Robust Model Reference Gaussian Process Regression: Enhancing Adaptability through Domain Randomization
Hyuntae Kim · 2024
Nonlinear data-driven control strategies, particularly Model Reference Gaussian Process Regression (MRGPR), have been effective in designing controllers directly from system input/output data, bypassing the need for explicit system modeling. This approach is advantageous for complex nonlinear systems where traditional modeling methods may be inadequate. MR-GPR employs Gaussian Process Regression to provide a non-parametric control method, enhancing adaptability and performance. However, real-world applications present challenges due to variability in system parameters, such as ensuring robustness and consistent performance. To address these challenges, this paper proposes a robust MR-GPR controller incorporating domain randomization to improve adaptability to varying operational conditions. This extension aims to maintain stable performance across diverse settings, mitigating the impact of parameter changes on control efficacy.