Hardware-in-the-Loop Iterative Optimal Feedback Control Without Model-Based Future Prediction

Abstract

Optimal control provides a systematic approach to control robots. However, computing optimal controllers for hardware-in-the-loop control is sensitively affected by modeling assumptions, computationally expensive in online implementation, and time-consuming in practical application. This makes the theoretical appeal of optimization challenging to exploit in real-world implementation. In this paper, we present a novel online optimal control formulation that aims to address the above-mentioned limitations. The formulation combines a model with measured state information to efficiently find near-optimal feedback controllers. The idea to combine a model with measurements from the actual motion is similar to what is used in model predictive control formulations, with the difference that here the model is not used for future prediction, the optimization is performed along the measured trajectory of the system, and the online computation is reduced to a minimum; it requires a small-scale, one time step, static optimization, instead of a large-scale, finite time horizon, dynamic optimization. The formulation can be used to solve optimal control problems defined with nonlinear cost, nonlinear dynamics, and box-constrained control inputs. Numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness of the proposed hardware-in-the-loop optimal control approach.