Variable-order stochastic adaptive control of robotic manipulators with a flexible forearm

An application of a recursive covariance lattice filter to the adaptive estimation and stochastic control of a robotic manipulator with one flexible link is presented. Not only the effective order, but also the corresponding parameters of an autoregression moving average with a bias (ARMAB) prediction model of the manipulator, are updated by a set of pure order-recursive lattice algorithms. The reduced-order prediction model that represents significant dynamics of the plant is used to generate optimal control sequences by minimizing the expectation of a weighted cost functional. In the simulations, the manipulator is modeled by the finite element method and Lagrange´s equations. The performance and robustness of the variable-order stochastic adaptive controller are demonstrated by numerical results