Home About us Contact
|
Home About us Contact | ||
|
|
||
Distributed Compensation (distributed + compensation)
Selected AbstractsAPPROXIMATION PROPERTIES OF TP MODEL FORMS AND ITS CONSEQUENCES TO TPDC DESIGN FRAMEWORKASIAN JOURNAL OF CONTROL, Issue 3 2007Domonkos Tikk ABSTRACT Tensor Product Distributed Compensation (TPDC) is a recently established controller design framework, that links TP model transformation and Parallel Distributed Compensation (PDC) framework. TP model transformation converts different models to a common representational form: the TP model form. The primary aim of this paper is to investigate the approximation capabilities of TP model forms, because the universal applicability of TPDC framework strongly relies on it. We point out that the set of functions that can be approximated arbitrarily well by TP forms with bounded number of components lies no-where dense in the set of continuous functions. Consequently, in a class of control problems this property necessitates the usage of tradeoff techniques between the accuracy and the complexity of the TP form, which is easily feasible within the TPDC framework unlike in analytic models. [source] H, fuzzy control design of discrete-time nonlinear active fault-tolerant control systemsINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 10 2009Huai-Ning Wu Abstract This paper is concerned with the problem of H, fuzzy controller synthesis for a class of discrete-time nonlinear active fault-tolerant control systems (AFTCSs) in a stochastic setting. The Takagi and Sugeno (T,S) fuzzy model is employed to exactly represent a nonlinear AFTCS. For this AFTCS, two random processes with Markovian transition characteristics are introduced to model the failure process of system components and the fault detection and isolation (FDI) decision process used to reconfigure the control law, respectively. The random behavior of the FDI process is conditioned on the state of the failure process. A non-parallel distributed compensation (non-PDC) scheme is adopted for the design of the fault-tolerant control laws. The resulting closed-loop fuzzy system is the one with two Markovian jump parameters. Based on a stochastic fuzzy Lyapunov function (FLF), sufficient conditions for the stochastic stability and H, disturbance attenuation of the closed-loop fuzzy system are first derived. A linear matrix inequality (LMI) approach to the fuzzy control design is then developed. Moreover, a suboptimal fault-tolerant H, fuzzy controller is given in the sense of minimizing the level of disturbance attenuation. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method. Copyright © 2008 John Wiley & Sons, Ltd. [source] Approximation and complexity trade-off by TP model transformation in controller design: A case study of the TORA system,ASIAN JOURNAL OF CONTROL, Issue 5 2010Zoltán Petres Abstract The main objective of the paper is to study the approximation and complexity trade-off capabilities of the recently proposed tensor product distributed compensation (TPDC) based control design framework. The TPDC is the combination of the TP model transformation and the parallel distributed compensation (PDC) framework. The Tensor Product (TP) model transformation includes an Higher Order Singular Value Decomposition (HOSVD)-based technique to solve the approximation and complexity trade-off. In this paper we generate TP models with different complexity and approximation properties, and then we derive controllers for them. We analyze how the trade-off effects the model behavior and control performance. All these properties are studied via the state feedback controller design of the Translational Oscillations with an Eccentric Rotational Proof Mass Actuator (TORA) System. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Application of two-loop robust control to air-conditioning systems,ASIAN JOURNAL OF CONTROL, Issue 6 2009Gongsheng Huang Abstract This paper presents the design and application of a two-loop robust controller for temperature control in air-conditioning systems. A Takagi-Sugeno fuzzy model with uncertain local models is developed to describe the associated nonlinearities and uncertainties in the operation of the air handling units. Parallel distributed compensation is used to design the global control law. The local control law consists of two loops: an inner-loop integral controller and an outer-loop min-max predictive controller with short prediction horizon. A discounting scheme is developed to weight the contribution of the two loops. Experimental results are presented which show that the proposed strategy can achieve acceptable control performance with a minimum of onsite tuning. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Stability analysis for discrete-time fuzzy system by utilizing homogeneous polynomial matrix function,ASIAN JOURNAL OF CONTROL, Issue 6 2009Likui Wang Abstract The purpose of this paper is to investigate the stability of nonlinear systems represented by a Takagi-Sugeno discrete-time fuzzy model. The homogeneous polynomial matrix function (HPMF) is developed to obtain new stabilization conditions. Applying the HPMF to the non-parallel distributed compensation (non-PDC) law and non-quadratic Lyapunov function, some new stabilization conditions are obtained by the following two means: (a) utilizing the popular idea of introducing additional variables for some fixed degree of the HPMF; and (b) increasing the degree of the HPMF. It is shown that the conditions obtained with approach (a) are less conservative than some sufficient stability conditions available in the literature to date. It is also shown that as the degree of HPMF increases the conditions obtained under (b) become less conservative. An example is provided to illustrate how the proposed approaches compare with other techniques available in the literature. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] An Automatic Building Approach To Special Takagi-Sugeno Fuzzy Network For Unknown Plant Modeling And Stable ControlASIAN JOURNAL OF CONTROL, Issue 2 2003Chia-Feng Juang ABSTRACT In previous studies, several stable controller design methods for plants represented by a special Takagi-Sugeno fuzzy network (STSFN) have been proposed. In these studies, the STSFN is, however, derived directly from the mathematical function of the controlled plant. For an unknown plant, there is a problem if STSFN cannot model the plant successfully. In order to address this problem, we have derived a learning algorithm for the construction of STSFN from input-output training data. Based upon the constructed STSFN, existing stable controller design methods can then be applied to an unknown plant. To verify this, stable fuzzy controller design by parallel distributed compensation (PDC) method is adopted. In PDC method, the precondition parts of the designed fuzzy controllers share the same fuzzy rule numbers and fuzzy sets as the STSFN. To reduce the controller rule number, the precondition part of the constructed STSFN is partitioned in a flexible way. Also, similarity measure together with merging operation between each neighboring fuzzy set are performed in each input dimension to eliminate the redundant fuzzy sets. The consequent parts in STSFN are designed by correlation measure to select only the significant input terms to participate in each rule's consequence and reduce the network parameters. Simulation results in the cart-pole balancing system have shown that with the proposed STSFN building approach, we are able to model the controlled plant with high accuracy and, in addition, can design a stable fuzzy controller with small parameter number. [source] | ||||||||||