KEYWORDS: Control systems, Data modeling, Telecommunications, Systems modeling, Quantization, Matrices, Data transmission, Data communications, Algorithms, Adaptive control
For the nonlinear discrete-time Multi-agent system of unknown dynamic models, there are cooperation and competition between agents, and the problem of data quantification in communication, a model-free adaptive iterative control (MFAILC) algorithm is proposed. First, the method of compact form dynamic linearization (CFDL) is used to transform the agent system into a model with time-varying parameters, and the quantizer is applied to quantize the data in the process of processing, and the cooperation-competition relationship between multi-agents is considered in algebraic graph theory, on this basis, the MFAILC control algorithm is designed and the convergence of the proposed algorithm is proved. Finally, the simulation results verify the effectiveness of the proposed algorithm.
Traditional fractional-order controller (FOPID) parameter tuning methods are mainly based on amplitude margin and phase angle margin in the frequency domain, and there are problems such as low parameter tuning efficiency and low accuracy. This paper proposes a parameter optimization method based on the multi-objective particle swarm optimization algorithm MOPSO. By rotating the Hankel matrix to approximate the fractional-order operator, the system fractional-order differential equation satisfied by the fractional-order controller parameters is transformed into an algebraic differential equation Using MOPSO to optimize controller parameters. Experimental results show that the design method improves the dynamic performance of the system and makes the system have good robustness.
In this paper, we study the leader-following consensus problem of general linear multi-agent systems under distributed adaptive event-triggered control. A distributed controller with estimated states is designed, and an adaptive event-triggered communication protocol with auxiliary variables is proposed for each agent to adjust its triggering threshold. Under this control strategy, the system only needs to use the estimated states at the moment of triggering. Compared with the traditional static threshold, the existence of dynamic threshold reduces the number of triggers. Also, an estimator is designed between triggering moments to reduce the state deviation. The next state of each agent depends on local information about itself and its neighbors rather than global information. In addition, it is shown that the multi-agent systems can reach consensus without Zeno behavior. Finally, the effectiveness and feasibility of the proposed method are verified by numerical simulations.
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