scholarly journals Speed Control of DC Motors: Optimal Closed PID-Loop Model Predictive Control

2020 ◽  
Vol 8 (1) ◽  
pp. 9-21
Author(s):  
Oluwasegun Somefun ◽  
Kayode Akingbade ◽  
Folasade Dahunsi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Speed Control ◽  
Loop Model ◽  
Dc Motors

A novel approach to disturbance rejection in idle speed control towards reduced idle fuel consumption

2003 ◽  
Vol 217 (8) ◽  
pp. 677-690 ◽  
Author(s):  
C Manzie ◽  
H C Watson
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Multiple Input Multiple Output ◽  
Speed Control ◽  
Idle Speed ◽  
Novel Approach ◽  
Idle Speed Control ◽  
Bypass Valve ◽  
Input Multiple Output ◽  
Air Valve

Idle speed control remains one of the most challenging problems in the automotive control field owing to its multiple-input, multiple-output structure and the step nature of the disturbances applied. In this paper a simulation model is described for a 4.0 l production engine at idle which includes the standard bypass air valve and spark advance dynamics, as well as the e ects of operating point on cycle-by-cycle combustion-generated torque variations. A model predictive control scheme is then developed for the idle bypass valve and spark advance. The idle speed control algorithm is based on rejecting the torque disturbance using model predictive control for the bypass valve duty cycle while minimizing the transient e ects of the disturbance by adjusting the spark advance. Simulation results are presented to demonstrate the effects of different elements of the controller such as levels of spark offset from minimum spark advance for best torque at idle and feedforward load previews. Compensation of the effects of cyclic variation in combustion torque is also implemented in the controller and its benefits are discussed.

Model Predictive Control and Controller Parameter Optimisation of Combustion Instabilities

2019 ◽  
Vol 36 (2) ◽  
pp. 185-194 ◽  
Author(s):  
I. Yazar ◽  
F. Caliskan ◽  
R. Vepa
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Closed Loop ◽  
Combustion Process ◽  
Open Loop ◽  
Loop Model ◽  
Parameter Optimisation ◽  
Direct Adaptive Control ◽  
The Cost ◽  
Adaptive Control Algorithm

Abstract In this paper the application of model predictive control (MPC) to a two-mode model of the dynamics of the combustion process is considered. It is shown that the MPC by itself does not stabilize the combustor and the control gains obtained by applying the MPC algorithms need to be optimized further to ensure that the phase difference between the two modes is also stable. The results of applying the algorithm are compared with the open loop model amplitude responses and to the closed loop responses obtained by the application of a direct adaptive control algorithm. It is shown that the MPC coupled with the cost parameter optimisation proposed in the paper, always guarantees the closed loop stability, a feature that may not always be possible with an adaptive implementations.

Engine idle speed control using nonlinear multiparametric model predictive control

2020 ◽  
Vol 41 (3) ◽  
pp. 960-979
Author(s):  
Amir‐Mohammad Shamekhi ◽  
Amir Taghavipour ◽  
Amir H. Shamekhi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Speed Control ◽  
Idle Speed ◽  
Idle Speed Control

scholarly journals Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1216
Author(s):  
Adile Akpunar ◽  
Serdar Iplikci
Keyword(s):  
Model Predictive Control ◽  
Permanent Magnet ◽  
Predictive Control ◽  
Mimo Systems ◽  
Wide Spectrum ◽  
Speed Control ◽  
Current Control ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Runge Kutta

Permanent magnet synchronous motors (PMSMs) have commonly been used in a wide spectrum ranging from industry to home appliances because of their advantages over their conventional counterparts. However, PMSMs are multiple-input multiple-output (MIMO) systems with nonlinear dynamics, which makes their control relatively difficult. In this study, a novel model predictive control mechanism, which is referred to as the Runge-Kutta model predictive control (RKMPC), has been applied for speed control of a commercial permanent magnet synchronous motor. Furthermore, the RKMPC method has been utilized for the adaptation of the speed of the motor under load variations via RKMPC-based online parameter estimation. The superiority of RKMPC is that it can take the constraints on the inputs and outputs of the system into consideration, thereby handling the speed and current control in a single loop. It has been shown in the study that the RKMPC mechanism can also estimate the load changes and unknown load disturbances to eliminate their undesired effects for a desirable control accuracy. The performance of the employed mechanism has been tested on a 0.4 kW PMSM motor experimentally for different conditions and compared to the conventional Proportional Integral (PI) method. The tests have shown the efficiency of RKMPC for PMSMs.

Nonlinear output path following control using a two-loop robust model predictive control approach

2021 ◽  
pp. 014233122097142
Author(s):  
Mohammad Ghassem Farajzadeh-Devin ◽  
Seyed Kamal Hosseini Sani
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Robot Manipulator ◽  
Path Following ◽  
Uncertain System ◽  
Loop Model ◽  
Control Approach ◽  
Output Tracking ◽  
Path Following Control ◽  
Nonlinear Uncertain System

In this paper, output tracking of a geometric path for a nonlinear uncertain system with input and state constraints is considered. We propose an enhanced two-loop model predictive control approach for output tracking of a nonlinear uncertain system. Additionally, we propose an optimal version of output path following control problem to improve the controller synthesis. Satisfaction of the dynamical constraints of a system such as velocity, acceleration and jerk limitations is added to the problem introducing a new augmented system. The recursive feasibility of the proposed method is demonstrated, and its robust stability is guaranteed such that relaxation on the terminal constraint and penalty are achieved. To validate the theoretical benefits of the proposed controller, it is simulated on a SCARA robot manipulator and the results are compared with a two-loop model predictive controller successfully.

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