  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).
