Hybrid Optimization Methods with Enhanced Convergence Ability

This chapter introduces a new hybrid optimal control solver to solve the constrained SMV trajectory optimization problem. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the effectiveness and quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability, when compared with other typical optimal control solvers. A Monte Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.