Adaptive Fuzzy Logic Controller and Its Application in MEMS Mirror Actuation Feedback Control

In this paper, a fault diagnosis method is presented in order to detection the inter-turn short circuit fault in permanent magnet synchronous motor (PMSM) based on the comparative analysis of two types of controllers. This study consists in the sliding mode control (SMC) of the (PMSM) and the two controllers (fuzzy logic controller (FLC) and Adaptive fuzzy logic controller (AFLC) by taking account of the presence of inter-turn short circuit fault. This study aims to select the most robust controller against the stator faults, load torque variation and reversing rotation speed. Tests regarding these two controllers associated with the sliding mode control were carried on a PMSM 5KW under to different loads, speed and severity degrees of failures for a functioning in healthy and degraded state. The obtained simulation results confirm the superiority and robustness of AFLC-SMC (insensitive to the load torque and the rotation direction, faster response time, decrease the oscillations, and minimization of THD) compared to FLC-SMC.

This paper demonstrates tracking control of a class of feedback linearizable unknown nonlinear dynamical systems, such as a robotic systems, using a discrete-time fuzzy logic controller (FLC). Designing a discrete-time FLC is significant because almost all FLC's are implemented on digital computers. A repeatable design algorithm and a stability proof are examined for an adaptive fuzzy logic controller that uses fuzzy basis functions based on the fuzzy system, unlike most standard adaptive control approaches which use basis vectors depending on the unknown plant. An /spl epsiv/-modification sort of approach to adapt the fuzzy system parameters is examined. Using this adaptive fuzzy logic controller, uniform ultimate boundedness of the closed-loop signals is presented and that the controller achieves tracking. In fact, the fuzzy system designed is a model-free universal fuzzy controller that works for any system in the given class of systems.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Simultaneous adaptive positioning and vibration control of a flexible active composite manipulator with two piezoelectric patches and an active strut are investigated in this paper. First, the configuration of the manipulator is described, and then its dynamics is analyzed. An adaptive fuzzy logic control (AFLC) strategy and an independent modal space adaptive control (IMSAC) scheme are proposed for the active strut motion control and for the manipulator vibration control, respectively. In the AFLC, an adaptive input scaling concept is introduced to adaptively adjust the input of a traditional fuzzy logic controller. As a result, an inverse dead zone is gained to compensate the existing strut dead zone and overcome the dead zone effects. In the IMSAC, a novel adaptive feedback control scheme is developed using the adaptive filtered-x algorithm that is commonly used for adaptive feedforward control. Simulations and experiments demonstrate that the AFLC can provide very accurate positioning control with a relative steady-state positioning error of 0.06%, and that the IMSAC raises the damping ratios of the first two modes of the flexible manipulator 10 times and substantially suppresses the vibration induced by the manipulator motion. </para>

Conventional control depends on the mathematical model of the plant being controlled. When this model is uncertain, intelligent controllers promise better performance. We aim in this paper to compare conventional control to fuzzy logic control (FLC) experimentally. This will be achieved by constructing a hardware station comprising a plant and implementing different control algorithms for the same load conditions or disturbances. FLC requires expertise knowledge of the process operation for FLC parameter setting, and the controller can be only as good as the expertise involved in the design. To make the controller less dependent on the quality of the expert knowledge, we investigate different adaptation schemes to compensate for this deficiency and propose a practical adaptive fuzzy logic controller (AFLC).

In this paper, real-type tabu search (RTS) is proposed, which adaptively changes the range of the neighbor solution to be searched each iteration according to an objective function. We design-the adaptive fuzzy logic controller (AFLC) for speed control of DC servomotor using RTS and neural network (NN). In this paper, the operation of the AFLC for speed control of a DC servomotor is composed of two steps. The first step is for optimizing the weighting value of NN and the initial value of input/output gain needed in real-time adjusting input/output gain of a fuzzy logic controller using a NN using the improved RTS. The second step is for real-time changing of the input/output gain of FLC using NN. We apply the proposed AFLC to the speed control of an actual DC servomotor system. Experimental results show that AFLC has the better control performance than a PI controller in terms of settling time, rise time and overshoot.

Intelligent Universal Transformer (IUT) will comprise in Advanced Distribution Automation (ADA) with a new invention in control and management in future. It evolves with a high speed traditional transformer in addition to power electronic base construction will eventuate to oil elimination, dimensional size and weight reduction. Adaptive Fuzzy Logic Control (AFLC) is an adaptive progressed method with the high system performance capability being raised even on the uncertainty conditions. It enhances system stability, improves flexibility and releases designers from precise mathematical model utilization. Expert designer Knowledge is a critical requirement for conventional fuzzy logic controller (FLC), in contrast the AFLC rules and parameters are generated by adaptive model and human knowledge will downright initialize the first parameters values. In this approach four layers IUT topology is considered for developing the end user service options as 48V DC, reliable power as 240V AC 400HZ and three phase power option. AFLC schemes are proposed for employing current and voltage controllers in input output stages. Real time voltage regulation, automatic sag correction, Harmonic Filtering, energy storage option and dynamic system monitoring are the resulting benefits of using IUT four layers topology. AFLC methodology, leading the system robustness in any cases of grid and load disturbances.

In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.

This paper addresses adaptive fuzzy logic controller for load frequency control of a multi-source two area power system with different power generation sources viz hydro gas & thermal power plant. Expert knowledge is required for conventional fuzzy logic control in developing parameters. Whereas in this method rules are tuned online by adaptation algorithm according to operating condition. The robustness of adaptive fuzzy logic controller has been shown by comparing with conventional fuzzy logic controller and integral controller. The comparison is done based on various performance indices like settling times and peak overshoots for 1 percent step load perturbation.

For amelioration of tracking efficiency, the Maximum power point trackers (MPPT) are very important for photovoltaic (PV) generation. For this purpose here a reformed adaptive fuzzy logic control (FLC) MPPT tracker has been presented to enhance its overall power efficiency and gives rapid transient response under changing weather conditions. For voltage regulation at load bus, the zeta buck-boost converter has been taken for its least voltage ripple. MATLAB/SIMULINK simulation environment and dSPACE DS1104 real time control board is used to test the proposed adaptive fuzzy logic controller based MPPT in variable irradiance level and ambient tempera-ture. The tracking efficiency in this presented method is analyzed in comparison with standard fuzzy logic controller (FLC) and perturb and observe (P and O) MPPT algorithms. The modified AFLC controller gives better tracking efficiency and precise response compared to conventional fuzzy logic controller and P and O MPPT algorithms. Theoretical and experimental results obtained are demonstrated for improved functioning of the system.

This paper presents the results of an investigation in which a PD-like and adaptive Fuzzy Logic Controllers (FLC) are designed to reduce the effects of the most dominant vibration mode on the elastodynamic response of a flexible mechanism system. The FLC schemes are implemented on a flexible four-bar mechanism whose coupler link is retrofitted with a pair of thin plate-type piezoceramic actuator and sensor bonded to the coupler link surface at the high strain location corresponding to the dominant vibration mode. Simulation results indicated that active damping with FLC is a viable strategy in reducing the amplitudes of vibrations. Moreover adaptive FLC with fixed, optimally obtained adaptation rates provides higher attenuation rate than with fuzzy adaptation mechanism. However, a simple robustness test indicated that fuzzy adaptation is more robust tan fixed adaptation.

The present paper describes the development of a new adaptive fuzzy logic controller (AFLC), which can directly employ an adaptive fuzzy/neuro/neuro-fuzzy function approximator to develop a desired controller, based on an ideal controller input-outpu

Hybrid solar wind microgrids with fuzzy logic-based dynamic load balancing

Control of a mobile robot with an onboard robot arm is examined in this paper. Since controllers are implemented on a digital computer, a fuzzy logic controller ( FLC) is designed in discrete-time. This FLC performs feedback linearization of the composite system and provides an inner loop that accounts for possible motion of the onboard arm. A repeatable design algorithm and stability proof is examined for the adaptive FLC that uses basis vectors based on the fuzzy basis function (FBF) system, unlike most standard adaptive control approaches which use basis vectors depending on the unknown plant (e.g. a tediously computed ‘regression matrix’). Novel fuzzy system parameter update schemes are investigated. Using this discrete-time adaptive FLC the uniform ultimate boundedness of the closed-loop signals is presented for the composite system and the controller achieves tracking under a persistence of excitation condition. The fuzzy system parameter updates yield passivity of the closed-loop system. In fact, the fuzzy logic (FL) system designed is a model-free fuzzy controller that can be applied for any state feedback linearizable nonlinear system including non-holonomic systems in the given class of systems. Finally, the cases of maintaining a desired course and speed and that of achieving a desired final orientation (docking angle) as the onboard arm moves to its desired orientation, are considered. Simulation results are presented in order to justify the theoretical conclusions

This paper discussed the attitude control problem of a manipulator frame which is suspended by 2 ropes hitched on its both sides. The dynamic disturbance, which induced by manipulator changing its arm status or by the arm's load varying, will results in the frame swing. To adjust the attitude of the frame and keep it in plumb, a dynamic adjusting mechanism is introduced, and the control model of the mechanism, which employ the adaptive FLC (fuzzy logic control), is discussed. The FLC algorithm is concise and robust, and can avoid the difficulties caused by uncertain parameters and the nonlinear characteristic of the adjusting mechanism. The simulation results of the control model show that the adaptive FLC is feasible and satisfied for this object's attitude control.

Fuzzy logic-based algorithms are widely employed in variable-speed drives. However, although they provide an excellent speed-control performance, they may result in significant current harmonics. These may be reduced by employing a non-linear distribution of fuzzy sets in the membership function of the output variable, albeit with some deterioration in the speed-control performance. A simple adaptive fuzzy logic control algorithm, in which the speed-error threshold may be either fixed or self-tuned, is proposed. Simulations and measurements on vector-controlled permanent-magnet brushless AC drives confirm that the proposed adaptive fuzzy logic control results in an excellent speed-control performance and robustness to parameter variations, while current harmonics are reduced. Its performance is compared with that which is achieved when conventional fuzzy logic and PI speed control are employed.