Learning-Based Safety-Stability-Driven Control for Safety-Critical Systems under Model Uncertainties
This work addresses safety and stability for autonomous systems like self-driving cars, but it is incremental as it builds on existing control methods with learning enhancements.
The paper tackled the problem of ensuring safety and tracking stability for nonlinear safety-critical systems under model uncertainties by proposing a learning-based control algorithm that uses Gaussian Processes to estimate model errors and formulates constraints into a quadratic program, achieving effective control in a connected cruise control simulator.
Safety and tracking stability are crucial for safety-critical systems such as self-driving cars, autonomous mobile robots, industrial manipulators. To efficiently control safety-critical systems to ensure their safety and achieve tracking stability, accurate system dynamic models are usually required. However, accurate system models are not always available in practice. In this paper, a learning-based safety-stability-driven control (LBSC) algorithm is presented to guarantee the safety and tracking stability for nonlinear safety-critical systems subject to control input constraints under model uncertainties. Gaussian Processes (GPs) are employed to learn the model error between the nominal model and the actual system dynamics, and the estimated mean and variance of the model error are used to quantify a high-confidence uncertainty bound. Using this estimated uncertainty bound, a safety barrier constraint is devised to ensure safety, and a stability constraint is developed to achieve rapid and accurate tracking. Then the proposed LBSC method is formulated as a quadratic program incorporating the safety barrier, the stability constraint, and the control constraints. The effectiveness of the LBSC method is illustrated on the safety-critical connected cruise control (CCC) system simulator under model uncertainties.