Neural-network-based output feedback control for networked multirate systems: A bit rate allocation scheme - Zhang, Yuhan; Zou, Lei; Song, Baoye; Zhao, Zhongyi; Wang, Yezheng (Information sciences, 2023)
This paper deals with the neural-network (NN)-based output feedback control problem for a class of networked systems with unknown nonlinearities under the effects of bit rate constraints. Considering the physical conditions/requirements in practical applications, the sampling period of sensors is assumed to be different from the state updating period of the system. For the purpose of facilitating digital communications over networks, a group of encoders is utilized to convert the measurement signal into codewords with limited bit lengths. A so-called bit rate constraint is introduced to capture the bandwidth-limited nature of communication network. To handle the unknown nonlinearity of the multirate system, both the NN-based observer and NN-based controller are designed to generate the desired state estimates and control input signals, respectively. Then, a unified framework is established to analyze the boundedness of the estimation error and system state as well as the neural network weights. The effects of the bit rate constraint on the resultant control performance is also analyzed. Subsequently, sufficient conditions are derived to guarantee the existence of the required NN-based output feedback controller. A particle-swarm-optimization-based (PSO-based) algorithm is developed to co-design the desired controller parameter and the bit rate allocation strategy. Finally, an illustrative example is given to verify the effectiveness of the proposed control strategy. •The neural-network-based output feedback control problem is, for the first time, investigated for a class of nonlinear multirate systems subject to bit rate constraints.•A particle-swarm-optimization (PSO)-based adaptive dynamic programming algorithm is proposed to allocate the constrained bit rate for each sensor and achieve the desired control policy simultaneously.•The boundedness for the closed-loop system and estimation errors of system state and the neural-network weights are analyzed in a unified framework.