Discrete-time adaptive fuzzy logic control of robotic systems

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

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.

<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>

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, 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.

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 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.

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.

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

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.

To overcome the open-loop control drawbacks of electrostatic actuated MEMS mirror, intelligent control - fuzzy logic control -- is introduced in the closed-loop feedback control for the first time to improve both static and dynamic performance. Furthermore, to enhance the robustness of the mirror actuation system, variable universe adaptive fuzzy logic controller (VFLC) is introduced in the feedback control to overcome the uncertainties such as MEMS fabrication process drifting and non-ideal working conditions. VFLC in feedback control can effectively control mirror with fast switching, no or little overshoot, little vibration, and fine positioning.

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 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.

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.

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).