Research on a Multi-Motor Coordinated Control Strategy Based on Fuzzy Ring Network Control

Abstract

Aiming at the multi-motor coordinated control of intelligent robots, cross-coupling ring control based on fuzzy theory (CRCF) is proposed. Each motor is cross-coupled with two adjacent motors to form a multi-motor ring network structure that eliminates lag in speed tracking between motors and enhances synchronization between motors. Applying fuzzy control to the cross-coupling control of the proposed control strategy, the speed error and change in speed error of two adjacent motors are used as the input of the fuzzy controller, and its output is used as the input of the second of the two adjacent motors. The fuzzy controller achieves real-time tracking adjustment of the speed error between motors. The controllability and observability of the proposed control strategy are verified by the theorem of the Kalman matrix rank criterion. The external stability and internal stability are verified using an impulse response matrix. The theoretical analysis of the lag is verified by establishing a physical and mathematical model. Finally, a fourmotor coordination control system model is built. By comparison with the non-ring network proportional cross-coupling control method, the effectiveness of the proposed control strategy is experimentally verified by the MATLAB/Simulink and the RT-LAB real-time simulation platform. INDEX TERMS Multi-motor coordinated control, ring network control, fuzzy control, stability analysis, lag analysis.

Smooth velocity tracking and kinematic-based fuzzy control of carrying mobile robots

“…The velocity graph computed using the method in [2] has 453.16% maximum overshoot from the reference velocity, while this overshoot is 3.69% using the presented method. On the other hand, if the errors derivatives are entered to the fuzzy logic [7][8][9][10][11], velocity is tracked at the price of a larger number of enforced fuzzy rules. Compared to 2*20 rules employed in Table 5, 3*49 rules are designed in [8] and [11].…”

“…However, for a velocity tracking observation, the rate changes of posture errors are also entered to the fuzzy logic [7][8][9]. The same combination of errors and errors rate are exploited as fuzzy inputs in [10], where a fuzzy ring network method is used to control a multi-motor coordinated system and in [11], in which the trajectory tracking of a pneumatic driven linear delta robot is studied. Naturally, the fuzzy system complication expands by increasing the number of inputs.…”

A Survey of Fuzzy Algorithms Used in Multi-Motor Systems Control

“…Robotic manipulators are another type of multi-motor systems [4]. In this type of multi-motor systems, the main goals are to ensure the precision of tool positioning, the precise amount of force acting on a processed object, and coordinated movement according to the reference path defined by inverse kinematics in the dynamical state.…”

“…Multi-motor drives are an optimal solution for high-inertia and high-power drives, such as belt conveyors [1], as well as for long continuous lines [2]. In recent years, multi-motor drives have been increasingly implemented in electric vehicles [3] and robot manipulators [4]. Depending on drive application, control objectives are defined.…”

“…In the development of new multi-motor control strategies, fuzzy logic control is widely used. It has been reported that the implementation of fuzzy logic control gives encouraging results in real-time speed error compensation [4], drive efficiency increase [1], and in rather simple and fast synchronizing rules.…”