Computational Optimal Control

Dr S Subchan, Cranfield University, UKDr Subchan is a research officer in the Department of Aerospace, Power and Sensors at Cranfield University's Shrivenham campus. He studied for his PhD at Cranfield in 2005, having previously worked for 3 years for Indonesian Aircraft Industries at Bandung on computational fluid dynamics.Dr Rafal Zbikowski, Cranfield University, UKDr Zbikowski is Reader in Control Engineering and is a Principal Research Officer in the Guidance and Control Group of the Department of Aerospace, Power and Sensors at Cranfield University's Shrivenham campus. Prior to this he was a Post-Doctoral Research Fellow at Glasgow University leading the Glasgow side of a joint project with Daimler-Benz on neural adaptive control technology for Mercedes products. Dr Zbikowski has published over fifty papers on adaptive and nonlinear aspects of intelligent control, and flapping wing micro air vehicles. A control engineer with a strong mathematical background, he is a member of the American Mathematical Society (AMS) and the Institute of Electrical and Electronics Engineers (IEEE). Since 1998 he has been instrumental in obtaining UK funding for four substantial projects in the area of guidance and control.

• Preface AcknowledgementsNomenclature1 Introduction1.1 Historical Context of Computational Optimal Control1.2 Problem Formulation1.3 Outline of the Book2 Optimal Control: Outline of the Theory and Computation2.1 Optimisation: From Finite to Infinite Dimension2.2 The Optimal Control Problem2.3 Variational Approach to Problem Solution2.4 Nonlinear Programming Approach to Solution 2.5 Numerical Solution of the Optimal Control Problem.2.6 Summary and Discussion3 Minimum Altitude Formulation3.1 Minimum Altitude Problem3.2 Qualitative Analysis3.3 Mathematical Analysis3.4 Indirect Method Solution3.5 Summary and Discussion4 Minimum Time Formulation4.1 Minimum Time Problem4.2 Qualitative Analysis4.3 Mathematical Analysis4.4 Indirect Method Solutions4.5 Summary and Discussion5 Software Implementation5.1 DIRCOL implementation5.2 NUDOCCCS Implementation5.3 GESOP (PROMIS/SOCS) Implementation5.4 BNDSCO Implementation5.5 User Experience6 Conclusions and Recommendations6.1 Three-stage Manual Hybrid Approach6.2 Generating an Initial Guess: Homotopy6.3 Pure State Constraint and Multi-objective Formulation6.4 Final RemarksAppendix BNDSCO Benchmark ExampleA.1 Analytic SolutionA.1.1 Unconstrained or Free Arc (l ≥1/4)A.1.2 Touch Point Case (1/6 ≤l ≤1/4)A.1.3 Constrained Arc Case (0 ≤l ≤1/6)BibliographyIndex