Integrating Structure, Information Architecture and Control Design: Application to Tensegrity Systems

TL;DR

This paper introduces a unified covariance control approach to optimize structural, sensor, actuator, and control parameters simultaneously for tensegrity systems, enhancing design efficiency and system performance.

Contribution

It presents a novel joint optimization framework using covariance control and LMIs for tensegrity system design, integrating structure, sensing, actuation, and control.

Findings

Successful formulation of joint optimization as a convex problem.

Demonstration of convergence to a stationary point using convexification.

Application to tensegrity systems with improved design parameters.

Abstract

A novel unified approach to jointly optimize structural design parameters, actuator and sensor precision and controller parameters is presented in this paper. The joint optimization problem is posed as a covariance control problem, where feasibility is achieved by bounding the covariance of the output as well as that of the control signals. The formulation is used to design a tensegrity system, where the initial prestress parameters, sensor and actuator precisions, and the control law are jointly optimized. Tensegrity system dynamics models linearized about an equilibrium point are used for system design, where minimality is ensured by constraint projection. The feedback loop is assumed to have a full-order dynamic compensator with its characteristic matrices chosen as optimization variables. The suboptimal solution of this non-convex system design problem is found by iterating over an approximated convex problem through the use of a convexifying potential function that enables the convergence to a stationary point. It is shown that for a linear dynamical system, the approximated joint optimization problem can be formulated using Linear Matrix Inequalities (LMIs).