This paper proposes an event-based near-optimal control algorithm for nonaffine discrete-time systems with constrained inputs. The method is derived from the dual heuristic dynamic programming (DHP) technique. The challenge caused by saturating actuators is overcome by using a nonquadratic performance index. Then, the event-based control technique is used to decrease the amount of computation. Meanwhile, the stability analysis is provided. It illustrates that the proposed event-based method can asymptotically stabilize the nonaffine systems by using the Lyapunov method. Furthermore, the stability conditions and the design process of the event-based controller are established. The event-based DHP algorithm is implemented by constructing three neural networks, namely, the model network, the critic network, and the action network. Finally, simulation studies are conducted to demonstrate the applicability and the performance of the proposed method.

针对输入受限的非仿射离散时间系统，提出了一种基于事件的近似最优控制算法。该方法源自双重启发式动态规划（DHP）技术。通过使用非二次性能指标可以克服致动器饱和所带来的挑战。然后，基于事件的控制技术用于减少计算量。同时，提供了稳定性分析。它说明了所提出的基于事件的方法可以通过使用Lyapunov方法渐近稳定非仿射系统。此外，建立了基于事件的控制器的稳定性条件和设计过程。基于事件的DHP算法是通过构建三个神经网络来实现的，即模型网络，评论者网络和动作网络。最后，