Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization (2403.04881v2)

Abstract: In this work, we propose a framework for adapting the controller's parameters based on learning optimal solutions from contextual black-box optimization problems. We consider a class of control design problems for dynamical systems operating in different environments or conditions represented by contextual parameters. The overarching goal is to identify the controller parameters that maximize the controlled system's performance, given different realizations of the contextual parameters.We formulate a contextual Bayesian optimization problem in which the solution is actively learned using Gaussian processes to approximate the controller adaptation strategy. We demonstrate the efficacy of the proposed framework with a sim-to-real example. We learn the optimal weighting strategy of a model predictive control for connected and automated vehicles interacting with human-driven vehicles from simulations and then deploy it in a real-time experiment.

• The paper introduces a new framework that applies contextual Bayesian optimization to adapt controller parameters dynamically in variable environments.
• It utilizes Gaussian processes to learn and approximate optimal control settings, enabling real-time adjustments for complex systems.
• The approach is validated through model predictive control simulations for connected vehicles, demonstrating enhanced safety and efficiency.

Insights into "Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization"

The paper by Viet-Anh Le and Andreas A. Malikopoulos introduces an innovative approach towards controller adaptation in dynamic systems operating under variable environmental conditions, specifically through the lens of contextual Bayesian optimization (BO). This methodology capitalizes on Gaussian processes to actively learn optimal parameter solutions derived from black-box optimization problems, leveraging contextual information that encapsulates environmental variability or operational tasks.

Summary of Core Contributions

Framework Development: The paper presents a comprehensive framework for adjusting controller parameters dynamically via contextual BO. The contextual parameters effectively represent the varying conditions under which the control system operates, feeding into the optimization process to ascertain the superior control tactics for different situational setups.

Contextual Bayesian Optimization: The methodology reframes the controller adaptation problem into a contextual BO problem, integrating Gaussian processes to learn the hidden function mapping from contextual parameters to optimal controller settings. This approach uniquely allows the approximation of the BO problem's solutions, making it feasible to adjust controller parameters in real-time applications efficiently.

Practical Demonstration: The practical utility of this framework is highlighted through a simulation-to-real example involving model predictive control (MPC) for connected and automated vehicles (CAVs). The example demonstrates how the framework effectively learns and subsequently applies the optimal weighting strategy for MPC when CAVs are interacting with human-driven vehicles in diverse traffic scenarios.

Numerical Results and Claims

The paper underscores strong numerical results, particularly through the successful deployment of the learned adaptation strategy in a real-time intersection scenario involving MPC for CAVs. The framework showcased robustness and adaptability, achieving desired control performance against a backdrop of variable human driving behaviors. These findings substantiate the framework's efficacy, reinforcing claims regarding its viability in facilitating real-time control parameter adaptation in dynamic, real-world environments.

Theoretical and Practical Implications

The theoretically sound underpinning of contextual BO combined with Gaussian processes as presented in the paper paves a path for more intelligent and adaptive control systems. By embedding adaptability into the control process, controllers can become significantly more responsive to changes in operational contexts—a crucial feature for applications such as autonomous vehicles, automated manufacturing processes, and smart building management systems.

Theoretical Implications: The principles laid out in the paper extend current understandings of BO by demonstrating its efficacy under contextual settings, potentially informing future research on adaptive control frameworks and optimization in mixed environments.

Practical Implications: The example of CAVs interacting with HDVs brings to the forefront the potential of these methodologies to transform control systems into more sophisticated, autonomous, and context-aware systems, thus improving their operational efficiency and safety.

Future Developments and Directions

Given the rapid advancements in AI and machine learning, the intersection with control systems stands as a fruitful field for research. Future developments should explore:

• Enhancing Scalability: Addressing the scalability of this approach for larger systems or systems with high-dimensional contextual spaces.
• Expanding Practical Applications: Extending the framework to address challenges encountered with other types of autonomous systems, such as unmanned aerial vehicles (UAVs) and robotics.
• Integration with Other Learning Models: Investigating the integration of this framework with other AI models, such as deep learning techniques, to improve prediction accuracy and decision-making capabilities.

In conclusion, the paper provides a well-defined trajectory for further advancements in adaptive control systems, and its implications reach across both theoretical and practical landscapes in the field of control engineering and autonomous systems.