represented by ISS gains. The decentralized control objective is achieved by applying the cyclic-small-gain theorem to the closed-loop decentralized system. Moreover, the outputs of the closed-loop decentralized system can be driven arbitrarily close to the levels of their corresponding sensor noise.     

Decentralized fault detection and isolation of Markovian jump interconnected systems with unknown interconnections

This paper investigates the decentralized fault detection and isolation (FDI) problem for Markovian jump interconnected systems with unknown interconnections. Different from the existing decentralized FDI approaches, the requirement for access to operation modes of all subsystems, which is unreasonable and hard to meet in realistic applications, is removed. By utilizing local measurements and neighboring mode information, a decentralized FDI filter is constructed to generate a residual for each subsystem of Markovian jump interconnected system. Then, a new design method is developed such that the resulting augmented system is stochastically stable and the generated residual is sensitive to local fault. In addition, the proposed method can achieve fault detection and isolation simultaneously. Finally, two examples are given to illustrate the effectiveness and merits of the new results. Copyright © 2017 John Wiley & Sons, Ltd.     

Decentralized stabilization of Markovian jump interconnected systems with unknown interconnections and measurement errors

This paper investigates the decentralized output feedback control problem for Markovian jump interconnected systems with unknown interconnections and measurement errors. Different from some existing results, the global operation modes of all subsystems are not required to be completely accessible for the decentralized control system. A decentralized dynamic output feedback controller is constructed using neighboring mode information and local outputs, where the measurement errors between actual and measured outputs are considered. Subsequently, a new design method is developed such that the resultant closed‐loop system is stochastically stable and satisfying an L_∞‐norm constraint. Sufficient conditions are formulated by linear matrix inequalities, and the controller gains are characterized in terms of the solution of a convex optimization problem. Finally, an example is given to illustrate the effectiveness of the proposed theoretical results.     

Global stabilisation via switching output-feedback for power integrator systems with unknown control direction

This paper considers the global output-feedback stabilisation for power integrator systems with unknown control direction. The presence of uncontrollable and unobservable linearisation renders the strategy based on Nussbaum function rather difficult (even impossible) to compensate the unknown control direction. In this paper, to dominate the inherent nonlinearities and unknowns, a powerful switching strategy is proposed by combining an output-feedback scheme and a switching mechanism. First, a special case with known control direction is considered to provide a basic structure and selection rules of design parameters for the expected controller, and then, the essential case with unknown control direction is investigated to build a suitable switching logic to online tune the design parameters. The proposed switching-type output-feedback controller guarantees that all the system signals are globally bounded and ultimately converge to zero. A numerical example is given to illustrate the effectiveness of the proposed methods.     

Data-driven output-feedback fault-tolerant control for unknown dynamic systems with faults changing system dynamics

This paper studies the data-driven output-feedback fault-tolerant control (FTC) problem for unknown dynamic systems with faults changing system dynamics. In a framework of active FTC, two basic issues are addressed: the fault detection employing only the measured input–output information; the controller reconfiguration to achieve optimal output-feedback control in the presence of multiple faults. To detect faults and write the system state via the input–output data, an approach to data-driven design of a residual generator with a full-rank transformation matrix is presented. An output-feedback approximate dynamic programming method is developed to solve the optimal control problem under the condition that the unknown linear time-invariant discrete-time plant has multiple outputs. According to the above results and the proposed input–output data-based value function approximation structure of time-varying plants, a model-free output-feedback FTC scheme considering optimal performance is given. Finally, two numerical examples and a practical example of a DC motor control system are used to demonstrate the effectiveness of the proposed methods.