Gradient-based Cooperative Control of quasi-Linear Parameter Varying Vehicles with Noisy Gradients

Abstract

This paper extends recent results on the exponential performance analysis of gradient based cooperative control dynamics using the framework of exponential integral quadratic constraints ($\alpha-$IQCs). A cooperative source-seeking problem is considered as a specific example where one or more vehicles are embedded in a strongly convex scalar field and are required to converge to a formation located at the minimum of a field. A subset of the agents are assumed to have the knowledge of the gradient of the field evaluated at their respective locations and the interaction graph is assumed to be uncertain. As a first contribution, we extend earlier results on linear time invariant (LTI) systems to non-linear systems by using quasi-linear parameter varying (qLPV) representations. Secondly, we remove the assumption on perfect gradient measurements and consider multiplicative noise in the analysis. Performance-robustness trade off curves are presented to illustrate the use of presented methods for tuning controller gains. The results are demonstrated on a non-linear second order vehicle model with a velocity-dependent non-linear damping and a local gain-scheduled tracking controller.