An Implicit Iterative Algorithm for Linear Quadratic Optimal Control Problem

Meijun Liu, Xueyan Zhao
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Abstract

This paper investigates linear quadratic regulator(LQR) problem of linear stochastic systems. An novel algorithm is proposed to solve the general algebraic Riccati equation(GARE) derived from linear stochastic systems based on successive over-relaxation technique. With some initial conditions satisfied, the monotone convergence can be guaranteed, and an initial value selection method is proposed. Furthermore, a numerical example is given to demonstrate the effectiveness of the algorithm.
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线性二次型最优控制问题的隐式迭代算法
研究了线性随机系统的线性二次型调节器问题。提出了一种基于逐次过松弛技术求解线性随机系统广义代数Riccati方程的新算法。在满足一定初始条件的情况下,保证了算法的单调收敛性,并提出了一种初始值选择方法。最后通过数值算例验证了该算法的有效性。
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