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Toward the aim of zero tracking error and user-specified convergence rate, a data-driven output tracking design for unknown linear discrete-time systems is investigated in this work. For policy learning, we customize a virtual auxiliary system, based on which an enhanced Bellman equation with the user-specified convergence rate is derived. This allows us not to explicitly measure the time for the system evolution, while only the input–output data are sufficient in our design. By utilizing the robust output regulation technique, we can learn an optimal tracker for such an auxiliary system via policy iteration and value iteration. It is proved that the output tracking error of the original discrete-time system converges to zero at the specified convergence rate. The effectiveness of the proposed algorithms is illustrated by a simulation example. [ABSTRACT FROM AUTHOR]

Summary: In this article, we consider the resilience analysis of the even‐based consensus control of networked Lagrangian systems under denial‐of‐service (DoS) attacks, which are usually implemented by the attackers to block the communication channels. Comparing with the linear cyber‐physical systems, the secure control of the networked Lagrangian systems is more complex and challenging, especially under the conditions of asymmetry communication and discontinuous control. To this end, an event‐based controller is first designed for consensus control of networked Lagrangian systems in the absence of DoS attacks. Sufficient conditions are given to stabilize the closed‐loop systems. Then the resilience analysis is presented for the event‐based controller under DoS attacks. Some conditions associated with the DoS duration and frequency are proposed for the control parameters against the attacks. Then Zeno behaviors are proved to be nonexistent in the proposed control scheme. An algorithm is also given to guide the control design. [ABSTRACT FROM AUTHOR]

Recent results have emerged that policy iteration is a powerful reinforcement learning tool in designing a stabilizing control policy for continuous-time systems with unknown system dynamics. Policy iteration involves a model-based initialization stage, i.e., seeking an initial stabilizing control policy, which is, however, dependent on the full system dynamics including the drift dynamics and system input matrix. To remove such model requirements, this paper utilizes a homotopy-based initialization strategy for policy iteration, wherein a stabilizing control policy for continuous-time systems is obtained by gradually moving a stable system to the original system. We propose two homotopic policy iteration-based stabilizing control schemes, namely, a model-based design and a model-free design using system data, which are proved to place unstable poles into a stable region. The effectiveness of the proposed designs is validated through an illustrative example. [ABSTRACT FROM AUTHOR]

In this work, an event-triggered coordination problem for multi-agent systems with general linear dynamics is considered over directed communication graphs with a spanning tree. We study such a problem by using a Lyapunov equation for leaderless consensus. It is shown that a Lyapunov-based approach can complete stability analysis of event-triggered consensus of multi-agent systems over general directed graphs. [ABSTRACT FROM AUTHOR]