Performance-Driven Safe Bayesian Optimization for Intelligent Tuning of High-Order Cascade Systems

Abstract

Automatic parameter tuning of high-order cascade controllers suffers from sampling inefficiency and strong couplings. This work presents a performance-driven, systematic, and safe intelligent parameter-tuning framework for high-order cascade systems. To achieve data-efficient and noise-robust hyper-parameter calibration, an intelligent tuning framework based on Bayesian optimization is proposed to calibrate the control parameters from the buffer of performance-metric measurements. Furthermore, we improve the Bayesian-optimization-based framework in three aspects, involving effectiveness, security, and sampling efficiency. Firstly, a comprehensive control performance assessment combining the error-integral and statistical performance criteria is designed to evaluate the cost of a sampling point in terms of final precision, response rapidity, and vibration. Meanwhile, the security of sampling exploration is heightened by imposing composite hard parametric and soft performance-metric (maximum input, overshoot, etc.) constraints on the acquisition points. Additionally, a hierarchical optimization strategy is proposed to further boost sampling exploitation by alternatively tuning the control parameters of subsystems and refining the constraint on the sampling space. The proposed framework is applied to automatic parameter tuning of high-order dynamic-surface-based backstepping control combined with anti-disturbance rejection control. The comparative simulation results demonstrate that the proposed intelligent tuning possesses superiorities in sampling efficiency and security for parameter calibration problems.