Local Feedback Gain Research Articles

Inferring State-feedback Cooperative Control of Networked Dynamical Systems

In this paper, we study the problem of inferring the state-feedback cooperative control of continuous-time NDSs from noisy state observations. To practice, we first propose a causality-based estimator to obtain the discretized system matrix, adaptive to both stable and explosive state evolution cases. Then, we derive the observation period guarantees and leverage the matrix logarithm to accurately reconstruct the continuous closed-loop matrix from the discrete one, circumventing the insufficiency of conventional sampling-recovery methods in this situation (e.g., Shannon sampling theorem). Finally, we exploit the element-wise coupling relation between the local feedback gain and the unknown interaction topology, and construct a duel-level least squares method to obtain the feedback matrix. Simulations are conducted to verify the proposed inference method.

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

In this paper, a decentralized fault tolerant control (DFTC) scheme is proposed for a class of large-scale nonlinear systems based on self-tuned local feedback gain against partial loss of actuator effectiveness (PLOAE). Consider a large-scale nonlinear system as a set of interconnected subsystems, a decentralized control method is proposed by employing two radial basis function neural networks (RBFNNs) for the fault-free system. Then, the unknown system is identified using RBFNNs. By establishing a decentralized observer, the derived self-tuned local feedback gain is placed before the proposed decentralized controller to guarantee control performance for the subsystem suffering from PLOAE fault. Finally, simulation examples are provided to demonstrate the effectiveness of the present DFTC scheme. The main contributions of this paper are: i) The unknown large-scale nonlinear system can be identified using locally measured states, so the actuator fault can be handled in its local subsystem. It implies that the performance degradation of the faulty subsystem cannot affect the fault-free subsystems. ii) The estimated effectiveness factor is placed before the proposed decentralized scheme. The fault tolerant control structure is simple since it does not need to be redesigned in the case of PLOAE.

Optimization of formation for multi-agent systems based on LQR

In this paper, three optimal linear formation control algorithms are proposed for first-order linear multiagent systems from a linear quadratic regulator (LQR) perspective with cost functions consisting of both interaction energy cost and individual energy cost, because both the collective object (such as formation or consensus) and the individual goal of each agent are very important for the overall system. First, we propose the optimal formation algorithm for first-order multi-agent systems without initial physical couplings. The optimal control parameter matrix of the algorithm is the solution to an algebraic Riccati equation (ARE). It is shown that the matrix is the sum of a Laplacian matrix and a positive definite diagonal matrix. Next, for physically interconnected multi-agent systems, the optimal formation algorithm is presented, and the corresponding parameter matrix is given from the solution to a group of quadratic equations with one unknown. Finally, if the communication topology between agents is fixed, the local feedback gain is obtained from the solution to a quadratic equation with one unknown. The equation is derived from the derivative of the cost function with respect to the local feedback gain. Numerical examples are provided to validate the effectiveness of the proposed approaches and to illustrate the geometrical performances of multi-agent systems.

Self-tuned Local Feedback Gain Based Decentralized Fault Tolerant Control of Reconfigurable Manipulators

Self-tuned Local Feedback Gain Based Decentralized Fault Tolerant Control of Reconfigurable Manipulators

Cooperation of multiple mobile sensors with minimum energy cost for mobility and communication

This paper investigates a minimum energy cooperation control problem in mobile sensor networks. Assuming a time-invariant undirected network topology for the sensors, we present a suboptimal solution that guarantees minimum overall energy cost of mobility and communication for the sensors to achieve consensus. The design procedure of the distributed control protocol is composed of two steps. The first step yields the local feedback gain via solving a linear quadratic regulating (LQR) problem, while the second step produces the network feedback gain based on convex optimization technique. The results are extended to formation control problems with and without communication delays. The effectiveness of the proposed methodology is illustrated by numerical examples.

A Novel Stabilization Criterion for Large-Scale T–S Fuzzy Systems

This correspondence studies the stabilization problem of large-scale Takagi-Sugeno fuzzy systems. A novel stabilization criterion with decentralized parallel distributed compensation (PDC) fuzzy controller is proposed. The criterion contains two inequalities and one negative definite matrix to be satisfied. The effects of all interconnection terms and all decentralized PDC gains are entirely included in the negative definite matrix. The size of the matrix depends on the number of subsystems; the more the number of subsystems is, the larger the size of the matrix is. By using the linear matrix inequality method, the inequalities in the criterion can be solved to synthesize the local feedback gain of each PDC fuzzy controller such that the whole closed-loop large-scale fuzzy system is asymptotically stable. Finally, we give a practical example to illustrate the effectiveness of the proposed criterion.

Stability and Stabilization of Fuzzy Large-Scale Systems

This paper investigates the stability and stabilization problems for the fuzzy large-scale system in which the system is composed of a number of Takagi-Sugeno fuzzy model subsystems with interconnections. The stability criterion of the fuzzy large-scale system without control is derived first. Instead of fuzzy parallel distributed compensation design, linear state feedback control is used in the stabilization work. Each local control is obtained from Riccati equation of the corresponding subsystem and the interconnection terms are used to determine the local feedback gain determination. Based on Lyapunov criterion, some conditions are derived under which the whole fuzzy large-scale system is stabilized asymptotically. A numerical example is given to illustrate the control design procedure and its effectiveness.

Decentralized exciter stabilizing control for multimachine power systems

AbstractIn this paper, a systematic approach to design controllers based on H‐infinity theory for a multimachine power system is presented. Generally, a centralized control scheme is difficult for a large‐scale interconnected power system because of the necessity of obtaining information on the whole system, computation times, and so on. In order to handle these problems, two decentralized control schemes are proposed. One is based on the decomposition of information. The feedback gains for the whole system are obtained, and after decomposing the gains into subblocks for each area, the diagonal block is used to design the controller for each generator. The other is based on area decompositions. The procedure is carried out by decomposing the original system into blocks for each area and the local feedback gain is obtained by using information for each decomposed system. Furthermore, to improve the robustness of the system, an effective weighting matrix design, which involves the allocation of eigenvalues, is also proposed. Several simulation tests show the effectiveness of the proposed methods. © 2002 Scripta Technica, Electr Eng Jpn, 139(1): 35–43, 2002; DOI 10.1002/eej.1144

Local State Covariance Assignment for Stochastic Large-Scale Systems

Based on the concept of variable structure control, this paper investigates the local state covariance assignment problem for stochastic large-scale systems. By using the invariance property of variable structure systems, the interconnection terms with matching condition will disappear on the sliding mode. With the aid of Ito-formula, the hitting controller of each subsystem is derived. Combining the sliding phase and hitting phase of the system design, the local feedback gain matrix Gi for each subsystem is obtained to achieve the local state covariance assignment.