Adaptive fuzzy logic DC output controller with minimization harmonies of side network

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.

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.

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

This article proposes power quality improvement in the grid using an adaptive fuzzy logic controller (AFLC) based series hybrid active power filter (SHAPF). The SHAPF injects the voltage to compensate for voltage unbalance, harmonics under balanced, non-sinusoidal supply conditions, neutral current and brings down the total harmonic distortion (THD) to below the recommended value as per IEEE standard 519–1992. This system makes use of the green energy sources with an energy storage unit to meet out the DC-link voltage requirement connected with SHAPF. For producing the required voltages for compensation, the system is implemented by instantaneous reactive power theory adapted with fuzzy logic DC link voltage control. The SHAPF have the ability to generate and preserve the required power to meet the load demand and superior for compensating the voltage interruption of a short period or over a long period. The simulation and experimental studies are realized to confirm the performance of the proposed grid integrated three-phase four-wire series hybrid active power filter.

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.

Adaptive type-2 fuzzy logic control of a bioreactor

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

<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>

Enhancement of fault ride through capability of Doubly Fed Induction Generator (DFIG) is demanding issue in developed countries. In this paper crowbar circuit is implemented for limitation of fault current while the Adaptive Sliding Mode Controller (ASMC) & Adaptive Fuzzy controller (AFC) based on Levenberg-Marquardt algorithm is proposed to enhance fault ride through capability. The Adaptive controller is used to control DC link voltage during normal and faulty condition. The result of controlled DC link voltage with ASMC & AFC is critically and analytically compared with the conventional tuned PI controller. The test bench model is IEEE 5 bus system composed of 9MW DFIG interfaced with 120kv grid.

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.

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.

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systemsIn this paper, an adaptive fuzzy robust output feedback control approach is proposed for a class of single input single output (SISO) strict-feedback nonlinear systems without measurements of states. The nonlinear systems addressed in this paper are assumed to possess unstructured uncertainties, unmodeled dynamics and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds is available. In recursive design, fuzzy logic systems are used to approximate unstructured uncertainties, and K-filters are designed to estimate unmeasured states. By combining backstepping design and a small-gain theorem, a stable adaptive fuzzy output feedback control scheme is developed. It is proven that the proposed adaptive fuzzy control approach can guarantee the all the signals in the closed-loop system are uniformly ultimately bounded, and the output of the controlled system converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by a simulation example and some comparisons.

This study presents a novel optimization algorithm known as the Energy Valley Optimizer Approach (EVOA) designed to effectively develop six optimal adaptive fuzzy logic controllers (AFLCs) comprising 30 parameters for a grid-tied doubly fed induction generator (DFIG) utilized in wind power plants (WPP). The primary objective of implementing EVOA-based AFLCs is to maximize power extraction from the DFIG in wind energy applications while simultaneously improving dynamic response and minimizing errors during operation. The performance of the EVOA-based AFLCs is thoroughly investigated and benchmarked against alternative optimization techniques, specifically chaotic billiards optimization (C-BO), genetic algorithms (GA), and marine predator algorithm (MPA)-based optimal proportional-integral (PI) controllers. This comparative analysis is crucial in establishing the efficacy of the proposed method. To validate the proposed approach, experimental assessments are conducted using the DSpace DS1104 control board, allowing for real-time application of the control strategies. The results indicate that the EVOA-AFLCs outperform the C-BO-based AFLCs, GA-based AFLCs, and MPA-based optimal PIs in several key performance metrics. Notably, the EVOA-AFLCs exhibit rapid temporal response, a high rate of convergence, reduced peak overshoot, diminished undershoot, and significantly lower steady-state error. The EVOA-AFLC outperforms the C-BO-AFLC and GA-AFLC in terms of efficiency, transient responses, and oscillations. In comparison to the MPA-PI, it improves speed tracking by 86.3%, the GA-AFLC by 56.36%, and the C-BO by 39.3%. Moreover, integral absolute error (IAE) for each controller has been calculated to validate the system wind turbine performance. The EVOA-AFLC outperforms other approaches significantly, achieving a 71.2% reduction in average integral absolute errors compared to the GA-AFLC, 24.4% compared to the C-BO-AFLC, and an impressive 84% compared to the MPA-PI. These findings underscore the potential of the EVOA as a robust and effective optimization tool for enhancing the performance of adaptive fuzzy logic controllers in DFIG-based wind power systems.

An adaptive fuzzy logic controller is proposed for trajectory tracking control of robot manipulators. The approach uses quantitative control schemes to ensure global stability and qualitative control schemes to approximate any nonlinear functions caused by disturbances, system uncertainties and interconnections. With the PD control as a preliminary component in maintaining the local stability, we add nonlinear feedback to ensure global stability of the entire system. An adaptive fuzzy logic controller is incorporated into the robot arm control system as a function approximator to compensate the interconnections effect, unmodeled dynamics, friction, gravity force and uncertainties. The stability criteria of the proposed controller is developed using the Lyapunov synthesis approach. Computer simulations show that the proposed adaptive fuzzy controller outperforms traditional control approaches.

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.