Solving Nonlinear Optimal Control Problems using a Hybrid IPSO-SQP Algorithm


A hybrid algorithm by integrating an improved particle swarm optimization (IPSO) with successive quadratic programming (SQP), namely IPSO-SQP, is proposed for solving nonlinear optimal control problems. The particle swarm optimization (PSO) is showed to converge rapidly to a near optimum solution, but the search process will become very slow around global optimum. On the contrary, the ability of SQP is weak to escape local optimum but can achieve faster convergent speed around global optimum and the convergent accuracy can be higher. Hence, in the proposed method, at the beginning stage of search process, a PSO algorithm is employed to find a near optimum solution. In this case, an improved PSO (IPSO) algorithm is used to enhance global search ability and convergence speed of algorithm. When the change in fitness value is smaller than a predefined value, the searching process is switched to SQP to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To validate the performance of the proposed IPSO-SQP approach, it is evaluated on two optimal control problems. Results show that the performance of the proposed algorithm is satisfactory.


Electrical and Computer Engineering

Keywords and Phrases

Convergence Speed; Convergent Speed; Fitness Values; Global Optimum; Global Search Ability; Hybrid Algorithms; Improved Particle Swarm Optimization; Improved PSO; Inertia Weight; Local Optima; Near Optimum; Non-Linear Optimal Control; Optimal Control; Optimal Control Problem; Optimum Solution; PSO Algorithms; Search Process; SQP Algorithm; Successive Quadratic Programming; Algorithms; Control; Convergence of Numerical Methods; Quadratic Programming; Particle Swarm Optimization (PSO); Optimization; Particle Swarm Optimization

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Article - Journal

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© 2011 Elsevier, All rights reserved.

Publication Date

01 Apr 2011