Tham khảo tài liệu 'advances in flight control systems part 6', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Application of Evolutionary Computing in Control Allocation 87 attributes of GA are mutation and cross over. A good cross over rate is expected to take better parts of parent genes to the next generation. Mutation on the other hand changes the individuals and if it is kept to a safe low level it helps the population to avoid falling in local minima. This makes GA different from other optimisers and particularly suitable for non-convex optimisation problems like the compensator parameter optimisation in this research. The main disadvantage linked with GA is the higher computation time and required resources but this can be avoided if there is a possibility to stop the GA anytime in the routine. Also with the ever increasing processing power of computers over time this constraint diminishes. Optimizing routine using GA Numerically the optimizing problem is given as Find M by minimizing . min 31 where M is a diagonal gain matrix of dimension 11X11 . The GA optimising routine is formulated by using the MATLAB Genetic Algorithm Direct Search Toolbox. A flow chart representation of the optimisation routine is shown in Fig. 12. Fig. 12. Flow chart for tuning compensator parameters using GA 88 Advances in Flight Control Systems The complete process shown in Fig. 11 can be summarised as The GA main function calls the evaluation function giving searched parameters to calculate compensator parameters The evaluation function calculates compensator parameters and calls the simulation model giving the parameters for the compensator The simulation model runs the simulation for the given compensator parameter . individual of population and returns the value of error between ucmd and actual u The evaluation function calculates the cost function value for given errors and returns to the main GA function This is repeated for the total number of genes in one generation population and then one generation completes and so the remaining generations are iteratively completed The .
