Abstract
In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can’t obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.
Highlights
The parallel robot is superior to the serial robot in its rigidity, high precision, and load capacity
fuzzy model predictive control (FPC) algorithm used in the driving force control of redundant branch of parallel robot can obtain better anti-interference ability than Model predictive control (MPC)
Two improved control methods—PID model predictive control (PPC) and FPC are proposed in order to improve the application of the MPC algorithm in the control of parallel robot
Summary
The parallel robot is superior to the serial robot in its rigidity, high precision, and load capacity. FPC controller designed in this article is mainly composed of three parts They are predictive model, performance measurement, and control decision. The control system of the permanent magnet synchronous motor is a double-input and double-output system, and the prediction model is given in the study by Shuhuan et al.[3] The performance measurement is to evaluate the control effect of the control input hypothesis and determine the control decision based on the evaluation results. 2. If jedðtÞj < ded and jcedðtÞj < dced, jeqðtÞj < deq and jceqðtÞj < dceq, control input udðtÞ and uqðtÞ at current time are obtained according to equations (15) and (16) and go to (7), otherwise go to (3). 3. Obtain future output predictive value according to the predictive model under reference control udi and uqj. The cubic spline curve as follows x 1⁄4 a0 þ a1t þ a2t2 þ a3t3
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