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1

A multi-model based adaptive reconfiguration control scheme for an electro-hydraulic position servo system

Zhang Zhao, Yang Zhong, Liu Shuchang, Chen Shuang, Zhang Xiaokai

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

185--196

EN

Reliability and safety of an electro-hydraulic position servo system (EHPSS) can be greatly reduced for potential sensor and actuator faults. This paper proposes a novel reconfiguration control (RC) scheme that combines multi-model and adaptive control to compensate for the adverse effects. Such a design includes several fixed models, one adaptive model, and one reinitialized adaptive model. Each of the models has its own independent controller that is based on a complete parametrization of the corresponding fault. A proper switching mechanism is set up to select the most appropriate controller to control the current plant. The system output can track the reference model asymptotically using the proposed method. Simulation results validate robustness and effectiveness of the proposed scheme. The main contribution is a reconfiguration control method that can handle component faults and maintain the acceptable performance of the EHPSS.

2

Decentralized and distributed active fault diagnosis: Multiple model estimation algorithms

Straka Ondřej, Punčochář Ivo

International Journal of Applied Mathematics and Computer Science

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2020

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Vol. 30, no. 2

239--249

EN

The paper focuses on active fault diagnosis (AFD) of large scale systems. The multiple model framework is considered and two architectures are treated: the decentralized and the distributed one. An essential part of the AFD algorithm is state estimation, which must be supplemented with a mechanism to achieve feasible implementation in the multiple model framework. In the paper, the generalized pseudo Bayes and interacting multiple model estimation algorithms are considered. They are reformulated for a given model of a large scale system. Performance of both AFD architectures is analyzed for different combinations of multiple model estimation algorithms using a numerical example.

3

Fuzzy switching for multiple model adaptive control in manipulator robot

Kharabian B., Bolandi H., Smailzadeh S. M., Mousavi Mashhadi S. K.

Journal of Automation Mobile Robotics and Intelligent Systems

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2017

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Vol. 11, No. 1

53--56

EN

In this paper, fuzzy logic is used to perform switching controllers for Multiple Model Adaptive Control (MMAC) in manipulator robot. In the cases which uncertainty bounds of system’s parameters are large, the performance and stability issue of system are considerable concerns. Multiple Model Adaptive Control approach can be useful method to stabilize these kinds of systems. In this control method, the uncertainty bound is divided into several smaller bounds. As a result, the process of stabilization would be streamlined. In this regard, one estimation is obtained for uncertain parameter in every minor bound, and based on estimation errors designed controller can alter. In order to avoid switching controllers and pertinent challenges a summation of controllers with coefficient tuned by fuzzy logic is considered. Simulation results substantiate the efficacy of this method.

4

Nonlinear system identification using heterogeneous multiple models

Orjuela R., Marx B., Ragot J., Maquin D.

International Journal of Applied Mathematics and Computer Science

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2013

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Vol. 23, no. 1

103--115

EN

Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state spaces are not the same and consequently they can be of various dimensions. Thanks to this feature, the complexity of the submodels can be well adapted to that of the nonlinear system introducing flexibility and generality in the modelling stage. This paper deals with off-line identification of nonlinear systems based on heterogeneous multiple models. Three optimisation criteria (global, local and combined) are investigated to obtain the submodel parameters according to the expected modelling performances. Particular attention is paid to the potential problems encountered in the identification procedure with a special focus on an undesirable phenomenon called the no output tracking effect. The origin of this difficulty is explained and an effective solution is suggested to overcome this problem in the identification task. The abilities of the model are finally illustrated via relevant identification examples showing the effectiveness of the proposed methods.

5

Adaptive control applied to parallel kinematic machines

Balan R., Maties V., Hancu O., Stan S.

Problemy Eksploatacji

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2005

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nr 2

201--210

EN

In the paper, different approaches concerning the control of PKM (Parallel Kinematic Machines) are presented. The paper is focused on a Model Based Predictive Control (MBPC) type algorithm which uses on-line simulation and rule-based control. The efficiency and applicability of the proposed algorithm for a SISO motion system are demonstrated through applications.

PL

W artykule przedstawiono różne podejścia dotyczące sterowania w maszynach o kinematyce równoległej. Autorzy prezentują algorytm typu Modelowe Sterowanie Predykcyjne, wykorzystujący symulację on-line i sterowanie oparte na regułach. Efektywność i możliwość zastosowania proponowanego algorytmu dla systemu ruchu SISO zostały zademonstrowane na przykładach.