Development of a GA-Fuzzy-Immune PID Controller with Incomplete Derivation for Robot Dexterous Hand

Abstract

In order to improve the performance of robot dexterous hand, a controller based on GA-fuzzy-immune PID was designed. The control system of a robot dexterous hand and mathematical model of an index finger were presented. Moreover, immune mechanism was applied to the controller design and an improved approach through integration of GA and fuzzy inference was proposed to realize parameters' optimization. Finally, a simulation example was provided and the designed controller was proved ideal.

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

“…When antigens invade the body, the constituent elements of the lymphocyte helper T cell T H , the suppressor T cell T S , and the B cell cooperate with each other for the balance of immune feedback system. Based on the adaptive mechanism of the immune process, the increment PID controller, and the fuzzy control method, the FI-PID controller was designed by taking the number of antigens as error, the total stimulus received by B cells as the control signal or control law [51,52]. Meanwhile, the controller introduces two variables namely the rate of reaction K and the coefficient of stabilizing effect η, and set…”

“…For ATO system or other similar systems, the purpose of parameter tuning is to reduce the deviation between input and output. Errors were widely discussed in parameter optimization [9,10,51,53,54]. Taking error as the basic point, a multiobjective optimization model was put forward from the three aspects of train's operation, including overall operation, initial acceleration phase, braking, and stopping phase.…”

“…Fuzzy controller Process database [7,8,21,51,54]. NSGA-II is a multiobjective optimization method derived from GA with an elite preservation strategy and a fast-nondominated sorting method [7,55,56].…”

An MEA‐Tuning Method for Design of the PID Controller

“…With the development of the intelligent control theory, intelligent optimization algorithms started to be applied in PID parameter tuning and optimizing and achieved incomparable results that the conventional parameter tuning methods cannot obtain. For example, the genetic algorithm (GA) [14][15][16], particle swarm optimization (PSO) [17][18][19], tabu search algorithm (TSA) [20][21][22], bacterial foraging algorithm (BFA) [23][24][25], ant colony algorithm (ACA) [26], artificial bee colony (ABC) algorithm [27], and BAT search algorithm [28] were adopted to optimize PID controller parameters and had achieved much better performances.…”

Fuzzy PID control method of deburring industrial robots

“…The robot program, which contains the grinding path generated by a PC, was demonstrated [20]. A controller based on a GA-fuzzyimmune PID, a robot dexterous hand control system, and a mathematical model of an index finger were also presented [17]. Co-simulations of a novel exoskeletonhuman robot system based on humanoid gaits with Fuzzy PID algorithms was presented [3].…”