Method of inequalities-based multiobjective genetic algorithm for optimizing a cart-double-pendulum system

Abstract

This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system (CDPS). The designed controller, which is based on the human-simulated intelligent control (HSIC) method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based (MoI) multiobjective genetic algorithm (MMGA) is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.

Curve surface fitting based on an improved genetic algorithm

“…The normal curvature information of the four corners of each surface patch meets (9), which can get four constraint equations. Two of them are selected to establish the nonlinear equations.…”

“…The nonlinear equations were solved by using the improved genetic algorithm to obtain the parametric equation [9]. The genetic algorithm calculation result mainly depends on the establishment of the objective function, variable coding, fitness function design, genetic operator design, and so on.…”

An investigation on the design and performance assessment of double-PID and LQR controllers for the inverted pendulum

“…The linear motor adopts the zero d-axis current control method, thus the applied force can be simplified as (5).…”

Intelligently tuned wavelet parameters for GPS/INS error estimation

“…Utilizing this method ensures that the new population will contain chromosomes with better fitness values than the worst individual in the old population. This permutation will reduce the iteration required for the learning process and ensure good guidance in the complex and nonlinear search space [23,24] . Furthermore, the two best parents from the old population will be copied into the next generation without performing any additional operations to increase the probability of obtaining best fitness values and prevent the learning process from becoming worse compared with that of the previous generation.…”

“…Furthermore, the two best parents from the old population will be copied into the next generation without performing any additional operations to increase the probability of obtaining best fitness values and prevent the learning process from becoming worse compared with that of the previous generation. Single-point crossover was used for each chromosome of the chromosome-pair where the genes between the selected points are swapped to create a new pair of chromosomes [23] . Finding a suitable method for encoding the chromosome is the most important and successful key for many applications.…”