Fast Risk-Sensitive Model Predictive Control for Systems with Time-Series Forecasting Uncertainties

Risk-sensitive model predictive control (MPC) is a promising technique since it can balance the main drawbacks of robust MPC and stochastic MPC: conservative decision making and vulnerability to extreme events. The main challenge is that the risk measures accompany high nonlinearity making the technique difficult for various applications. This paper presents a fast risk-sensitive MPC for linear time-invariant systems that use time-series forecasting. The risk measure involving high nonlinearity is converted into a weighted square-sum of decision variables with the assumption of Gaussian system disturbances. The error statistics update rule is designed to consider the characteristics of time-series forecasting. The proposed formulation is applied to energy-efficient car-following control and compared with the robust and the stochastic MPC.