Abstract
We present a preconditioned interior-point algorithm tailored for input constrained quadratic programmings (QPs) arising in optimal control problems (OCPs). The implicit approach to OCPs results in large sparse QPs, which we utilized by a tailored Riccati recursion algorithm. The Riccati recursion algorithm requires the solution of a set of small dense linear sub-systems of equations. The proposed preconditioner is an easily invertible diagonal matrix, which we apply in every linear sub-system of equations. We solve a target tracking OCP for a linearized modified quadruple tank system in Matlab. The computational results indicate that ill-conditioning in the sub-systems are reduced and that the additional CPU time for preconditioning is negligible. Additionally, the paper presents a detailed description of the proposed algorithm and serves as an implementation guide for the algorithm.
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