Adaptive Fuzzy Decentralized Control for a Class of Large-Scale Nonlinear Systems with MIMO Subsystems

A novel decentralised indirect adaptive output feedback fuzzy controller with a compensation controller and an H ∞ tracking controller is presented for a class of uncertain large-scale nonlinear systems in this article. The compensator adaptively compensates for interconnections between subsystems as well as mismatched errors, while the H ∞ controller suppresses the effect of external disturbances. Based upon the combination of fuzzy inference systems, a state observer, H ∞ tracking technique and the strictly positive real condition, the proposed overall observer-based decentralised algorithm guarantees not only asymptotical tracking of reference trajectories but also an arbitrary small attenuation level of the unmodelled error dynamics including the disturbances on the tracking control. Simulation results substantiate the effectiveness of the proposed scheme.

In this article, a novel high gain observer (HGO)-based decentralised indirect adaptive fuzzy controller is developed for a class of uncertain affine large-scale nonlinear systems. By the combination of fuzzy logic systems and an HGO, the state variables are not required to be measurable. The proposed feedback and adaptation mechanisms guarantee that each subsystem is able to adaptively compensate for interconnections and disturbances with unknown bounds. It is ascertained using a singular perturbation method that all the signals of the closed-loop large-scale system stand uniformly ultimately bounded and the tracking errors converge to tunable neighbourhoods of the origin. Simulation results of correlated double inverted pendulums substantiate the effectiveness of the proposed controller.

In this paper, an adaptive decentralized control approach is proposed for a class of large-scale nonlinear systems with unknown dead-zone inputs using neural network. Firstly, the dead-zone outputs are firstly represented as simple linear systems with a static time-varying gain and bounded disturbance by introducing characteristic function. Secondly, in the controller design, neural networks are utilized to approximate the unknown nonlinear functions. Thirdly, an adaptive decentralized tracking control approach is constructed via backstepping design technique. It is shown that the proposed control approach can assure that all the signals of the closed-loop system semi-globally uniformly ultimately bounded and the tracking errors finally converge to a small domain around the origin. The proposed method can get precise tracking results with low computational cost, and have a good real-time performance and convergence. Finally, two examples are given to demonstrate the effectiveness of the proposed control scheme.

This paper presents a decentralized adaptive controller for a class of large-scale nonlinear systems with unknown subsystems and interactions. A direct adaptive controller is devised based on Lyapunov stability analysis so that the stability of the closed loop system is guaranteed by introducing a suitably driven adaptive rule. The adaptive controller proposed in this paper can guarantee the stability of the closed-loop system without knowing the sign of the controller coefficient. To show the effectiveness of the proposed decentralized adaptive controller, a nonlinear system is chosen as a case study. Simulation results are very promising.

Self-tuned local feedback gain based decentralized fault tolerant control for a class of large-scale nonlinear systems

In this paper, asymptotic stabilization for a class of nonlinear large-scale systems with unmatched uncertainties is investigated. Two types of robust control algorithms are proposed to stabilize the uncertain nonlinear large-scale systems asymptotically provided that the nominal systems are asymptotically stable and the unmatched uncertainties are equivalently matched. The decentralized control utilizes the local state of each subsystems as the feedback information. The two-level control uses the local state as well as the states of the state of the connecting subsystems. An example is used to illustrate the proposed control method.

formula omitted] tracking-based decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems

We present a novel decentralized tracking control scheme for a class of large-scale nonlinear systems with partial state constraints. For the first time, backstepping design with the newly proposed BLF is incorporated to effectively deal with the control problem of nonlinear systems with interconnected constraints. To prevent the states of each subsystem from violating the constraints, we employ a special barrier Lyapunov function (BLF), which grows to infinity whenever its argument approaches some finite limits. By ensuring boundedness of the barrier Lyapunov function in the closed loop, we ensure that those limits are not transgressed. Asymptotic tracking is achieved without violation of the constraints, and all closed-loop signals remain bounded. In the end, an illustrative example is presented to demonstrate the performance of the proposed control.

This paper presents a decentralized adaptive control design for a class of large-scale nonlinear systems with unknown subsystems. When the subsystems are modeled by affine equations, a direct adaptive controller is devised based on the Lyapunov theory, so that the stability of the closed-loop system is guaranteed by introducing a suitably driven adaptive rule. A neuro-based structure is proposed when the subsystems are nonaffine, and the stability analysis is also performed based on the Lyapunov theory. Moreover, the unknown interactions among the subsystems are considered as having a nonlinear function against the simple form considered for the affine case. The proposed controllers are employed in an inverted two-pendulum system, and their promising performances are illustrated.

In this paper, we consider decentralized adaptive output-feedback control problem for a class of large-scale nonlinear systems, in which the nonlinear actions within subsystems and nonlinear interactions between subsystems contain unknown time delays, and need not satisfy the matching condition. A memoryless decentralized adaptive output-feedback controller is proposed, and global stability results of the closed-loop system are proved.

This paper proposes a novel adaptive synchronization approach for a class of nonlinear large-scale systems via T-S fuzzy model. It is known that the practical large-scale systems are very complicated with nonlinear coupling strength between every two subsystems, thus the classical linear control methods are invalid for them. To overcome this deficiency, we employ the T-S fuzzy model in this paper. Firstly, the local linearization approach is utilized to preprocess the nonlinear large-scale systems, and then, according to the universal approximation property of TS fuzzy model, a new system model is established. Secondly, the fuzzy adaptive synchronization controllers are designed by using Parallel Distributed Compensation technique and the corresponding stability analysis is presented through Lyapunov technique. In addition, a new criterion, under which the proposed control scheme can be transformed into pinning control and save unnecessary costs, is also given. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.

Presents results in decentralized adaptive output feedback control for a general class of large-scale nonlinear systems in disturbed output-feedback form. Severe assumptions required in the literature such as the matching and polynomial-type growth conditions are removed. Nonlinear zero-dynamics are considered and enter every subsystem nonadditively whereas the additivity is a crucial hypothesis in most previous work in both settings of centralized and decentralized adaptive output-feedback control. The effectiveness of the proposed constructive decentralized methodology is illustrated in a practical example of two inverted pendulums on carts without velocity measurements.

Global H ∞ control for a class of interconnected nonlinear systems

In this paper, a class of nonlinear large-scale systems with similar subsystems is studied. Both matched and unmatched uncertainties are considered by utilizing their bounding functions, and the interconnections take a more general form than considered previously. Based on a constrained Lyapunov equation, a nonlinear dynamic output feedback decentralized controller is presented. Unlike existing results, matched uncertainties are considered in the control design; by using a decomposition of the interconnections, the known and uncertain interconnections are treated separately; thus, the robustness is improved and conservativeness is reduced significantly. The computation effort for solving the Lyapunov equation is greatly reduced by taking into account the similar subsystem structure. Finally, simulation is used to illustrate the effectiveness of our results.

In this paper, the problem of adaptive fuzzy decentralized optimal control is investigated for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear large-scale systems contain the unknown nonlinear functions and unmeasured states. By utilizing the fuzzy logic systems to approximate the unknown nonlinear functions and cost functions, a fuzzy state observer is established to estimate the unmeasured states. The control design is divided into two phases. First, by using the state observer and the backstepping design technique, a feedforward decentralized controller with parameters adaptive laws is designed, by which the original controlled strict-feedback nonlinear large-scale system is transformed into an equivalent affine nonlinear large-scale system. Second, by using adaptive dynamic programming theory, a feedback decentralized optimal controller is developed for the equivalent affine nonlinear system. The whole adaptive fuzzy decentralized optimal control scheme consists of a feedforward decentralized controller and a feedback decentralized optimal controller. It is shown that the proposed adaptive fuzzy decentralized optimal control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero. In addition, the proposed control approach can guarantee that the cost functions are minimized. Simulation results are given to demonstrate the effectiveness of the proposed control approach.