Data-Driven Linear Quadratic Optimization for Controller Synthesis with Structural Constraints (1912.03616v4)

Abstract: For various typical cases and situations where the formulation results in an optimal control problem, the Linear Quadratic Regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the Algebraic Riccati Equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.