Event-based iterative neural control for a type of discrete dynamic plant

With the widespread popularity of network-based techniques and extension of computer control scales, more dynamical systems, particularly complex nonlinear dynamics, including increasing communication burdens, increasing difficulties in building accurate mathematical models, and different uncertain factors are encountered. Consequently, in contrast to the linear case, the optimization of the design of these uncertain complex systems is difficult to achieve. By combining reinforcement learning, neural networks, and dynamic programming, the adaptive critic method is regarded as an advanced approach to address intelligent control problems. The adaptive critic method has been currently used to solve the optimal regulation, trajectory tracking, robust control, disturbance attenuation, and zero-sum game problems. It has been considered a promising direction within the artificial intelligence field. However, many traditional design processes of the adaptive critic method are conducted based on the time-based mechanism, where the control signals are updated at each time step. Thus, the related control efficiencies are often low, which results in poor performance when considering practical updating times. Hence, more improvements are needed to enhance the control efficiency of adaptive-critic-based nonlinear control design. In this study, we developed an event-based iterative neural control framework for discrete-time nonlinear dynamics. The iterative adaptive critic method was combined with the event-driven mechanism to address the approximate optimal regulation problem in discrete-time nonlinear plants. An event-triggered value learning strategy was established with two iterative sequences. The convergence analysis of the iterative algorithm and the neural network implementation of the new framework were presented in detail. Therein, the heuristic dynamic programming technique was employed under the event-based iterative environment. Moreover, the triggering condition of the event-driven approach was determined with the appropriate threshold. Finally, simulation examples were provided to illustrate the excellent control performance, particularly in utilizing the communication resource. Thus, constructing a class of intelligent control systems based on the event-based mechanism will be helpful.