Design of a Fuzzy Networked Control Systems. Priority Exchange Scheduling Algorithm

This work presents a supervisory control strategy for Networked Control Systems (NCSs). This shows the identification and control of the plant using fuzzy theory. The fuzzy model incorporates the delay dynamics within the fuzzy rules based upon a real-time hierarchical scheduling strategy. A hierarchical scheduling Priority Exchange algorithm is used based upon codesign strategy following mutual correlation among control and network algorithms in order to bounded time delays. A system of magnetic levitation is presented as a case study

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

PSO Tuned Flatness Based Control of a Magnetic Levitation System

Investigation on the application of flatness-based feedback linearization to the magnetic levitation model of INTECOTm Maglev system is presented in this paper. The MAGLEV system dynamics studied consists of a set of third order nonlinear differential equations. Using computational techniques proposed by Levine, it is verified that the ball position is the flat output. The derived flat output is applied in the construction of a nonlinear control law used to control the levitation to a set point as well as tracking a sine function trajectory. The controller gains are obtained and optimized using particle swarm optimization. The simulation results compared very well with the default PID control. Real-time and non real-time simulation using the MATLAB/ SIMULINK real workshop environment is presented

Two-Dimensional Fuzzy Sliding Mode Control of a Field-Sensed Magnetic Suspension System

This paper presents the two-dimensional fuzzy sliding mode control of a field-sensed magnetic suspension system. The fuzzy rules include both the sliding manifold and its derivative. The fuzzy sliding mode control has advantages of the sliding mode control and the fuzzy control rules are minimized. Magnetic suspension systems are nonlinear and inherently unstable systems. The two-dimensional fuzzy sliding mode control can stabilize the nonlinear systems globally and attenuate chatter effectively. It is adequate to be applied to magnetic suspension systems. New design circuits of magnetic suspension systems are proposed in this paper. ARM Cortex-M3 microcontroller is utilized as a digital controller. The implemented driver, sensor, and control circuits are simpler, more inexpensive, and effective. This apparatus is satisfactory for engineering education. In the hands-on experiments, the proposed control scheme markedly improves performances of the field-sensed magnetic suspension system

A Fuzzy Networked Control System Following Frequency Transmission Strategy

At present, network control systems employ a common approximation to solve the connectivity issue due to time delays coupled with external factors . However, this approach tends to be complex in terms of time delays, and the inherent local phase is missing. Therefore, it is necessary to study the behavior of the delays as well as the integration of the differential equations of these bounded delays. The related time delays need to be known a priori, but from a dynamic real-time perspective in order to understand the dynamic phase behavior. The objective of this paper is to demonstrate the inclusion of the data frequency transmission and time delays that are bounded as parameters of the dynamic response from a real-time scheduling approximation, considering the local phase situation. The related control law is designed considering a fuzzy logic approximation for nonlinear time delays coupling. The main advantage is the integration of this behavior through extended state space representation. This keeps certain linear and bounded behavior leading to a stable situation during an events presentation, based on an accurate data transmission rate. An expected result is that the basics of the local phase missing as a result of the local bounded time delays from the lack of tide synchronization conforms to the modeling approximation

Nonlinear dynamic modeling and fuzzy sliding-mode controlling of electromagnetic levitation system of low-speed maglev train

The electromagnet levitation system (ELS) of low-speed maglev train is taken as the research object. The nonlinear dynamics and control law of ELS are discussed. Specifically, by employing the Euler-Lagrange’s method, a nonlinear dynamic model is constructed for the single-ELS. Then, the linear control law is studied, which has a disadvantage of weak robustness. To improve the performance of the controller, a fuzzy sliding-mode control law is proposed. According to the dynamic nonlinear model, a novel sliding surface which can make the system reach the stable point within the finite time is presented. Moreover, the fuzzy inference method is utilized to slow down the speed of the states crossing the sliding surface. The simulation results demonstrate that the global robustness of external disturbance and parameter perturbation can be achieved through the proposed control law. And the chattering phenomenon can be reduced significantly. Finally, the experiments are also implemented to examine its practical dynamic performance of the proposed control law

Nonlinear dynamic modeling and fuzzy sliding-mode controlling of electromagnetic levitation system of low-speed maglev train

The electromagnet levitation system (ELS) of low-speed maglev train is taken as the research object. The nonlinear dynamics and control law of ELS are discussed. Specifically, by employing the Euler-Lagrange’s method, a nonlinear dynamic model is constructed for the single-ELS. Then, the linear control law is studied, which has a disadvantage of weak robustness. To improve the performance of the controller, a fuzzy sliding-mode control law is proposed. According to the dynamic nonlinear model, a novel sliding surface which can make the system reach the stable point within the finite time is presented. Moreover, the fuzzy inference method is utilized to slow down the speed of the states crossing the sliding surface. The simulation results demonstrate that the global robustness of external disturbance and parameter perturbation can be achieved through the proposed control law. And the chattering phenomenon can be reduced significantly. Finally, the experiments are also implemented to examine its practical dynamic performance of the proposed control law

Fuzzy supervisory control of Rotor-AMB system and bias current optimization

This paper presents the Fuzzy Supervisory Control (FSC) and bias current optimization algorithm developed for a Rotor-Active Magnetic Bearing (Rotor-AMB) system. Since the AMBs are inherently unstable, it is essential to use a controller for a stable levitation. Initially, a closed loop control of Rotor-AMB system is achieved and the whole model is built in a virtual environment (MatlabÒ/ Simulink). With the help of simulation model the system dynamics is analyzed. In addition to that, the effect of bias current on energy consumption is studied for both unidirectional and differential control current strategies. A diffential current control gives better efficiency compared with a unidirectional approach when the bias current is variable. Thus, optimizing the bias current according to the operational conditions is beneficial for magnetically levitated systems. According to the results obtained an energy optimization algorithm (EOA) is developed. Since the system is nonlinear and has some uncertainties a Fuzzy Supervisory Controller is used to overcome these problems. Then, the real time model of the FSC and EOA are achieved using dSPACE. Finally an experimental set up is formed and FSC+EOA are applied to the Rotor-AMB system. It is shown that, FSC and the algorithm developed make the system tolerant to higher unbalances and disturbances with minimum energy consumption. Keywords: Active magnetic bearing, fuzzy logic, energy.Bu &ccedil;alışmada, Rotor-Aktif Manyetik Yatak (AMY) sistemi i&ccedil;in tasarlanılan Bulanık Denetleyici Kontrol&ouml;r (BDK) ve sistemde harcanılan enerjiyi azaltmayı sağlayan enerji eniyileştirme algoritması (EİA) sunulmuştur. Başlangı&ccedil;ta sistem dinamiğini incelemek i&ccedil;in sisteme ait benzetişim modeli Matlab&Ograve;/ Simulink ortamında ger&ccedil;ekleştirilmiştir. Oluşturulan model ile denge akımının enerji kaybına olan etkisi literat&uuml;rde yer alan iki farklı y&ouml;ntemi i&ccedil;in incelenmiştir. Elde edilen sonu&ccedil;lardan hareket ile enerji eniyileştirme algoritması geliştirilmiş ve sistemin kontrol&uuml;nde kullanılan BDK&rsquo;ya bağlanmıştır. Ger&ccedil;ekleştirilen BDK+EİA, dSPACE mod&uuml;l&uuml; yardımı ile Rotor-AMY sistemine uygulanmıştır. Sonu&ccedil;ta sistem parametrelerinin aynı anda değiştirilmesi ile dinamik yapının uygun katılıkta ve en az enerji harcayacak bi&ccedil;imde &ccedil;alıştığı deneysel olarak g&ouml;sterilmiştir.&nbsp;Anahtar Kelimeler: Aktif manyetik yatak, bulanık mantık, enerji