| 研究実績の概要 |
Consensus-based stabilization studies of multi-agent systems are proposed in this project. In particular, a memory-based sampled-data consensus framework for multi-agent systems in the presence of nonlinear actuator faults is studied. To reduce state exchanges and conserve energy resources, communication between neighboring agents is based solely on state samples at variable sampling intervals. As two general constraints of the actuator, both bounded nonlinear partial loss of effectiveness and bias failure are considered in the problem formulation. Sufficient conditions to guarantee consensus under certain circumstances are derived as linear matrix inequality conditions. Unlike existing Lyapunov-Krasovskii-based methods, the design framework proposed in this brief is based on a loop-functional approach that reduces conservatism in the design of the required consensus control gains. This less conservative approach allows for larger sampling intervals as well as more severe actuator failures, increasing the utility of the proposed approach. To obtain simulation results, the MATLAB Yalmip parser and SDPT3 solver are effectively used in this project. Simulation results based on tunnel diode circuit and a nonholonomic mobile robot MAS quantifies the effectiveness of the proposed approach. Additionally, the gain matrix of the sampled data controller is obtained by solving the inequality of the derived linear matrix. Moreover, robust exponential stability and Takagi-Sugeno fuzzy control synthesis for networked control systems via H∞ performance has been investigated.
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