Self-learning Fuzzy Controller Research Articles

A novel self-learning fuzzy predictive control method for the cement mill: Simulation and experimental validation

A novel self-learning fuzzy predictive control method for the cement mill: Simulation and experimental validation

Research on Self-Learning Fuzzy Control of Controllable Excitation Magnetic Suspension Linear Synchronous Motor

This paper studies the problem of different parameters adjustment and poor anti-interference capability for the magnetic suspension control system of controllable excitation linear synchronous motor (CELSM). Based on operating mechanism of CELSM, the mathematical model of magnetic suspension system is established. In addition, based on the Takagi–Sugeno fuzzy model-based approach, a self-learning fuzzy controller with parameter adjustment mechanism is designed. The controller includes a reference model that displays the desired characteristics of the magnetic suspension control system, a fuzzy inverse model that adjusts the fuzzy rules in real time and reduces the errors between the reference model output and the system output to zero, the rules and parameters adjustment units that change the adjustment degree of the rules according to the output proportion factor of the fuzzy inverse model, and the errors between the system output and the reference model output. A self-learning mechanism for modifying the output value of a standard fuzzy controller. At last, the simulation model is set up and compared with other control methods, the result shows that the suspension height control system has better anti-interference ability and tracking effect under the self-learning fuzzy control strategy, as well as the ability to deal with the change of the internal parameters of the magnetic suspension system. And the feasibility and the superiority of the modified control method are verified.

Optimal Control of Microgrid Operation Based on Fuzzy Sliding Mode Droop Control

In the application of microgrid systems that include wind power, photovoltaic systems, diesel generators, and battery storage, the cooperative control and optimisation of power distribution between power sources is a major issue. Recently, the droop control has been used widely in microgrids. However, droop control relies mainly on the line parameter model between the grid and the load. Therefore, to improve the performance of the microgrid, the optimal control of microgrid operation based on the fuzzy sliding mode droop control method is considered in this paper. To begin, system parameters were obtained by modeling droop control with self-learning fuzzy control strategy. Then, to improve the accuracy of the power distribution in the multi-micro source system, the nonlinear differential smoothing control method was employed. Finally, by comparing the self-learning fuzzy sliding mode control based on drooping strategy and the traditional droop control method, it was demonstrated that the method proposed can effectively reduce the fluctuation of the bus voltage and improve the output voltage quality of the microgrid system.

Dual-Loop Self-Learning Fuzzy Control for AMT Gear Engagement: Design and Experiment

Gear engagement is the most important part in gear-shift process of automated manual transmission (AMT). However, it is practical to encounter complicated nonlinearities, uncertainties, and multistage characteristics in the system model, so the controller design for the AMT gear engagement becomes challenging. This paper proposes a dual-loop self-learning fuzzy control framework. In the outer loop, the self-learning rules based on fuzzy logic is designed to adjust desired trajectory of actuator motor. In the inner loop, the gear engagement is divided into three stages, and a fuzzy controller with model reference self-learning algorithm is designed, which controls the actuator motor to track the desired trajectory. Besides, the control parameters could be adjusted to be optimal automatically when the parameters change. Results of simulations and experiments indicate that the proposed method is able to realize the smooth and fast control of gear engagement. In addition, the self-learning fuzzy controller can be extended to deal with other nonlinear systems with uncertain and even unknown parameters.

Fuzzy self-learning control of glass tempering and annealing temperature based on the optimised genetic big data analysis algorithm

The temperature control of glass tempering and annealing process has the problems of the time varying parameters and time lag characteristic. In order to solve this problem, this paper proposes a self-learning fuzzy controller based on improved genetic algorithm and big data analysis. The proposed algorithm can quickly search the global optimal factor by using the big data temperature. Thus the fuzzy control rules are perfected and corrected. The simulation results demonstrate that the proposed control algorithm is suitable for systems with time varying parameters and time lag characteristic.

An Interleaved Equalization Architecture with Self-Learning Fuzzy Logic Control for Series-Connected Battery Strings

This paper proposes an interleaved equalization architecture for series-connected lithium-ion battery strings, which can deliver energy from a battery module to the cells in the next adjacent battery module, resulting in an enhancement of the equalization current and efficiency. Particularly, the global equalization is achieved without the need of additional stage circuits to balance among modules, leading to a small size and low cost. A two-module equalizer based on resonant LC converter and buck converter with soft switching is implemented to verify the validity of the proposed interleaved architecture. Furthermore, a self-learning fuzzy logic control (SLFLC) algorithm is employed to online regulate the equalization period based on the voltage difference among cells and the cell voltage, not only greatly abbreviating the balancing time but also effectively preventing overequalization. The SLFLC has the outstanding advantages of high balancing precision, easy implementation, and strong robustness. Experimental results demonstrate that the proposed equalizer has good balancing performances with soft switching and high efficiency, and achieves zero-voltage gap among cells. Moreover, the proposed SLFLC algorithm abridges the total equalization time by about 27%, and reduces the equalization cycles by about 59% compared with the traditional control algorithm.

Magnetic induction of hyperthermia by a modified self-learning fuzzy temperature controller.

The aim of this study involved developing a temperature controller for magnetic induction hyperthermia (MIH). A closed-loop controller was applied to track a reference model to guarantee a desired temperature response. The MIH system generated an alternating magnetic field to heat a high magnetic permeability material. This wireless induction heating had few side effects when it was extensively applied to cancer treatment. The effects of hyperthermia strongly depend on the precise control of temperature. However, during the treatment process, the control performance is degraded due to severe perturbations and parameter variations. In this study, a modified self-learning fuzzy logic controller (SLFLC) with a gain tuning mechanism was implemented to obtain high control performance in a wide range of treatment situations. This implementation was performed by appropriately altering the output scaling factor of a fuzzy inverse model to adjust the control rules. In this study, the proposed SLFLC was compared to the classical self-tuning fuzzy logic controller and fuzzy model reference learning control. Additionally, the proposed SLFLC was verified by conducting in vitro experiments with porcine liver. The experimental results indicated that the proposed controller showed greater robustness and excellent adaptability with respect to the temperature control of the MIH system.

Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-to-Turn Missiles

This paper proposes an adaptive self-learning fuzzy autopilot design for uncertain bank-to-turn (BTT) missiles due to external disturbances and system errors from the variations of the aerodynamic coefficients and control surface loss. The self-learning fuzzy systems called extended sequential adaptive fuzzy inference systems (ESAFISs) are utilized to compensate for these uncertainties in an adaptive backstepping architecture. ESAFIS is a real‐time self-learning fuzzy system with simultaneous structure identification and parameter learning. The fuzzy rules of the ESAFIS can be added or deleted based on the input data. Based on the Lyapunov stability theory, adaptation laws are derived to update the consequent parameters of fuzzy rules, which guarantees both tracking performance and stability. The robust control terms with the adaptive bound-estimation schemes are also designed to compensate for modeling errors of the ESAFISs by augmenting the self-learning fuzzy autopilot control laws. The proposed autopilot is validated under the control surface loss, aerodynamic parameter perturbations, and external disturbances. Simulation study is also compared with a conventional backstepping autopilot and a neural autopilot in terms of the tracking ability. The results illustrate that the designed fuzzy autopilot can obtain better steady‐state and transient performance with the dynamically self-learning ability.

Model reference self-learning fuzzy control method for automated mechanical clutch

Automated mechanical clutch is a critical component in vehicle powertrain, whose operations have great influence on fuel economy, comfort, and drivability. However, the control of clutch is a challenging problem due to the nonlinearities and uncertainties, which has attracted extensive attention. This paper proposed a model reference self-learning fuzzy control method with considering clutch wear. Fuzzy control is a simple but effective method to deal with the nonlinear system, but the determined fuzzy rules cannot be suitable for system variations due to clutch wear. Therefore, a self-learning algorithm based on referent model is introduced into fuzzy controller. Then, simulations are carried out in MATLAB/SIMULINK, and the results show that the method solves the problem of clutch wear effectively compared with the fuzzy control without self-learning ability. Finally, a test bench is designed and experiments are carried out to verify the validity of the proposed method.

Self-Learning Fuzzy Sliding-Mode Control for a Water Bath Temperature Control System

A self-learning fuzzy sliding-mode controller (SLFSMC) is proposed to control the temperature of a water bath. The SLFSMC system automatically tunes the rule bases using a rule modifier and the updating value of each rule is based on the fuzzy firing weight. In addition, this controller can be used for on-line learning in real-time control systems. In order to illustrate the performance of the proposed control method, it is compared with a proportional derivative-type fuzzy control (PDFC) and a gain-tuning fuzzy control (GTFC). These three algorithms are applied to a water bath temperature control and are simulated under the same conditions. The effect of load disturbance, the response to control, the tracking performance, and suitable sampling time are determined for each system. The simulation results show that the SLFSMC has superior characteristics, is more simple to use and has a fast response, so the SLFSMC performs better than the PDFC and GTFC.

Real-time closed-loop control of human heart rate and blood pressure.

Prolonged bed rest has significant negative impacts on the human body, particularly on the cardiovascular system. To overcome adverse effects and enhance functional recovery in bedridden patients, the goal is to mobilize patients as early as possible while controlling and stabilizing their cardiovascular system. In this paper, we used a robotic tilt table that allows early mobilization by modulating body inclination and automated leg movement to control the cardiovascular variables heart rate (HR) or systolic or diastolic blood pressures (sBP, dBP). The design and use of a control system is often done with a simulation model of a plant, but the time-variant and nonlinear nature of the cardiovascular system and subject-specific responses to external stimuli makes the modeling and identification challenging. Instead, we implemented an intelligent self-learning fuzzy controller that does not need any prior knowledge about the plant. The controller modulates the body inclination in order to adjust the cardiovascular parameters, with leg movement considered as a perturbing factor to the controller. The controller performance was evaluated in six healthy subjects. Measured mean values of HR, sBP, and dBP differed from desired reference values by 1.11 beats/min, 5.10 mmHg, and 2.69 mmHg, respectively. With this new control strategy, HR and dBP could be successfully controlled within medically tolerable ranges (deviations < 2.5 beats/min and < 5 mmHg from desired values, respectively). The control of sBP was less accurate; the results suggest that simultaneous control of multiple input stimuli rather than only adaptive automatic change of the tilt table angle might improve the controllability.

Fast Steering Mirror Control Using Embedded Self-Learning Fuzzy Controller for Free Space Optical Communication

Coarse and Fine tracking in free space laser communication is very crucial. This paper presents architecture for laser beam acquisition, tracking and pointing mechanism. The centroid of the received image beam is calculated and then error is computed using reference position. Embedded self-learning fuzzy controller (SLFC) is used to drive the fast steering mirror mechanism to point the laser beam on receiver detector. The SLFC is embedded in hardware using Arduino development board. The performance of the SLFC is compared with the standard PID control. It is shown that the proposed architecture successfully track the laser beam and the SLFC shows the robustness against model uncertainties and reject disturbances.

Study and Simulation on Fuzzy Control Model for Crop Disease

In order to solve the precision medicine problem of crop diseases control, the conventional fuzzy model which applies to crop diseases control was established. Subordinate function, reasoning pattern and domain of discourse of fuzzy model were select. The computational process of the weight of five drug need fuzzy control sub model was mainly introduced and AHP and Neural Networks were adopted. Self-learning fuzzy control model for crop disease control was proposed to make control rules study and better adapt the on-site work environment. The result of MATLAB showed an expected effect of controlling the spray rate of the system. The study provided a new control strategy and method to realize intelligent control of precision spraying for crop.

Particle Swarm Optimization Based Adaptive Strategy for Tuning of Fuzzy Logic Controller

This paper presents a new method for learning and tuning a fuzzy logic controller automatically by means of a particle swarm optimization (PSO). The proposed self-learning fuzzy logic control that uses the PSO with adaptive abilities can learn the fuzzy conclusion tables, their corresponding membership functions and fitness value where the optimization only considers certain points of the membership functions. To exhibit the effectiveness of proposed algorithm, it is used to optimize the Gaussian membership functions of the fuzzy model of a nonlinear problem. Moreover, in order to design an effective adaptive fuzzy logic controller, an on line adaptive PSO based mechanism is presented to determine the parameters of the fuzzy mechanisms. Simulation results on two nonlinear problems are derived to demonstrate the powerful PSO learning algorithm and the proposed method is able to find good controllers better than neural controller and conventional controller for the target problem, cart pole type inverted pendulum system.

Based on the Application of Self-Learning Fuzzy PID Control in Tension Control System

Tension control system is one of the widely used systems in modern industrial control systems. In tension control system PID parameter setting is one of the main problems affecting the control system performance. However, conventional PID parameter is difficult to set. This article describes a PID fuzzy controller based on adaptive learning, which can change according to the deviation and deviation rate to adjust parameters in real time. Practice shows that such self-learning fuzzy PID controller has better control performance than conventional PID controller in tension control.

Self-learning fuzzy logic controllers for pursuit–evasion differential games

This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. The system learns autonomously without supervision or a priori training data. Two novel techniques are proposed. The first technique combines Q( λ )-learning with function approximation (fuzzy inference system) to tune the parameters of a fuzzy logic controller operating in continuous state and action spaces. The second technique combines Q( λ )-learning with genetic algorithms to tune the parameters of a fuzzy logic controller in the discrete state and action spaces. The proposed techniques are applied to different pursuit–evasion differential games. The proposed techniques are compared with the classical control strategy, Q( λ )-learning only, reward-based genetic algorithms learning, and with the technique proposed by Dai et al. (2005) [19] in which a neural network is used as a function approximation for Q-learning. Computer simulations show the usefulness of the proposed techniques.

Self-learning fuzzy control of scan tracking measurement in copying manufacture

Aiming at the effect of the geometric shape of the model surface on the precision of scan tracking control, technologies of model surface geometric feature extraction and predictive control technology are researched. A self-learning fuzzy controller is designed and realized, which automatically adjusts the scan tracking velocity according to the smoothness of the model surface. Finally, through the Matlab simulation experiment, the realizability and application effect of the studied self-learning control method of surface scan tracking velocity are verified.

A Fuzzy Sliding Mode Controller and Its Application

An approach of self-learning fuzzy sliding-mode control which combines fuzzy control with the sliding-mode control, is presented for the tracking control of a class of nonlinear systems with the parameter uncertainties. The fuzzy control rules are updated through on-line learning, which make the output of fuzzy control approximate to sliding-mode equivalent control along the direction of making sliding-mode asymptotic stable. Based on Lyapunov theory, the asymptotic stability of the overall systems is proved. The proposed method is applied to some electrohydraulic servo systems, and the results of simulation show that the satisfied control precision and stability can be obtained by using proposed method for the systems.

Construction of self-learning fuzzy controllers using autonomous adaptive control methodology

A method of constructing adaptive fuzzy controllers using autonomous adaptive control methodology is considered. Knowledge in the system is represented in the form of fuzzy production rules. New rules are automatically generated by clustering empirical data obtained using substractive method in the course of system operation. The system is adapted with application of a special quantity calculated for each rule, the “adequacy degree”, which specifies the weight of the rule in the course of control. The method developed can be used for constructing applied control systems of dynamic objects. This opportunity is shown experimentally using the problems of balancing an inverted pendulum and stabilizing the angular motion of a spacecraft.

AN ENHANCED PATIENT CONTROLLED ANALGESIA (EPCA) FOR THE EXTRACORPOREAL SHOCK WAVE LITHOTRIPSY (ESWL)

The conventional patient-controlled-analgesia (PCA) can be further improved in terms of less drug consumption and better pain control if advanced feedback control algorithm is applied. The purpose of this research was to develop an enhanced patient-controlled analgesia (EPCA) using a hierarchical fuzzy logic control system with two levels and to apply it to patients in an extracorporeal shock wave lithotripsy (ESWL) operation. The two-level control system utilizes the conventional PCA as a basic level on the top of which is added an adaptive self-learning fuzzy logic control (SLFLC) level. Twenty-three patients involved in these clinical trials were to study the clinical effectiveness of EPCA for the management or pain during the ESWL process. A two-button device was designed, one button for severe pain (SP) and another for mild/moderate pain (MP) to investigate the frequencies of analgesic demands or delivery related to either severe or mild/moderate pain. The results show that the average percentage of demand/delivery to total demand/delivery due to MP is significantly higher than that due to SP, which implies the pain levels that most patients experienced were mostly mild to moderate in the ESWL process with the EPCA. The drug consumption of alfentanil with the EPCA is comparable to that with a fixed FLC; however, it is much lower than that with conventional PCA. In conclusion, the efficiency of the therapy, pain relief, and the patients' satisfaction of pain management with the EPCA in the ESWL operation are superior to those with the conventional PCA.