This research project is focused on complex autonomous systems, where the complexities mainly arise from interactions between multiple agents, from tightly constrained and dynamic environments, and human-interactions. To cope with these complexities, classical model-based robust control methods can be overly conservative or even impossible to be implemented. Therefore, data-driven control methods have been developed. In particular, methods from machine learning have been fused with control algorithms in order to achieve a high performance and guarantee safety for the controlled complex systems.
Data-driven methods have been developed to learn optimal control policies for complex interconnected and multi-agent systems, leading to (local) optimal performance and safe control behavior. The results have been validated in extensive simulations of distributed linear systems and of multi-agent nonlinear systems. For the latter, an application example is a time-optimal navigation task of multiple agents to a desired goal position, while ensuring collision avoidance with all other agents. The optimal control policies are iteratively learned from previously seen data and employed in a decentralized way (without communication between the agents), leading to a scalable (applicable to a large number of subsystems) data-driven control method with locally optimal control performance and guaranteed safety (collision avoidance).
Furthermore, a hierarchical control framework has been developed, where previously recorded data is used to learn a higher-level strategy to guide the lower-level optimization problem. The advantage is that the underlying optimization problem is less complex and thus can be solved online. Furthermore, both a good control performance as well as the safety of the controlled system through a finite state machine are guaranteed, even for control tasks that need to navigate in tightly constrained and dynamic environments with other human-driven cars. One considered control scenario is autonomous driving in a tight parking lot, where other human-driven cars are driving and parking into empty spots. Since the environment is dynamic and tightly constrained, the exact optimization problem that needs to be solved for controlling the autonomous car is too complex to be solved online in real-time. The performance and safety of the novel hierarchical control framework for this control task were validated in extensive simulations, and in experiments at UC Berkeley.
These methods have further been fused with the developed distributed methods for optimal data generation, and have been applied to the problem of platooning in mixed traffic conditions.
In order to cope with complex dynamical systems that are impossible to be modeled analytically, methods have been developed that replace the analytical model by a purely data-driven representation based on matrix zonotopes from reachability theory. The data-driven representation only needs one pair of input-output trajectories from the system. The algorithm, called ZPC (zonotopic predictive control), can cope with noisy data. Robust safety guarantees for this novel method have been provided.
