A PID Tuning Strategy Based on a Variable Weight Beetle Antennae Search Algorithm for Hydraulic Systems

Abstract

To select reasonable PID controller parameters and improve control performances of hydraulic systems, a variable weight beetle antenna search algorithm is proposed for PID tuning in the hydraulic system. The beetle antennae search algorithm is inspired by the beetle preying habit depending on symmetry antennae on the head. The proposed algorithm added the exponential equation mechanism strategy in the basic algorithm to further improve the searching performance, the convergence speed, and the optimization accuracy and obtain new iteration and an updating method in the global searching and local searching stages. In the PID tuning process, advantages of less parameters and fast iteration are realized in the PID tuning process. In this paper, different dimension functions were tested, and results calculated by the proposed algorithm were compared with other famous algorithms, and the numerical analysis was carried out, including the iteration, the box-plot, and the searching path, which comprehensively showed the searching balance in the proposed algorithm. Finally, the reasonable PID controller parameters are found by using the proposed method, and the tuned PID controller is introduced into the hydraulic system for control, and the time-domain response characteristics and frequency response characteristics are given. The results show that the proposed PID tuning method has good PID parameter tuning ability, and the tuned PID has a good control ability, which makes the hydraulic system achieve the desired effect.

Parameter optimization of nonlinear PID controller using RBF neural network for continuous stirred tank reactor

“…where h p , h i , h d are the rate of change of the proportional, integral and differential parameters of the NPID control algorithm, and ∂y ∂Du is the Jacobian matrix information of the controlled object obtained by the RBF neural network through identification, which can be approximated by equation (10).…”

“…Step 4: The network output y m (k) is calculated, and the center vector m j , the base width vector s j , the weight vector v j , and the Jacobian matrix are adjusted according to equations ( 7)- (10) to obtain the network identification information.…”

“…When the system is uncertain, it is difficult to ensure the optimal effect. 8,9 In order to solve the limitations of manual tuning parameters of conventional PID control algorithms, more and more artificial intelligence algorithms have been proposed to optimize PID parameters, such as swarm intelligence algorithms, 10 fuzzy rules, 11 and neural networks. Among them, RBF neural network has good generalization ability and simple network structure.…”

A New General Type-2 Fuzzy Predictive Scheme for PID Tuning

“…The other class of PID regulation methods is the evolutionary based algorithms. For example the weight beetle antenna method is developed in [16], for PID tuning and its performance is evaluated on hydraulic plant. The genetic algorithm is applied in [17] to tune the PID gains and the optimized PID is used for a temperature control problem.…”

“…Equation ( 15) also indicates the error between the system output value and the desired value at moment k. By calculating Equation ( 14), the output of the control is obtained in accordance with Equation (16). Using this relation, the output of the controller can be obtained at moment k. We also consider the parameter y(k) to consider the delay between system input and output.…”