Partitioning: A unifying framework for adaptive systems, II: Control

Lainiotis, Demetrios G.

doi:10.1109/proc.1976.10289

Cited by 96 publications

(38 citation statements)

References 40 publications

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“…Unlike typical adaptive control based on real-time parameter tuning, multiple model adaptive control (MMAC) [4], [5] uses a bank of linearized models and a family of candidate controllers. These models can be obtained from N operating points in advance.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and a Simple Multiple Model Adaptive Control of PMSM Drive System

Kang¹,

Kim²,

Lee³

et al. 2017

Journal of Power Electronics

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This paper deals with the input-output modeling of a vector controlled PMSM drive system and design of a simple multiple model adaptive control (MMAC) scheme with desired transient responses. We present a discrete-time modeling technique using closed-loop identification that can experimentally identify the equivalent models in the d-q coordinates. A bank of linear models for the equivalent plant of the current loop is first obtained by identifying them at several operating points of the current to account for nonlinearity. Based on these models, we suggest a simple q-axis MMAC combined with a fixed d-axis controller. After the current controller is designed, another equivalent model including the current controller in the speed control loop shall be similarly obtained, and then a fixed speed controller is synthesized. The proposed approach is demonstrated by experiments. The experimental set up consists of a surface mounted PMSM (5 KW, 220V, 8 poles) equipped with a flywheel load of 220kg and a digital controller using DSP (TMS320F28335).

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Section: Introductionmentioning

confidence: 99%

“…The MMAC scheme is to find a proper interpolation of the outputs of predesigned controllers instead of real-time parameter adaptation. The idea of MMAC was proposed by Lainiotis [4]. It has been used for the control of aircraft [6] and for robust damping control of power system [7].…”

Section: Introductionmentioning

confidence: 99%

Modeling and a Simple Multiple Model Adaptive Control of PMSM Drive System

Kang¹,

Kim²,

Lee³

et al. 2017

Journal of Power Electronics

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“…The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11-12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].The purpose of this study is not to introduce one more load forecasting criterion, but it focuses on applying three different methods to real electric load data and evaluating their performance.The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21].…”

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confidence: 99%

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Pappas¹

2017

IJPER

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Abstract. Electric load forecasting is a process that has to be both fast and reliable. An accurate method of load forecasting plays the most crucial role in achieving the aforementioned properties and also is a valuable tool in overcoming a variety of economic and operational problems connected to electrical energy production and distribution. In this study real data is used and the performance of three different techniques for adaptive electric load forecasting is evaluated. The first method is a combination of the well-established multimodel partitioning filter (MMPF) implementing extended Kalman filters (EKF) with Support Vector Machines (SVM), the second is the adaptive MMPF Kalman filters (KF) model and the third one is an artificial three layer feed-forward neural network (ANN). The results indicate that all three methods are reliable, however the combination of MMPF and SVM provides a more accurate load forecasting and at the same time identifies successfully both normal periodic behavior and any unusual activity of the electric grid.Keywords: Adaptive multimodel partitioning filter (MMPF), Support vector machines (SVM), artificial neural networks (ANN), forecasting, Kalman filters (KF), electricity demand load. IntroductionElectrical energy is considered as one of the most important factors that are closely related to both economic and social development. Accurate and fast load forecasting is related to a number of operational functions, such as system safety, power system real-time control, economic planning, grid's maintenance and also fuel planning. The problem of load forecasting has been studied extensively during recent decades. Some of the proposed techniques make use of time series analysis using ARMA [1][2][3][4][5] or ARIMA models [6][7][8][9][10]. Other algorithms achieve load forecasting by adopting evolutionary techniques such as ANN's [11-12], SVM's [13][14] either alone or combined with other methods for the same purpose [15][16][17].The purpose of this study is not to introduce one more load forecasting criterion, but it focuses on applying three different methods to real electric load data and evaluating their performance.The first method is based on a hybrid model that combines the adaptive MMPF [18][19][20], known for its stability, with SVM. The idea of using this method for electric load forecasting came from the fact that it was applied for wind speed prediction with very good results [21]. The data used now is not subjected to any prior -offline manipulation in order to remove weekly and annual seasonality as was done in previous cases [5]. That's why in this case the MMPF implements a bank of extended Kalman filters (EKF) with ARMA models instead of simple Kalman filters (KF) with ARMA models in order to handle data's non-linearities. MMPF with EKF combined with genetic algorithms (GA) were successfully applied in prediction of epilepsy and in the evolution of stock values using biomedical and financial data respectively [22].The second method, presented analytically in [5], impl...

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“…One such adaptive algorithm is the multiple-model adaptive controller (MMAC), which was originally introduced by Lainiotis [7]. Subsequently, it has been used for the control of aircraft [8] and for regulation of hemodynamic variables [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Application of Multiple-Model Adaptive Control Strategy for Robust Damping of Interarea Oscillations in Power System

Chaudhuri

Majumder

Pal

2004

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper demonstrates the application of multiple-model adaptive control (MMAC) strategy for robust damping of low-frequency electromechanical oscillation in an interconnected power system. The control algorithm uses a model-based approach to account for the variability and uncertainty involved in the postdisturbance dynamics of the system. Conventional proportional-integral-derivative (PID) controllers are tuned to achieve the desired performance for each of these models. Using a Bayesian approach, the probability of each model representing the actual power system response is computed in each iteration. The resultant control action is derived as a probability-weighted average of the individual control moves of the controllers. This strategy has been used to design and test a damping controller for a thyristor controlled series compensator (TCSC) device installed in a prototype power system. The control scheme worked satisfactorily following possible disturbances without any prior knowledge about the specific postdisturbance dynamics.Index Terms-Bayesian approach, interarea oscillations, multiple-model adaptive control (MMAC), robustness, thyristor controlled series compensator (TCSC).

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Partitioning: A unifying framework for adaptive systems, II: Control

Cited by 96 publications

References 40 publications

Modeling and a Simple Multiple Model Adaptive Control of PMSM Drive System

Modeling and a Simple Multiple Model Adaptive Control of PMSM Drive System

Application and Comparison of Evolutionary Techniques for Forecasting the Hellenic Grid Electricity Load

Application of Multiple-Model Adaptive Control Strategy for Robust Damping of Interarea Oscillations in Power System

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