No abstract

This paper proposes a novel load frequency control (LFC) strategy for power systems based on distributed model‐free adaptive predictive control. First, a power system dynamic model is established by the input and output signals of the power system. Then, a distributed model‐free adaptive predictive control algorithm is established for the power system under denial‐of‐service attack by the established power system dynamic model, and a predictive compensation algorithm is designed to compensate the impact of DoS attack. Based on the designed distributed model‐free adaptive predictive control algorithm, the frequency tracking error of the multiarea power system is bounded. The scheme is independent of the structure of the power system and does not need to measure any state signals of the power system, relying only on the input and output data of the power system. The results of simulations demonstrate the effectiveness and superiority of the design.

This paper studies the iterative learning control (ILC) for a class of second‐order nonlinear hyperbolic impulsive partial differential systems. Firstly, to follow the discontinuous desired output, a P‐type learning law is adopted, and sufficient conditions for the convergence of the tracking error is established under identified initial state value. The rigorous analysis is also given using the impulsive Gronwall inequality. Secondly, the tracking error of output trajectory is considered in systems with state initial values shifting based on an initial learning algorithm. These results of this paper show that the tracking error on the finite time interval can uniform converge to 0 as the iteration index goes to infinity if impulse number of the systems is only a finite numbers. Finally, two numerical simulation examples are given to verify the effectiveness of the theoretical results.

No abstract

No abstract