Bayesian Learning-Based Adaptive Control for Safety Critical Systems
This work addresses safety and stability constraints for deploying deep learning in critical systems, such as space exploration, but is incremental as it builds on existing control theories.
The paper tackles the challenge of applying deep learning to safety-critical control systems by proposing a Bayesian learning-based adaptive control framework that guarantees stability and safety with probability 1, demonstrated in high-speed terrestrial mobility applications like Mars rover missions.
Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical systems, which have constraints on safety, stability, and real-time performance. We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties. Central to our method is the use of Bayesian model learning, which provides an avenue for maintaining appropriate degrees of caution in the face of the unknown. In the proposed approach, we develop an adaptive control framework leveraging the theory of stochastic CLFs (Control Lyapunov Functions) and stochastic CBFs (Control Barrier Functions) along with tractable Bayesian model learning via Gaussian Processes or Bayesian neural networks. Under reasonable assumptions, we guarantee stability and safety while adapting to unknown dynamics with probability 1. We demonstrate this architecture for high-speed terrestrial mobility targeting potential applications in safety-critical high-speed Mars rover missions.