SummaryIn this article, considering one type of fuzzy discrete‐time networked control systems (NCSs) under stochastic communication protocol (SCP), a fuzzy‐based nonfragile H∞ filter is developed to detect the subsistent fault signal. The Takagi‐Sugeno (T‐S) mathematical model is employed to approximate the nonlinearities in the concerned fuzzy NCSs. The SCP is adopted to decide which sensor gets the access to the communication network at certain time instant, and the scheduling model of which is constructed as a Markov chain. Taking into account that the filter gain parameters in real practice may suffer fluctuations during the implementation, a modified nonfragile fuzzy filter is designed to detect the fault occurred in the signal transmission. By using the strong centralized stochastic analysis technique and the matrix calculation method, a Lyapunov function is adopted to derive sufficient conditions under which the filtering error dynamics is stochastically stable and the H∞ performance is satisfied. Then, the desired nonfragile fuzzy fault detection filter is realized by solving a certain linear matrix inequality. Finally, the effectiveness of the develop fault detection scheme is verified in the simulation example.
The traditional deep deterministic policy gradient (DDPG) algorithm has the disadvantages of slow convergence velocity and ease of falling into the local optimum. From these two perspectives, a DDPG algorithm based on the double network prioritized experience replay mechanism (DNPER-DDPG) is proposed in this paper. Firstly, the value function is approximated by introducing the idea of two neural networks, and the minimum of the action value functions generated by the two networks is selected as the updated value of the actor policy network, which reduces the incidence of local optimal policy. Then, the Q values obtained by the two networks and the immediate reward obtained by the environment are used as the criteria for prioritization, and the importance of the samples in the experience replay mechanism is divided to improve the convergence speed of the algorithm. Finally, the improved method is demonstrated in the classic control environment of OpenAI Gym, and the results show that the proposed method has increased convergence speed and cumulative reward compared with the comparison algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.