Improved genetic algorithm for parameters identification of cart-double pendulum

Cart-double pendulum is a typical nonlinear under-actuated mechanical device, and highly sensitive to modeling error and external disturbance. Motion control of double pendulum is a very complex and difficult task, especially when it turns to real-time situation because a minor difference between kinetic model and actual device could lead to the necessity of significant adjustments according to control laws comparing to the usage of simulation controller. The use of accurate kinetic model is the key factor for fast transition from simulation to real-time operation. An improved genetic algorithm (IGA), which includes orthogonal experiment design, feedback mutation, pairing operation based on Hamming distance, and variable precision crossover operation, is introduced to identify the physical parameters of a double pendulum. The improved genetic algorithm (IGA) can overcome the shortcomings of the traditional genetic algorithm (TGA), for instance, premature convergence, and get results closer to the global optimal solution. The results of experiments demonstrate the effectiveness of the proposed method.