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We present a locally optimal tracking controller for Cable Driven ParallelRobot (CDPR) control based on a time-varying Linear Quadratic Gaussian (TV-LQG)controller. In contrast to many methods which use fixed feedback gains, ourtime-varying controller computes the optimal gains depending on the location inthe workspace and the future trajectory. Meanwhile, we rely heavily on offlinecomputation to reduce the burden of online implementation and feasibilitychecking. Following the growing popularity of probabilistic graphical modelsfor optimal control, we use factor graphs as a tool to formulate our controllerfor their efficiency, intuitiveness, and modularity. The topology of a factorgraph encodes the relevant structural properties of equations in a way thatfacilitates insight and efficient computation using sparse linear algebrasolvers. We first use factor graph optimization to compute a nominaltrajectory, then linearize the graph and apply variable elimination to computethe locally optimal, time varying linear feedback gains. Next, we leverage thefactor graph formulation to compute the locally optimal, time-varying KalmanFilter gains, and finally combine the locally optimal linear control andestimation laws to form a TV-LQG controller. We compare the tracking accuracyof our TV-LQG controller to a state-of-the-art dual-space feed-forwardcontroller on a 2.9m x 2.3m, 4-cable planar robot and demonstrate improvedtracking accuracies of 0.8{\deg} and 11.6mm root mean square error in rotationand translation respectively.

Locally Optimal Estimation and Control of Cable Driven Parallel Robots using Time Varying Linear Quadratic Gaussian Control