A new variant of Model Predictive Control and Identification (MPCI) is proposed. The on-line objective is not to minimize the sum of square errors, but to maximize on-line the sum of the lower bounds on the minimum eigenvalues of the information matrices over finite horizons. In that way, inputs to the controlled process are allowed to excite the process highly enough to generate as much modelling information as possible, while the process goes off-spec as little as possible. Constraints can be loosened or tightened according to the need for identification. The effectiveness of the proposed new methodology is illustrated through a number of simulations
The architecture of model predictive control (MPC), with its explicit internal model and constrained...
In control engineering, system identification is frequently used to create models from inputoutput d...
Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic pr...
A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant sys...
This paper presents an intuitive on-line tuning strategy for linear model predictive control (MPC) a...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
A model predictive control (MPC) strategy based on augmented autonomous predictions enables a highly...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
Explicit model predictive control (MPC) addresses the problem of removing one of the main drawbacks ...
Abstract — This paper considers a method for optimal input design in system identification for contr...
An adaptive Model Predictive Control (adaptive MPC) strategy is proposed for linear systems with con...
Model Predictive Control (MPC) is an optimal control method. At each instant of time, a per-formance...
Model predictive control, MPC, form a class of model-based controllers that select control actions b...
This paper extends an effcient robust Model Predictive Control (MPC) methodology based on offline op...
© 2020 Andrei PavlovThe thesis addresses several critical challenges in the implementation of Model ...
The architecture of model predictive control (MPC), with its explicit internal model and constrained...
In control engineering, system identification is frequently used to create models from inputoutput d...
Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic pr...
A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant sys...
This paper presents an intuitive on-line tuning strategy for linear model predictive control (MPC) a...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
A model predictive control (MPC) strategy based on augmented autonomous predictions enables a highly...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
Explicit model predictive control (MPC) addresses the problem of removing one of the main drawbacks ...
Abstract — This paper considers a method for optimal input design in system identification for contr...
An adaptive Model Predictive Control (adaptive MPC) strategy is proposed for linear systems with con...
Model Predictive Control (MPC) is an optimal control method. At each instant of time, a per-formance...
Model predictive control, MPC, form a class of model-based controllers that select control actions b...
This paper extends an effcient robust Model Predictive Control (MPC) methodology based on offline op...
© 2020 Andrei PavlovThe thesis addresses several critical challenges in the implementation of Model ...
The architecture of model predictive control (MPC), with its explicit internal model and constrained...
In control engineering, system identification is frequently used to create models from inputoutput d...
Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic pr...