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In this paper, an adaptive fuzzy control (AFC) system is proposed to realize level position control of two coupled water tanks, often encountered in practical process control. The fuzzy control system includes an adaptive model identifier and controller. The gains of AFC are obtained by using the fuzzy identifier model which is defined by real system outputs and control inputs. The parameters of fuzzy identifier model are adjusted online by using recursive least square algorithm. Because the controller has a recursive form it treats model uncertainties and external disturbances in an implicit way. Thus there is no need to specify uncertainty and disturbances for this controller design in advance. A well-tuned conventional proportional integral (PI) controller is also applied to the two coupled tank system for comparison with the AFC system. Experimentation of the coupled tank system is realized in two different configurations, namely configuration #1 and configuration #2 respectively. In configuration #1, the water level in the top tank is controlled by a pump. In configuration #2, the water level in the bottom tank is controlled by the water flow coming out of the top tank. Experimental results prove that the AFC shows better trajectory tracking performance than PI controller in that the plant transient responses to the desired output changes have shorter settling time and smaller magnitude overshot/undershoot. Robustness of the AFC with respect to water level variation and capability to eliminate external disturbances are also achieved. Experimental results show that AFC is a strong and a practical choice for liquid level control. [ABSTRACT FROM AUTHOR]

This paper describes a new approach to estimate speed of the induction machine from measured terminal voltages and currents. In this approach, the speed is assumed to be unknown constants, because it changes slowly compared to electrical variables such as currents and fluxes. Based on this assumption, a state observer is defined to eliminate the flux information of the machine. Then, a Lyapunov function and a new candidate expression for the time derivative of Lyapunov function that guaranties the stability conditions of the system dynamics are developed from which the speed of the machine is derived. The proposed system is analyzed and verified by both simulation and experiment. [ABSTRACT FROM AUTHOR]

In this paper an observer based on measured and estimated currents and a kinematic model is designed to estimate the velocity and direction angle of an unmanned vehicle (UV). The Lyapunov stability theory is used to establish the stability conditions for the observer. It is shown that the observer will converge in a finite time and the observer gains satisfy the constraints of the stability conditions. The observed velocity and direction angle are used in the closed loop instead of measured ones for sensorless control of the UV. The velocity and direction angle control of the UV are carried out by a well-tuned PI controller. The experimental results show that the proposed observer can be perfectly implemented for sensorless control without using a mechanical sensor, and satisfying results are obtained. In the experiment, command voltages are used rather than measured ones, and therefore only current sensors are needed for the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper a fuzzy controller is applied to velocity and direction angle control of a certain type of wheeled mobile robots called Automated Guided Vehicles (AGVs). The velocity and direction angle of the AGV are controlled to keep the vehicle on desired path. A PI controller is also applied to AGV in order to show the robustness of the fuzzy controller. Experimental results prove that the fuzzy controller shows better tracking performance than the PI controller in terms of robustness, smoothness and fast dynamics. Results are also given for sudden disturbance and extra load conditions and satisfied results are obtained. [ABSTRACT FROM AUTHOR]

This paper presents a continuous approach of sliding-mode current and flux observers for an induction machine. The proposed observer structure both decouples machine equations and makes them completely insensitive to rotor resistance variation. An estimation algorithm based on these observers is proposed to calculate speed and rotor resistance independently. In the proposed algorithm, the speed and rotor resistance are considered to be unknown constants, because the speed and rotor resistance change slowly compared to the electrical variables such as currents and fluxes. The simulation and experimental results demonstrate the good performance of the proposed observer and estimation algorithm and of the overall indirect-field-oriented-controlled system. [ABSTRACT FROM AUTHOR]

In this paper, a novel speed estimation algorithm mathematically derived from current model of an induction motor is presented. The method used here is more advantageous than other techniques due to its simplicity. In this technique, since the speed varies slowly as compared to electrical variables such as currents and fluxes, the speed is assumed to be an unknown constant. Based upon this assumption, a definition of a new speed estimator is reported and its performance is confirmed experimentally. [Copyright &y& Elsevier]

Presents a study which utilized a close loop current model flux observer to estimate the rotor flux, position and velocity of an induction machine. Methodology for the proposed flux and speed observers; Computer simulation results; Performance of the proposed algorithm on speed command signals.

This study presents a novel meta-heuristic optimization method that combines the Henry Gas Solubility Optimization (HGSO) technique with symmetric chaotic systems. By leveraging the randomness of chaotic systems, the parameters of the HGSO algorithm that require random generation are produced through chaotic processes, allowing the algorithm to exhibit chaotic behavior in its pursuit of optimal values. This innovative approach is termed Chaotic Henry Gas Solubility Optimization (CHGSO), with the primary objective of enhancing the performance of the HGSO method. The randomness of the data obtained from chaotic systems was validated using NIST-800-22 tests. The CHGSO method was applied to both 47 benchmark functions and the optimization of parameters for a PID controller utilized in the speed control of a DC motor. To evaluate the effectiveness of the proposed method, it was compared with several widely recognized algorithms in the literature, including PSO, WOA, GWO, EA, SA, and the original HGSO algorithm. The results demonstrate that the proposed method achieved the best performance in 43 of the benchmark functions, outperforming the other algorithms. In the context of controller design, the PID parameters were optimized using the error-based ITSE objective function. According to the controller responses, the proposed method has achieved the best results in the simulation studies, with a settling time of 0.035 and a rise time of 0.014 without overshooting, and in the experimental studies, with a settling time of 0.15 and a settling time of 1.4%. When the results are examined, it is observed that it has achieved successful results in the controller design problem. [ABSTRACT FROM AUTHOR]