Model predictive control of internal combustion engines: A review and future directions
An internal combustion engine (ICE) is a highly nonlinear dynamic and complex
engineering system whose operation is constrained by operational limits, including …
engineering system whose operation is constrained by operational limits, including …
Computationally efficient model predictive control algorithms
In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …
of: the current control error (the proportional part), the past errors (the integral part) and the …
Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks
This paper presents a neural network approach to robust model predictive control (MPC) for
constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded …
constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded …
A critical review of the most popular types of neuro control
In this review article, the most popular types of neural network control systems are briefly
introduced and their main features are reviewed. Neuro control systems are defined as …
introduced and their main features are reviewed. Neuro control systems are defined as …
New method for the on-line signature verification based on horizontal partitioning
Verification of identity based on the analysis of dynamic signatures is an important problem
of biometrics. The effectiveness of the verification significantly increases when the dynamic …
of biometrics. The effectiveness of the verification significantly increases when the dynamic …
Model predictive control for systems with fast dynamics using inverse neural models
In this work, a novel model predictive control (MPC) scheme is introduced, by integrating
direct and indirect neural control methodologies. The proposed approach makes use of a …
direct and indirect neural control methodologies. The proposed approach makes use of a …
[PDF][PDF] Disturbance modeling and state estimation for offset-free predictive control with state-space process models
Disturbance modeling and design of state estimators for offset-free Model Predictive Control
(MPC) with linear state-space process models is considered in the paper for deterministic …
(MPC) with linear state-space process models is considered in the paper for deterministic …
Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function
Model Predictive Control (MPC) algorithms typically use the classical L 2 cost function,
which minimises squared differences of predicted control errors. Such an approach has …
which minimises squared differences of predicted control errors. Such an approach has …
Nonlinear model predictive control based on collective neurodynamic optimization
In general, nonlinear model predictive control (NMPC) entails solving a sequential global
optimization problem with a nonconvex cost function or constraints. This paper presents a …
optimization problem with a nonconvex cost function or constraints. This paper presents a …
RBF-ARX model-based MPC strategies with application to a water tank system
A hybrid pseudo-linear RBF-ARX model that combines Gaussian radial basis function (RBF)
networks and linear ARX model structure is utilized for representing the dynamic behavior of …
networks and linear ARX model structure is utilized for representing the dynamic behavior of …