Adaptive fuzzy trajectory control for biaxial motion stage system

This paper presents a theoretical study on a model-reference adaptive fuzzy logic controller for vector-controlled permanent magnet AC servomotor drives. In the proposed system, fuzzy logic is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The results are compared with that provided by a nonadaptive fuzzy controller. The implementation of proposed adaptive fuzzy controller is also discussed.

In this paper, temperature monitoring of sterilizing equipment system was established with the help of fuzzy and self tuning Adaptive fuzzy logic controller designed in Lab VIEW software. It combines the advantages of both fuzzy logic and self tuning Adaptive fuzzy logic controller. The implementation attempts to rectify the errors between the measured value and the set point which helps to achieve efficient temperature control. The Adaptive fuzzy controller uses defined rules to control the system based on the current values of input variables and temperature errors. The simulation results presented in order to evaluate the proposed method. The result shows that self tuning Adaptive fuzzy logic controller was tolerant to disturbance and the temperature control is most accurate.

formula omitted] tracking-based decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale nonlinear systems

In this paper, an adaptive fuzzy control scheme for fuzzy systems with multiple delayed state perturbations, time- varying uncertain parameters, and disturbances. These delayed state perturbations, uncertainties, and external disturbances in the fuzzy systems are assumed to be any uncertain bounded but unknown continuous functions. An improved adaptation law with sigma-modification is proposed to estimate such unknown bounds, and on the basis of the updated values of these unknown bounds, a continuous memoryless state feedback controller is constructed for achieving robust stability of the closed-loop system. Based on Lyapunov stability theorem, the proposed adaptive robust fuzzy controller can guarantee that the state converges asymptotically to zero in the presence of multiple delayed state perturbations, time-varying uncertain parameters, and disturbance. In addition, it is also shown that this improved adaptation law with sigma-modification can be applied to the general adaptive control problems to obtain some more exact control results. Finally, an inverted pendulum system is given to demonstrate the validity of the proposed design method.

This paper addresses the problem of state estimation and the tracking control scheme combining fuzzy control technique for a class of MIMO nonlinear systems, in which some variables are not measurable, with plant uncertainties and external disturbances First, the nonlinearities are approximated using neural networks, and then are modeled based on linear difference inclusions(LDI), which makes the structure of the desired filter simpler and parameter turning easier. A unified framework is established to solve the addressed H ∞ filtering problem by exploiting linear matrix inequality (LMI) approach. The proposed control law is based on indirect adaptive fuzzy control and uses two on-line estimations. The adaptive fuzzy tracking control using Variable Structure (VS) control technique is derived based on Lyapunov criterion to resolve system uncertainties and external disturbances. This is done in such a way that all states of the system are bounded and the H ∞ tracking performance is achieved. Finally, A numerical example is investigated to demonstrate the feasibility and the effectiveness of the proposed methodology.

A reinforced adaptive fuzzy sliding-mode controller was developed to track the desired state for a certain class of nonlinear dynamic system. Initially, adaptive fuzzy logic was used to derive the update laws for approximating the uncertain nonlinear functions of a dynamic system. Subsequently, a reinforced adaptive mechanism and sliding-mode control were incorporated into the adaptive fuzzy control scheme. In the reinforced adaptive mechanism, both the model and controller parameters were adjusted simultaneously to attenuate the effects caused by the unmodeled dynamics and disturbances. Performance analysis demonstrated the superiority of the proposed reinforced adaptive law over the conventional direct adaptive scheme regarding faster tracking and convergence of parameters. To verify its effectiveness and extend its application, the proposed reinforced adaptive fuzzy sliding-mode controller was applied to balance a two-wheeled robot steering on a bumpy road. A number of simulations and experiments were performed. In addition, a conventional direct adaptive scheme under the same environment was also performed for comparison. These results demonstrated that the balance performance of a nonlinear dynamic two-wheeled robot improved because of embedding the proposed reinforced adaptive fuzzy sliding-mode controller into the microcontroller.

To achieve a better robustness and get a static response of a certain target missiles in longitudinal control, a mathematical model of target missiles is established. The uncontrolled trajectory, the predetermined trajectory and the requirements of the predetermined trajectory are analyzed next. Then the trajectory instruction angle is designed to ensure the stable flight of the target missiles system. To solve the problem that the PID controller cannot adjust parameters online and the output is easy to be disturbed by external noise, the fuzzy adaptive PID control is designed. It enhanced the robustness of the target missiles by fuzzy control, and also has a good response by using PID control. The simulation experiments show the fuzzy adaptive control system can track the predetermined trajectory rapidly and smoothly, and the static error and overshoot were significantly reduced. When the system are affected by noise, the fuzzy adaptive PID control can achieve a good robustness than traditional PID controller.

Research on A Fuzzy Adaptive PID Control Method for Four Rotor UAV Control System With the continuous improvement of national living standards, people have higher and higher requirements for the reliability of power supply. The emergence of unmanned aerial vehicle inspection technology provides a new idea to solve the contradiction between the increasing equipment scale and the increasing inspection requirements. However, at present, the stability of the traditional proportion integration differentiation (PID) control system of the four rotor unmanned aerial vehicle (UAV) is not high enough. During the inspection, there are accidents of drones hitting power equipment from time to time. In view of the above problems, this paper studies the fuzzy adaptive PID control method to support the stable flight of four rotor UAV. The principle of fuzzy control is analyzed. The fuzzy control method and PID control method are deeply integrated. A fuzzy adaptive PID control system is designed. The universe and membership function of the relevant control parameters of the system are formulated. The tuning rules of fuzzy adaptive PID control system are formulated. Finally, the improved control system is simulated. The simulation results show that the fuzzy adaptive PID control algorithm can effectively improve the stability, reliability and emergency ability of UAV. This method can effectively improve the adaptability of UAV to perform complex missions in complex environments. The research results have guiding significance for expanding the application field of UAV.

This paper reports on a study whose objective is to explore the potential and compare the performance of intelligent adaptive control methods. Specifically, the performance of PID plus an adaptive neural network compensator (ANNC) is compared with the performance of a fuzzy adaptive PID controller. The application is position control of a pneumatic gantry robot. Both controllers were carefully tuned to provide a fair comparison. Experimental results were collected for the tracking of a sine wave. Both adaptive controllers were found to improve tracking performance over fixed gain PID by upwards of 70%. However, the tuning procedure for the fuzzy controller is judged to be more intuitive in nature and hence, more practical than that for ANNC. The fuzzy adaptive controller uses a novel rule set that is reduced in size from that used in previous studies.

Adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with non-lower triangular structure

An adaptive fuzzy controller has been designed to develop a high-performance fault-tolerant SRM drive. The fuzzy controller continuously adapts its properties to regulate the machine torque as desired by the drive system even under fault conditions. The adaptation of the fuzzy membership functions results in extended conduction period and increased peak current of the healthy phases to deliver the commanded torque, as much as possible. The adaptive fuzzy controller provides smooth torque output with minimum ripple, even under fault conditions, yielding a high-performance SRM drive with fault-tolerant capability.

Abstract- This paper introduces a linear structure of wind turbine, operator and the pitch angle controller. Then, a new method of adaptive fuzzy on the variable speed wind turbines was provided and it was compared with PID and Fuzzy Logic Control ler and the simulation results were analyzed. So when the results of each simulation using PID and Fuzzy controllers and adaptive fuzzy PID controllers provided, the observed advantages and disadvantages of the rotor speed and wind turbine power can be introduced. It can be seen that in PID method the high overshoot is discussed as a disadvantage while it is overcome by using fuzzy controller and overshoot will decrease. The time of reaching to a sustained speed increases slightly and in adaptive fuzzy PID controller, the less overshoot has provided a good and effective performance for system response. The use of adaptive fuzzy PID controller causes the system does not have any steady-state error and at all moments of time, the response rate is better than PID and fuzzy controller. The importance of the amount of overshoot and rate fluctuations is that by reducing these parameters in addition to reducing the cost of preventive care, maintenance and depreciation, the fluctuations in electricity generated by induction generator also greatly reduced.

Application of adaptive fuzzy control to ac machines

Harmony search algorithm and Lyapunov theory based hybrid adaptive fuzzy controller for temperature control of air heater system with transport-delay

A novel design technique for high performance drives employing both H/spl infin/ optimal control and fuzzy control is proposed in this paper. The fuzzy control design is equipped with an adaptive learning algorithm to achieve H/spl infin/ tracking performance with external disturbances. It gives elevation to the selection of optimal performance weights without any trial and error attempt. The idea is to establish a link between H/spl infin/ optimal control design and fuzzy control design, so as to provide H/spl infin/ tracking design with more intelligence and achieve better performance with fuzzy control design. The effect of both fuzzy logic approximation error and external disturbance on the tracking error is attenuated to an assigned level. The control strategy does not require explicit knowledge of the motor/load dynamics, which is a useful feature when dealing with parameter and load uncertainties. The robustness of the proposed methodology is displayed for different types of trajectories. Experimental and simulation results suggest that the effect of both the fuzzy approximation error and external disturbance on the tracking error can be attenuated efficiently by the H/spl infin/ adaptive fuzzy control. Accordingly, the proposed design method is suitable for the robust tracking control of the uncertain nonlinear drive systems and is an attractive control design philosophy. Test results obtained indicate excellent tracking performance for both speed and position. Test results illustrate the validity of the proposed approach.