A systematic min-max optimization design of model predictive tracking control (MPC) for
industrial processes under partial actuator uncertainty and unknown disturbances is …

In this paper, an improved constrained tracking control design is proposed for batch
processes under uncertainties. A new process model that facilitates process state and …

The model predictive control (MPC) method is widely used in multivariable process control
due to its optimization control nature and easy engineering realization. Aiming at the large …

Model predictive control (MPC) has acquired lots of developments and extensive
applications in various industries during the past 40 years. For the early version of basic …

Conventional model predictive control (MPC) involves solving an optimization problem
online to determine the control actions that minimize a performance criterion function. The …

For industrial processes with external disturbance and actuator failure, off-policy
reinforcement learning-based novel model-free minmax fault-tolerant control is proposed in …

Due to the ability to handle constraints systematically and predict system evolution with
models, model predictive control (MPC) methods have been widely studied and …

In this paper a novel robust model predictive control (RMPC) algorithm is proposed, which is
guaranteed to stabilize any linear time-varying system in a given convex uncertainty region …

In this study, a novel nonlinear generalized predictive control (NGPC) method is proposed to
tackle the tracking control problem which considers a mathematical model in conjunction …

A model-based predictive control methodology in the space of the latent variables for
continuous processes is presented. Implementing identification and control in the latent …