PI Control, PI-Based State Space Control, and Model-Based Predictive Control for Drive Systems With Elastically Coupled Loads—A Comparative Study

2011 ◽  
Vol 58 (8) ◽  
pp. 3647-3657 ◽  
Author(s):  
Sönke Thomsen ◽  
Nils Hoffmann ◽  
Friedrich Wilhelm Fuchs
Keyword(s):  
Comparative Study ◽  
State Space ◽  
Predictive Control ◽  
Pi Control ◽  
Model Based ◽  
Drive Systems ◽  
Coupled Loads

scholarly journals State Estimation and Model-Based Control of a Pilot Anaerobic Digestion Reactor

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Finn Haugen ◽  
Rune Bakke ◽  
Bernt Lie
Keyword(s):  
Anaerobic Digestion ◽  
Control Systems ◽  
Predictive Control ◽  
Gas Flow ◽  
The State ◽  
Pi Control ◽  
State Estimator ◽  
Model Based Control ◽  
Methane Gas ◽  
Model Based

A state estimator and various model-based control systems have been designed for a real anaerobic digestion (AD) pilot reactor fed with dairy manure. The model used is a modified Hill model which is a relatively simple dynamical AD process model. The state estimator is an Unscented Kalman Filter (UKF) which uses only methane gas flow measurement to update its states. The model and the state estimates are used in different control systems. One of the control systems aims at controlling the methane gas flow to a setpoint. Simulations indicate that the setpoint tracking performance of a predictive control system is considerably better comparing with PI control, while disturbance compensation is not much better. Consequently, assuming the setpoint is constant, the PI controller competes well with the predictive controller. A successful application of predictive control of the real reactor is presented. Also, three different control systems aiming at retaining the reactor at an operating point where the volatile fatty acids (VFA) concentration has a maximum, safe value are designed. A simulation study indicates that the best control solution among the three alternatives is PI control based on feedback from estimated VFA.

On State Space Model Based Predictive Control

1998 ◽  
Vol 31 (11) ◽  
pp. 301-306 ◽  
Author(s):  
David Di Ruscio ◽  
Bjarne Foss
Keyword(s):  
State Space ◽  
Predictive Control ◽  
State Space Model ◽  
Space Model ◽  
Model Based

Evaluation of Model Based Predictive Control Algorithms for Fractional Horse Power Drives

2015 ◽  
Vol 805 ◽  
pp. 231-240
Author(s):  
Matthias Blank ◽  
Sebastian Wendel ◽  
Philipp Loehdefink ◽  
Armin Dietz
Keyword(s):  
Cost Function ◽  
Predictive Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Small Time ◽  
Control Algorithms ◽  
Model Based ◽  
Computational Requirement ◽  
Drive Systems ◽  
Horse Power

Model based predictive control is a new promising control method in the field of powerelectronics and electrical drives. The main advantages of MPC are simplicity and intuitiveness of thecontrol method. Constraints and nonlinearities of the system can easily be included, which makes thelinearisation of the system unnecessary. By using MPC it is possible to avoid the cascaded structureof common linear control methods and to gain a fast dynamic performance. A disadvantage is theneed to calculate the optimal actuating variable multiple times in every single sampling cycle leadsto a huge requirement of computational power. So far the computational requirement was the greatestbarrier for the practical application of model based predictive control in the field of power electronicsand electrical drive systems. In addition the small time constants of fractional horse power drivescomplicate the application of predictive control algorithms. In this paper, the feasibility of hardwareimplementation of a cost function based Finite Control Set MPC (FCS-MPC) algorithm for directspeed control of fractional horse power drives is explored. The cost function allows to address variouscontrol goals like dynamics of transitions and energy efficiency – an advantage linear conventionalcontrol methods cannot offer. Hitherto there are very few publications for direct predictive speed. Thepresented approach for direct predictive speed control includes a finite number of possible switchingstates of the converter. This considers the discrete nature of power converters and avoids the need formodulation. The basic principle of the control method is presented and the performance is demonstratedby simulations and experimental results for an industrial brushed type DC-motor.

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