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This paper proposes a linear model predictive control (LMPC) based on extend state observer (ESO) which can approximate the control performance of the nonlinear model predictive control (NMPC). The ESO is used to estimate the disturbance caused by the linearization of actual nonlinear system. The estimated disturbance is added into the system model of the LMPC controller design. The low speed control...
Model predictive control of discrete-time nonlinear systems with incremental input constraints is proposed in this paper. Firstly, the existence of the terminal set and terminal penalty is proven on the assumption that the considered system is twice continuously differentiable. Secondly, properties of the optimal cost function are exploited. It shows that the optimal cost function is positive semi-definite,...
Owing to its ability to deal with hard constraints, model predictive control (MPC) can meet the good control performance requirements of modern complex industrial control systems. But the large computational load limits its application to more fields such as fast dynamic systems. In order to apply MPC to millisecond timescale systems, this paper studies the fast MPC based on multiscale system theory...
In this paper, a novel model predictive control (MPC) scheme is presented for linear stochastic systems with probabilistic constraints. Instead of the prediction of the behavior of the original linear stochastic system, the behavior of a corresponding nominal linear system is predicted. Thus, the optimization problem that is solved online has the same computational burden as the ones of standard deterministic...
The online computational burden associated with the solution of the NMPC optimization problem is a key issue when applying nonlinear model predictive control (MPC) to fast dynamic systems. In order to improve the computation efficiency, a novel hardware implementation method for NMPC on a field programmable gate array (FPGA) chip is proposed. The optimization problem formed by NMPC is solved by particle...
Model predictive control (MPC), also called moving horizon control or receding horizon control, is one of the most successful and the most popular advanced control methods. The basis of MPC is the online solution of a constrained optimization problem updated by the actual state. The obtained control is injected into the system until the next sampling time, while the procedure is repeated whenever...
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