PID, Fuzzy and Model Predictive Control Applied to a Practical Nonlinear Plant

2016 ◽  
Vol 4 (1) ◽  
pp. 19-42
Author(s):  
Kai Borgeest ◽  
Peter Josef Schneider
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Cooling System ◽  
Control Strategies ◽  
Fan Noise ◽  
Nonlinear Properties ◽  
Control Schemes ◽  
Advanced Control ◽  
Save Energy ◽  
Nonlinear Plant

For the cooling system of a mobile machine with m control variables and with n=m correction variables different control strategies have been investigated in order to minimize power to save energy and to reduce fan noise with sufficient cooling. The plant is nonlinear and not identified. Three different kinds of controllers have been investigated in several variations, i.e. fuzzy control, PI(D) and model predictive control (MPC). 14 different criteria have been used for evaluation. In many respects a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant. A problem of advanced control schemes with unidentified plants is the difficulty to prove stability.

Comparison of Control Strategies by the Example of the Cooling Fan Control of a Mobile Machine

2015 ◽  
pp. 500-526
Author(s):  
Kai Borgeest ◽  
Peter Josef Schneider
Keyword(s):  
Prediction Model ◽  
Control Problem ◽  
Cooling System ◽  
Control Strategies ◽  
Fan Noise ◽  
Nonlinear Properties ◽  
Linear Controller ◽  
Cooling Fan ◽  
Save Energy ◽  
Mobile Machine

In order to compare different control strategies, the cooling system of a mobile machine has been chosen. The example control problem was to run the cooling system for m control variables and with n=m correction variables in a way to minimize power in order to save energy and to reduce fan noise while maintaining sufficient cooling. The plant is nonlinear. Three different kinds of controllers have been investigated in several variations (i.e. fuzzy control, PI[D], and Model Predictive Control [MPC]). Fourteen different criteria have been used in this chapter for evaluation. In many respects, a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant.

Finite-Set Model-Predictive Control Strategies for a 3L-NPC Inverter Operating With Fixed Switching Frequency

2018 ◽  
Vol 65 (5) ◽  
pp. 3954-3965 ◽  
Author(s):  
Felipe Donoso ◽  
Andres Mora ◽  
Roberto Cardenas ◽  
Alejandro Angulo ◽  
Doris Saez ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategies ◽  
Switching Frequency ◽  
Finite Set

Model Predictive Control of a Pumped Two-Phase Cooling System With Microchannel Heat Exchangers

2018 ◽  
Author(s):  
Oyuna Angatkina ◽  
Andrew Alleyne
Keyword(s):  
Heat Flux ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Heat Exchangers ◽  
Cooling System ◽  
High Heat ◽  
High Heat Flux ◽  
Two Phase ◽  
Cooling Systems ◽  
Two Phase Cooling

Two-phase cooling systems provide a viable technology for high–heat flux rejection in electronic systems. They provide high cooling capacity and uniform surface temperature. However, a major restriction of their application is the critical heat flux condition (CHF). This work presents model predictive control (MPC) design for CHF avoidance in two-phase pump driven cooling systems. The system under study includes multiple microchannel heat exchangers in series. The MPC controller performance is compared to the performance of a baseline PI controller. Simulation results show that while both controllers are able to maintain the two-phase cooling system below CHF, MPC has significant reduction in power consumption compared to the baseline controller.

scholarly journals A Novel Fuzzy Model Predictive Control of a Gas Turbine in the Combined Cycle Unit

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guolian Hou ◽  
Linjuan Gong ◽  
Xiaoyan Dai ◽  
Mengyi Wang ◽  
Congzhi Huang
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Control Strategies ◽  
Control Process ◽  
Fuzzy Model ◽  
Weight Coefficient ◽  
Combined Cycle ◽  
Generation Process ◽  
Selection Mechanism

The complex characteristics of the gas turbine in a combined cycle unit have brought great difficulties in its control process. Meanwhile, the increasing emphasis on the efficiency, safety, and cleanliness of the power generation process also makes it significantly important to put forward advanced control strategies to satisfy the desired control demands of the gas turbine system. Therefore, aiming at higher control performance of the gas turbine in the gas-steam combined cycle process, a novel fuzzy model predictive control (FMPC) strategy based on the fuzzy selection mechanism and simultaneous heat transfer search (SHTS) algorithm is presented in this paper. The objective function of rolling optimization in this novel FMPC consists of two parts which represent the state optimization and output optimization. In the weight coefficient selection of those two parts, the fuzzy selection mechanism is introduced to overcome the uncertainties existing in the system. Furthermore, on account of the rapidity of the control process, the SHTS algorithm is used to solve the optimization problem rather than the traditional quadratic programming method. The validity of the proposed method is confirmed through simulation experiments of the gas turbine in a combined power plant. The simulation results demonstrate the remarkable superiorities of the adopted algorithm with higher control precision and stronger disturbance rejection ability as well as less optimization time.

ON MODEL PREDICTIVE CONTROL OF QUASI-RESONANT CONVERTERS

2009 ◽  
Vol 18 (07) ◽  
pp. 1167-1183 ◽  
Author(s):  
FARZAD TAHAMI ◽  
MEHDI EBAD
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Control Strategies ◽  
Sampling Interval ◽  
Control Synthesis ◽  
Resonant Converters ◽  
Control Input ◽  
Model Approximation ◽  
Dynamic Matrix

In this paper, different model predictive control synthesis frameworks are examined for DC–DC quasi-resonant converters in order to achieve stability and desired performance. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the system that is updated during each sampling interval, by making use of a nonlinear model. In this paper, by using and comparing these methods, a new control scheme for quasi-resonant converters is described. The proposed control strategy is applied to a typical half-wave zero-current switching QRC. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.

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