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 …
Identification and adaptive neural network control of a DC motor system with dead-zone characteristics
In this paper, an adaptive control approach based on the neural networks is presented to
control a DC motor system with dead-zone characteristics (DZC), where two neural networks …
control a DC motor system with dead-zone characteristics (DZC), where two neural networks …
A nonlinear generalized predictive control for pumped storage unit
In this paper, the control problem of pumped storage unit (PSU) has been studied. A
nonlinear generalized predictive control (NGPC) method has been applied to design the …
nonlinear generalized predictive control (NGPC) method has been applied to design the …
Predictive hierarchical harmonic emotional neuro-cognitive control of nonlinear systems
Emotion enables biological organisms to respond quickly and reasonably to uncertain and
unpredictable events. This basic survival instinct is remarkably in line with the theory of …
unpredictable events. This basic survival instinct is remarkably in line with the theory of …
VPNet: Variable projection networks
In this paper, we introduce VPNet, a novel model-driven neural network architecture based
on variable projection (VP). Applying VP operators to neural networks results in learnable …
on variable projection (VP). Applying VP operators to neural networks results in learnable …
Practical nonlinear predictive control algorithms for neural Wiener models
This paper describes three nonlinear Model Predictive Control (MPC) algorithms for neural
Wiener models. In all algorithms the model or the output trajectory is linearised on-line and …
Wiener models. In all algorithms the model or the output trajectory is linearised on-line and …
On identification of well‐conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes
The focus of this work is on economic model predictive control (EMPC) that utilizes well‐
conditioned polynomial nonlinear state‐space (PNLSS) models for processes with nonlinear …
conditioned polynomial nonlinear state‐space (PNLSS) models for processes with nonlinear …
A Wiener neural network-based identification and adaptive generalized predictive control for nonlinear SISO systems
In this study, a Wiener-type neural network (WNN) is derived for identification and control of
single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified …
single-input and single-output (SISO) nonlinear systems. The nonlinear system is identified …
Nonlinear predictive control of dynamic systems represented by Wiener–Hammerstein models
This paper is concerned with computationally efficient nonlinear model predictive control
(MPC) of dynamic systems described by cascade Wiener–Hammerstein models. The Wiener …
(MPC) of dynamic systems described by cascade Wiener–Hammerstein models. The Wiener …
A self-tuning control method for Wiener nonlinear systems and its application to process control problems
P Yuan, B Zhang, Z Mao - Chinese journal of chemical engineering, 2017 - Elsevier
Many chemical processes can be modeled as Wiener models, which consist of a linear
dynamic subsystem followed by a static nonlinear block. In this paper, an effective discrete …
dynamic subsystem followed by a static nonlinear block. In this paper, an effective discrete …