Controller parameters tuning with constrained Bayesian optimization
YANG Liang-liang
HUA Jun-hui
PAN Xiao-ming
LU Wen-qi
Abstract：The feedforward force control problem can be transformed into a controller parameter self-tuning problem by using parameterized fixed-structure feedforward control methods.But the controller parameter tuning is often time-consuming,and the traditional Bayesian optimization is prone to unsafe situations such as system instability during the parameter tuning processes.To solve this problem,a Bayesian safe optimization incorporating objective function con-straints is proposed.Firstly,the agent model of the tuning parameters is obtained by the Gaussian process.Then the safe evaluation point is determined in Bayesian optimization by incorporating a pre-set safety threshold and leveraging the particle swarm algorithm,which enabling control force updating.Eventually,the optimal parameters are obtained by an iterative process,and the optimal trajectory tracking performance of the motion control system can be achieved by the feedforward controller with the corresponding optimal parameters while adhering to safety constraints.The experimental results verify that the proposed algorithm can achieve the optimal point-to-point trajectory tracking performance under the safety constraints.
Keywords：automatic parameter tuningsafe optimizationGaussian processtrajectory trackingparticle swarms optimization
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 403-411 )