论文标题
具有常见的非线性MPC最佳反馈法律的状态空间集
State space sets with common optimal feedback laws for nonlinear MPC
论文作者
论文摘要
在模型预测控制（MPC）中，为当前状态解决了最佳控制问题（OCP），并将解决方案的第一个输入（最佳反馈定律）应用于系统。此过程需要在每个时间步骤中求解OCP。最近，提出了一种新方法，用于线性MPC。线性二次OCP的参数解决方案是一条分段式反馈定律。在状态空间中的某个点的解决方案提供了最佳的反馈定律和该定律是最佳解决方案的领域。只要系统保留在域中，就可以重复使用法律并避免OCP的计算。在某些领域，最佳反馈定律是相同的。通过团结相应的域，可以实现更大的域，并且可以更频繁地重复使用最佳反馈定律。在本文中，我们研究了该方法可以从线性延伸到非线性MPC多远，我们提出了一种算法，并用一个示例说明了实现的节省。
In model predictive control (MPC), an optimal control problem (OCP) is solved for the current state and the first input of the solution, the optimal feedback law, is applied to the system. This procedure requires to solve the OCP in every time step. Recently, a new approach was suggested for linear MPC. The parametric solution of a linear quadratic OCP is a piecewise-affine feedback law. The solution at a point in state space provides an optimal feedback law and a domain on which this law is the optimal solution. As long as the system remains in the domain, the law can be reused and the calculation of an OCP is avoided. In some domains the optimal feedback laws are identical. By uniting the corresponding domains, bigger domains are achieved and the optimal feedback law can be reused more often. In the present paper, we investigate in how far this approach can be extended from linear to nonlinear MPC, we propose an algorithm and we illustrate the achieved savings with an example.
