This paper proposes two algorithms for the simultaneous identification of time-delay and rational transfer function of continuous time systems. The algorithms, which include the projection algorithm with momentum term...
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This paper proposes two algorithms for the simultaneous identification of time-delay and rational transfer function of continuous time systems. The algorithms, which include the projection algorithm with momentum term and the least-squares algorithm with momentum term obtained from different cost functions, are simple and derivative-free for practical realization. The identification scheme can be applied to the systems with variable time delay. The simulation results demonstrate the proposed methods can successfully estimate the unknown time delay and rational dynamics of the systems even in the presence of disturbance noise.
In this paper, a distributed adaptive control method is considered for a class of discrete-time multi-agent systems with nonlinearity and uncertainty. Each agent is affected by its neighbors, and there is a hidden age...
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In this paper, a distributed adaptive control method is considered for a class of discrete-time multi-agent systems with nonlinearity and uncertainty. Each agent is affected by its neighbors, and there is a hidden agent as the leader in multi-agent systems who knows the desired reference signal. However, other agents are aware of neither the reference signal nor the existence of the hidden leader and leadership. In order to deal with uncertainty, a criteria function for each agent, which is consist of a weighted square combination of state errors and parameter errors with timevarying weighting factor, is adopted. By minimizing the criteria function, we propose a projection algorithm for each agent to estimate unknown parameters. Furthermore, we design a distributed adaptive controller for each agent using the information of its neighbors. Under the distributed adaptive control, the rigorous mathematical proof is presented to demonstrate that all the agents ultimately track the desired reference signal. Finally, simulation results are given to illustrate the theoretical results.
We study two projection algorithms for solving the variational inequality problem in Hilbert space. One algorithm is a modified subgradient extragradient method in which an additional projection onto the intersection ...
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We study two projection algorithms for solving the variational inequality problem in Hilbert space. One algorithm is a modified subgradient extragradient method in which an additional projection onto the intersection of two half-spaces is employed. Another algorithm is based on the shrinking projection method. We establish strong convergence theorems for both algorithms.
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