Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems With Trajectory-Based Initial Control Policy
提出OptNet-PGADP算法，先用非线性模型预测控制计算系统轨迹，再用OptNet获取初始可行控制策略，最后通过策略梯度自适应动态规划迭代优化，实现离散时间非线性系统的最优控制，仿真实验表明性能优于传统方法。
The policy gradient adaptive dynamic programming (PGADP) technique has gained recognition as an effective approach for optimizing the performance of nonlinear systems. Nonetheless, existing PGADP algorithms often demand a substantial volume of expensive or potentially risky interaction data with the system. Moreover, the utilization of neural networks in these algorithms can result in suboptimal learning efficiency and unstable training procedures. To address these challenges, a novel algorithm, referred to as OptNet-PGADP, has been introduced. This algorithm integrates an initially tailored control policy based on OptNet to tackle the optimization of control problems in discrete-time nonlinear systems. The OptNet-PGADP algorithm operates through a two-step process. Initially, the input–output trajectory of the system is computed using the nonlinear model predictive control (NMPC) method. Subsequently, an initial admissible control policy is acquired through OptNet. This policy is iteratively enhanced using the PGADP algorithm to attain the optimal controller. The resulting closed-loop control policy can be readily deployed in real-time applications. The implementation of the algorithm employs OptNet for the actor network and integrates an experience replay mechanism to bolster the controller’s learning efficiency. Furthermore, a convergence and optimality analysis of the algorithm is included. Simulation and experimental results conducted on two nonlinear systems conclusively demonstrate that the approach outperforms traditional PGADP and NMPC algorithms. These findings underscore the efficacy of OptNet-PGADP in mitigating the constraints of current methods and achieving superior control performance for nonlinear systems.