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Minimum-Delay Link Determination in Networked control Systems Using Asexual Reproduction Optimization

Minimum-Delay Link Determination in Networked Control System...

引用

ieee symposium on computational intelligence in control and automation

作者： Farasat, A. Amani, A. M. Menhaj, M. B. Univ Tehran Tehran Iran Amirkabir Univ Technol Tehran Iran Amirkabir Univ Technol Fac Elect Engn Tehran Iran

ISBN: (纸本)9781424427529

Networked control systems (NCS) have gained an increasing attention in recent years due to their flexibility and cost reduction. The two main problems affecting stability and performance of closed-loop NCS are packet delay and drop. The problem of delay is severe in large scale NCS where the sensor-controller-actuator are connected via a network with large number of switches and routers. In this paper, we propose a real time optimization algorithm, called Asexual Reproduction Optimization (ARO) which is inspired by asexual reproduction, to find the least delay link in the control loop. Simulation results reveal that the proposed algorithm remarkably outperforms the recently cited Genetic Algorithm (GA) in finding the minimum delay link.

关键词： Networked control systems

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Modeling and control of a Nonholonomic Wheeled Mobile Robot with Wheel Slip Dynamics

Modeling and Control of a Nonholonomic Wheeled Mobile Robot ...

引用

ieee symposium on computational intelligence in control and automation

作者： Tian, Yu Sidek, Naim Sarkar, Nilanjan Vanderbilt Univ Dept Mech Engn Nashville TN 37209 USA Vanderbilt Univ Dept Elect Engn Nashville TN 37209 USA

ISBN: (纸本)9781424427529

In order to model a Wheeled Mobile Robot (WMR) system to improve its maneuverability in a real environment, wheel dynamics, which may violate no-slipping and pure rolling constraints, needs to be studied. In this paper, wheel dynamics with slip is modeled and introduced into the robot overall dynamics. Wheel slip phenomenon is captured and at the same time accommodated. Considering both lateral and longitudinal slip phenomena due to traction, a WMR system becomes an underactuated dynamic system. A time-invariant discontinuous feedback law is developed to asymptotically stabilize the system to the desired configuration with exponential convergence rate. Simulation results are provided to validate the theoretical results.

关键词： Wheels

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Fuzzy Lyapunov Decentralized control of Takagi-Sugeno Interconnected Descriptors

Fuzzy Lyapunov Decentralized Control of Takagi-Sugeno Interc...

引用

ieee symposium on computational intelligence in control and automation

作者： Jabri, Dalel Guelton, Kevin Manamanni, Noureddine Abdekrim, Mohamed N. Univ Reims CReSTIC URCA Moulin Housse BP 1039 F-51687 Reims 2 France Univ Gabes MACS Lab Gabes 6029 Tunisia

ISBN: (纸本)9781424427529

This paper deals with decentralized stabilization of nonlinear systems composed of interconnected Takagi-Sugeno fuzzy descriptors. To ensure the stability of the overall closed-loop system, a set of decentralized Parallel Distributed Compensations (PDC) controllers is employed. The stability conditions are then derived into Linear Matrix Inequalities (LMI) using a fuzzy Lyapunov function for less conservatism. Nevertheless, it contains decision parameters that are not available in practice. So the LMIs are casted into relaxed quadratic conditions using simple assumptions. Finally, a numerical example is proposed to illustrate the effectiveness of the suggested decentralized approach.

关键词： Linear matrix inequalities

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Adaptive unified predictive control applied to first-order-plus-dead-time processes to eliminate unknown class of deterministic disturbance

Adaptive unified predictive control applied to first-order-p...

引用

ieee symposium on computational intelligence in control and automation

作者： Lin, Shu-Kai Lee, An-Chen Department of Mechanical Engineering National Chiao-Tung University Hsinchu Taiwan

ISBN: (纸本)9781424499038

A novel Adaptive Unified Predictive control method (Adaptive-UPC) is proposed and applied to the first-order-plus-dead-time (FOPDT) process models with model uncertainties under unknown deterministic disturbances. Based on Indirect Self-tuning Regulator, this new control structure is constructed by combining UPC controller, Recursive Least-Squares Estimation with forgetting factor (RLS with forgetting factor) and Variable Regression Estimation (VRE). The simulation results show that the method can be effectively applied to first-order-plus-dead-time (FOPDT) process models with uncertain process parameters (gain, time constant and dead-time) and unknown deterministic disturbance. © 2011 ieee.

关键词： Uncertainty analysis

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Biomimetic Hybrid Feedback Feedforword Adaptive Neural control of Robotic Arms 4

Biomimetic Hybrid Feedback Feedforword Adaptive Neural Contr...

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ieee symposium on computational intelligence in control and automation (cica)

作者： Pan, Yongping Yu, Haoyong Natl Univ Singapore Dept Biomed Engn Singapore 117575 Singapore

ISBN: (纸本)9781479945306

This paper presents a biomimetic hybrid feedback feedforword (HFF) adaptive neural control for a class of robotic arms. The control structure includes a proportional-derivative feedback term and an adaptive neural network (NN) feedforword term, which mimics the human motor learning and control mechanism. Semiglobal asymptotic stability of the closed-loop system is established by the Lyapunov synthesis. The major difference of the proposed design from the traditional feedback adaptive approximation-based control (AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as NN inputs. Such a slight difference leads to several attractive properties, including the convenient NN design, the decrease of the number of NN inputs, and semiglobal asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach has two unique features: 1) all above attractive properties are achieved by a much simpler control scheme;2) the bounds of plant uncertainties are not required to be known. Simulation results have verified the effectiveness and superiority of this approach.

关键词： Adaptive control neural network robotic manipulator feedforword control human motor learning control

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Intelligent PD-type fuzzy controller design for mobile satellite antenna tracking system with parameter variations effect

Intelligent PD-type fuzzy controller design for mobile satel...

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ieee symposium on computational intelligence in control and automation

作者： Lin, Jium-Ming Chang, Po-Kuang Dept. of Communication Engineering Chung-Hua University Hsin-Chu Taiwan Ph.D. Program in Engineering Science College of Engineering Chung-Hua University Hsin-Chu Taiwan

ISBN: (纸本)9781424499038

This research applied both the traditional and the fuzzy control methods for mobile satellite antenna tracking system design. Firstly, the antenna tracking and the stabilization loops were designed according to the traditional bandwidth and phase margin requirements. However, the performances would be degraded if the tacking loop gain is reduced due to parameter variations. On the other hand a PD-type fuzzy controller was also applied for tracking loop design. It can be seen that the system performances obtained by the fuzzy controller were better for both low and high antenna tracking loop gains, and the tracking loop gain parameter variations effect can be reduced. © 2011 ieee.

关键词： Fuzzy control

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A novel training method based on variable structure systems theory for fuzzy neural networks

A novel training method based on variable structure systems ...

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ieee symposium on computational intelligence in control and automation

作者： Cigdem, Ozkan Kayacan, Erdal Khanesar, Mojtaba Ahmadieh Kaynak, Okyay Teshnehlab, Mohammad Department of Electrical and Electronics Engineering Bogazici University 34342 Istanbul Turkey Department of Control Engineering Faculty of Electrical Engineering K. N. Toosi University of Tech. 16314 Tehran Iran

ISBN: (纸本)9781424499038

Uncertainty is an inevitable problem in real-time industrial control systems and, to handle this problem and the additional one of possible variations in the parameters of the system, the use of sliding mode control theory-based approaches is frequently suggested. In this paper, instead of using a conventional sliding mode controller, a sliding mode control theory-based learning algorithm is proposed to train the fuzzy neural networks in a feedback-error-learning structure. The parameters of the fuzzy neural network are tuned by the proposed algorithm not to minimize the error function but to ensure that the error satisfies a stable equation. The parameter update rules of the fuzzy neural network are derived, and the proof of the learning algorithm is verified by using the Lyapunov stability method. The proposed method is tested on a real-time servo system with time-varying and nonlinear load conditions. © 2011 ieee.

关键词： Fuzzy neural networks

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Decentralized direct I-term fuzzy-neural control of an anaerobic digestion bioprocess plant

Decentralized direct I-term fuzzy-neural control of an anaer...

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ieee symposium on computational intelligence in control and automation

作者： Baruch, Ieroham S. Hernandez, Sergio-Miguel Department of Automatic Control CINVESTAV-IPN Mexico City Mexico

ISBN: (纸本)9781424499038

The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in fixed bed and recirculation tank. The distributed parameter analytical model of the digestion bioprocess is used as a plant data generator. It is reduced to a lumped system using the orthogonal collocation method, applied in four collocation points (plus one point of the recirculation tank), which are used as centres of the membership functions of the fuzzyfied plant output variables with respect to the space variable. The local and global weight parameters and states of the proposed FNMM identifier are learnt by the Levenberg-Marquardt learning algorithm and they are implemented by a Hierarchical Fuzzy-Neural Multi-Model Direct controller with Integral Term. The graphical simulation results of the digestion system direct fuzzy-neural I-term learning control, exhibited a good convergence, and precise reference tracking. © 2011 ieee.

关键词： Membership functions

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On control-specific derivation of affine Takagi-Sugeno models from physical models: Assessment criteria and modeling procedure

On control-specific derivation of affine Takagi-Sugeno model...

引用

ieee symposium on computational intelligence in control and automation

作者： Kroll, A. Durrbaum, A. Department of Measurement and Control University of Kassel 34125 Kassel Germany

ISBN: (纸本)9781424499038

Models are commonly derived and their performance is assessed wrt. minimal prediction error on a closed data set. However, if no perfect model can be used, the degrees of freedom in modeling should be used to adjust the model to application-specific metrics. For model-based controller design, control-oriented performance metrics (e.g. performance wrt. to control-critical properties) are important, but not primarily prediction (i.e. prognosis- and simulation-oriented) ones. This motivates the derivation of control-specific models. The contribution introduces structured and quantitative measures on "model suitability for control" for the class of affine dynamic Takagi-Sugeno models. A method is suggested that derives control-specific dynamic models from a physical model given as a set of nonlinear differential equations. Within a case study, the proposed method demonstrates its significance: Using control-specific models improves control performance metrics such as set-point tracking quality, stability region and energy efficiency. © 2011 ieee.

关键词： Degrees of freedom (mechanics)

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Neural Network Fitting for Input-Output Manifolds of Online control Laws in Constrained Linear Systems 4

Neural Network Fitting for Input-Output Manifolds of Online ...

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ieee symposium on computational intelligence in control and automation (cica)

作者： do Carmo, Samronne N. de Almeida, Marconi O. de Castro, Flavio A. Campos, Rafael F. R. Araujo, Jose M. Dorea, Carlos E. T. Inst Fed Educ Ciencia & Tecnol Bahia Dept Automacao & Sistemas BR-40301015 Salvador BA Brazil Univ Fed Rio Grande do Norte Ctr Tecnol Dept Engn Comp & Automacao BR-59078900 Lagoa Nova Natal RN Brazil

ISBN: (纸本)9781479945306

control techniques for systems with constraints on control and state are somewhat attractive, mainly in cases where these constraints represent safety or critical points of operation. An important approach for control of constrained linear systems is based on the concept of set invariance, whose main advantages are the inclusion of constraints in the whole design, the non-conservative nature of the controllers and the ability to cope with noise measurement and disturbance entering in the system. Some disadvantage are a possibly high complexity of the control law for higher order systems or the absence of an analytical, off-line control law in some cases, as, for instance, in the output feedback case. The online computation of the control input at each step is ever possible, but the computational cost involved may turn the solution impracticable in the case of systems with fast dynamics. Neural networks, on the other hand, is an interesting alternative for function approximation, and works well in capturing the characteristics of the input-output manifold of the online control law, starting from a training set generated by simulation of the control system. In this paper, neural networks are applied to substitute in an efficient way the online control computation. A real case based example is used to verify the effectiveness of the proposed neural controller.

关键词： feedback function approximation linear systems neurocontrollers constrained linear system input-output manifold neural network fitting noise measurement online control law set invariance computational modeling Manifolds Neural networks Noise Output feedback Runtime function approximation Linear system Noise measurement Manifolds Output feedback Neurocontrollers Neural network Runtime control law computational modeling Noise Feedback

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