A convex optimization approach to synthesizing state feedback data-driven controllers for switched linear systems

This paper seeks to develop a computationally tractable framework for data-driven control of switched linear systems . Specifically, given model structure and experimental data collected at different operating points, we seek directly design state-feedback controller that stabilizes system arbitrarily switches amongst all sub-systems could have generated the observed data, without an explicit plant identification step. The main result shows this robust optimization problem can be recast, through use duality, into polynomial form efficiently solved, leading with guaranteed ℓ ∞ worst-case performance. effectiveness proposed technique is illustrated several examples, including horizontal motion quadcopter