Model Predictive Path-Following Control of Snake Robots Using an Averaged Model

Abstract

We propose a new simplified model for the control design of snake robots and apply it to a path-following control design using model predictive control (MPC). While MPC has an advantage in that inequality constraints can be explicitly considered in control design, most of the previous simplified models are still too complex to apply to MPC since the models include joint angles as time-varying parameters. Thus, we exclude joint angles using the averaging method to construct a simpler model. Another feature of the proposed model is that it can be derived from the original complex model without parameter identification using simulation data and without assuming straight-line movements. In addition to inequality constraints on joint angles and the frequency of joint motions, we impose constraints on the change rates of these variables in our MPC design since the averaged model is derived by assuming that these variables slowly change. Furthermore, we introduce a soft constraint to decrease the effects of approximation error of the simplified model on the control performance. The effectiveness of the control system is verified in both simulations and experiments.