  This study presents the design, discretization and implementation of the
continuous-time linear-quadratic model predictive control (CT-LMPC). The
control model of the CT-LMPC is parameterized as transfer functions with time
delays, and they are separated into deterministic and stochastic parts for
relevant control and filtering algorithms. We formulate time-delay,
finite-horizon CT linear-quadratic optimal control problems (LQ-OCPs) for the
CT-LMPC. By assuming piece-wise constant inputs and constraints, we present the
numerical discretization of the proposed LQ-OCPs and show how to convert the
discrete-time (DT) equivalent into a standard quadratic program. The
performance of the CT-LMPC is compared with the conventional DT-LMPC algorithm.
Our numerical experiments show that, under fixed tunning parameters, the
CT-LMPC shows better closed-loop performance as the sampling time increases
than the conventional DT-LMPC.
