Model predictive control is considered as a desirable candidate for high performance control of industrial processes and for safe and efficient operation of autonomous systems. Despite its remarkable success in the chemical process control industry, the successful transition of model predictive control into time-and-safety-critical applications is limited by the computational challenges faced in its real-time implementation. This research project lays the theoretical foundations for a new real-time implementation strategy that will significantly reduce the runtime of model predictive control, increase its overall reliability, and extend its applicability to the automotive and aerospace domains.
Traditional model predictive control is implemented by fully solving an optimal control problem at every time step. This research project focuses on an alternative scheme where the solution to the optimal control problem is tracked with a bounded error over multiple time steps. By treating the numerical solver as a dynamic system that evolves in parallel to the controlled plant, the project will identify sufficient conditions for asymptotic stability and constraint satisfaction using Input-to-State Stability arguments. Recursive stability and constraint satisfaction will then be ensured by introducing the Real-Time Iteration Governor, which is an add-on supervision layer that suitably manipulates the reference of the model predictive controller so that the solution to the optimal control problem can be tracked with an acceptably small error. These theoretical contributions will be supplemented by dedicated numerical algorithms and analog circuits that take full advantage of running at a faster timescale with respect to the rate of change of the optimal control problem. The proposed framework will also address nonconvex constraints by using the Real-Time Iteration Governor to progressively steer the numerical solver away from undesirable local minima. Finally, to demonstrate the effectiveness of the Real-Time Iteration Governor and its supporting theory, the proposed framework will be applied to practical engineering challenges, which include the autonomous navigation of small spacecraft in close proximity to asteroids, the development of efficient control algorithms for self-driving cars, and the implementation of constrained coordination strategies for unmanned aerial vehicles.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
