Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes

• URL: http://arxiv.org/abs/2408.16307v2
• Date: Mon, 25 Nov 2024 07:20:06 GMT
• Title: Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes
• Authors: Hongxuan Wang, Xiaocong Li, Lihao Zheng, Adrish Bhaumik, Prahlad Vadakkepat,
• Abstract summary: We propose SafeCtrlBO to optimize multiple controllers simultaneously and safely. We simplify the exploration process in safe Bayesian optimization, reducing computational effort without sacrificing expansion capability.
• Score: 2.1757909866792313
• License:
• Abstract: Controller tuning and optimization have been among the most fundamental problems in robotics and mechatronic systems. The traditional methodology is usually model-based, but its performance heavily relies on an accurate mathematical system model. In control applications with complex dynamics, obtaining a precise model is often challenging, leading us towards a data-driven approach. While various researchers have explored the optimization of a single controller, it remains a challenge to obtain the optimal controller parameters safely and efficiently when multiple controllers are involved. In this paper, we propose SafeCtrlBO to optimize multiple controllers simultaneously and safely. We simplify the exploration process in safe Bayesian optimization, reducing computational effort without sacrificing expansion capability. Additionally, we use additive kernels to enhance the efficiency of Gaussian process updates for unknown functions. Hardware experimental results on a permanent magnet synchronous motor (PMSM) demonstrate that compared to existing safe Bayesian optimization algorithms, SafeCtrlBO can obtain optimal parameters more efficiently while ensuring safety.

Related papers

• Adaptive Bayesian Optimization for High-Precision Motion Systems [2.073673208115137]
  We propose a real-time purely data-driven, model-free approach for adaptive control, by online tuning low-level controller parameters. We base our algorithm on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization. We evaluate the algorithm's performance on a real precision-motion system utilized in semiconductor industry applications.
  arXiv  Detail & Related papers  (2024-04-22T21:58:23Z)
• Stability-informed Bayesian Optimization for MPC Cost Function Learning [5.643541009427271]
  This work explores closed-loop learning for predictive control parameters under imperfect information. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate.
  arXiv  Detail & Related papers  (2024-04-18T13:49:09Z)
• Towards Safe Multi-Task Bayesian Optimization [1.3654846342364308]
  Reduced physical models of the system can be incorporated into the optimization process, accelerating it. These models are able to offer an approximation of the actual system, and evaluating them is significantly cheaper. Safety is a crucial criterion for online optimization methods such as Bayesian optimization.
  arXiv  Detail & Related papers  (2023-12-12T13:59:26Z)
• Stochastic Optimal Control Matching [53.156277491861985]
  Our work introduces Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for optimal control. The control is learned via a least squares problem by trying to fit a matching vector field. Experimentally, our algorithm achieves lower error than all the existing IDO techniques for optimal control.
  arXiv  Detail & Related papers  (2023-12-04T16:49:43Z)
• Tuning Legged Locomotion Controllers via Safe Bayesian Optimization [47.87675010450171]
  This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. We leverage a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system.
  arXiv  Detail & Related papers  (2023-06-12T13:10:14Z)
• Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization [73.94660141019764]
  tuning machine parameters of particle accelerators is a repetitive and time-consuming task. We propose and evaluate a step size-limited variant of safe Bayesian optimization.
  arXiv  Detail & Related papers  (2022-03-26T02:21:03Z)
• Safe and Efficient Model-free Adaptive Control via Bayesian Optimization [39.962395119933596]
  We propose a purely data-driven, model-free approach for adaptive control. tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation.
  arXiv  Detail & Related papers  (2021-01-19T19:15:00Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Global Optimization of Gaussian processes [52.77024349608834]
  We propose a reduced-space formulation with trained Gaussian processes trained on few data points. The approach also leads to significantly smaller and computationally cheaper sub solver for lower bounding. In total, we reduce time convergence by orders of orders of the proposed method.
  arXiv  Detail & Related papers  (2020-05-21T20:59:11Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.