Robust optimal real-time control of industrial robots by means of stochastic optimization

The standard engineering approach for optimal control of industrial or service robots assumes that all model parameters are exactly known, but due to stochastic variations of the material, manufacturing errors, modeling errors and stochastic variations of the workspace environment (e.g. stochastic payload) they need to be modeled via stochastic methods instead. This leads to optimal control/variational problems under stochastic disturbances, that can not be solved directly but approximately by means of a reduction to a finite nonlinear program. Due to the short cycle times of robots these nonlinear programs have to be solved in real-time in order to update the optimal controls, whenever new information about the uncertain parameters involved is available. A task impossible because of the complexity of the control problem. Hence, a sensitivity analysis based real-time solution strategy is introduced, that takes advantage of an optimal reference solution calculated before-hand off-line.