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摘要: 面向離散時間非線性動態系統，提出一種基于事件的迭代神經控制框架。主要目標是將迭代自適應評判方法與事件驅動機制結合起來，以解決離散時間非線性系統的近似最優調節問題。首先，構造兩個迭代序列并建立一種事件觸發的值學習策略。其次，詳細給出迭代算法的收斂性分析和新型框架的神經網絡實現。這里是在基于事件的迭代環境下實施啟發式動態規劃技術。此外，通過設計適當的閾值以確定事件驅動方法的觸發條件。最后，借助兩個仿真實例驗證本文控制方案的優越性能，尤其是在通信資源的利用方面。本文的工作有助于構建一類事件驅動機制下的智能控制系統.Abstract: 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.
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圖 1 離散動態系統基于事件的迭代HDP框架簡圖
Figure 1. Simple diagram of the event-based iterative heuristic dynamic programming (HDP) framework with discrete dynamic plants
圖 2 執行迭代HDP算法之后的事件驅動控制實現過程
Figure 2. Event-based control implementation process after conducting the iterative HDP algorithm
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