Approximate Finite-horizon Optimal Control with Policy Iteration

摘要：

　　In this paper,the policy iteration algorithm for the finite-horizon optimal control of continuous time systems is addressed.The finite-horizon optimal control with input constraints is formulated in the Hamilton-Jacobi-Bellman(HJB)equation by using a suitable nonquadratic function.The value function of the HJB equation is obtained by solving a sequence of cost functions satisfying the generalized HJB(GHJB)equations with policy iteration.The convergence of the policy iteration algorithm is proved and the admissibility of each iterative policy is discussed.Using the least squares method with neural networks(NN)approximation of the cost function,the approximate solution of the GHJB equation converges uniformly to that of the HJB equation.A numerical example is given to illustrate the result.

关键词： Finite-horizon Policy Iteration Input Constraints Neural Networks Approximation HJB Equation Least Squares

作者: ZHAO Zhengen YANG Ying LI Hao LIU Dan

作者单位: State Key Lab for Turbulence and Complex Systems,Department of Mechanics & Engineering Science,College of Engineering Peking University,Beijing 100871,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 8895-8900

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）