MIT 16.S498 Risk Aware and Robust Nonlinear Planning (rarnop)


Advanced Probabilistic and Robust Optimization-Based Algorithms for Control and Safety Verification of Nonlinear Uncertain Autonomous Systems

Founder/Instructor: Ashkan Jasour
Concern for safety is one of the dominant issues that arises in planning in the presence of uncertainties and disturbances. This course addresses advanced probabilistic and robust optimization-based techniques for control and safety verification of nonlinear dynamical systems in the presence of uncertainties. Specifically, we will learn how to leverage rigorous mathematical tools, such as the theory of measures and moments, the theory of nonnegative polynomials, and semidefinite programming to develop convex optimization formulations to control and analyze uncertain nonlinear dynamical systems with applications in autonomous systems and robotics.

Optimization Methods: Semidefinite Programs for i) Nonlinear Chance Optimization, ii) Nonlinear Chance Constrained Optimization, iii) Nonlinear Robust Optimization, and iv) Nonlinear Distributionally Robust Chance Constrained Optimization.

Applications: i) Probabilistic and Robust Nonlinear Safety Verification,  ii) Risk Aware Control of Probabilistic Nonlinear Dynamical Systems, iii) Robust Control of Uncertain Nonlinear Dynamical Systems.