This paper proposes an experimental investigation of a four-story structure that is connected to a shaking table. The investigated shaking table is designed with a particular method to produce any kind of vibration amplitude. Also, the whale optimization algorithm is used for the identification of the experimental structure parameters such as mass, stiffness, and damping to show the adaptation of the results collected from the identified model on the results achieved from the linear model. The other idea of this paper is to suggest a novel control strategy that is established by combining proportional-integral-derivative and fuzzy logic control, using an optimization procedure called whale optimization algorithm for optimum tuning of controller coefficients, the hybrid control method is designed. The main objective of the hybrid optimal-based fuzzy-proportional-integral-derivative controller is to reduce the displacement of isolation system without allowing a significant increase in the acceleration of superstructure for both far-field and near-field earthquake excitations. The proposed control algorithm is designed and developed on a four-story shear frame, which contains an active tuned mass damper on each level. Numerical simulations show that the proposed controller which is a combination of two controllers better mitigates the seismic responses of a smart structure excited by a range of real-data earthquakes.

Today, vehicles are increasingly equipped with brushless DC permanent magnet motors due to the nature of sensorless operation. BLDC motor controllers can operate the effective speed and position control in a closed loop feedback system without a position sensor mounted on the shaft. The fractional order PID controller is one of the most common examples of a feedback control algorithm used in many control processes. The BLDC sensorless motor drive has better optimal control over the rotor speed and accuracy with the help of PID fractional controller (FOPID). In this paper, using two meta-heuristic optimization algorithms, the FOPID control parameters including sitting time, rising time, overload and step response stability of the mentioned system were optimized. The results show that the PMBLDC stepper motor response using the proposed genetic algorithm provides better control performance compared to other available methods.

A new framework for controlling load frequency in a complex, interconnected power system with multiple sources has been developed. This framework combines a fuzzy logic controller (FLC) and a tilted integral derivative (TID) controller, creating a self-tuning fuzzy tilted integral derivative (STFTID) controller. The purpose of this controller is to conduct and reduce load frequency perturbations during the operation of a multi-area interconnected multi-source power system. The STFTID controller is optimized using a particle swarm optimization algorithm to minimize the frequency fluctuations effectively. Investigations of the proposed STFTID controller were performed for power systems with generation units of a conventional system and renewable energy sources. In the design process of the STFTID controller, various nonlinearities, uncertainties, and fluctuations are considered to simulate practical challenges. These challenges include generation rate constraints, governor deadband, and communication time delays (as the sources of nonlinearity), as well as fluctuations caused by step load switching and the connection of renewable power plants to the system. The STFTID controller is compared with the proportional integral derivative (PID), titled integral derivative (TID), and integral tilted-derivative (I-TD) controllers. Simulation results show that the developed STFTID controller significantly enhances the system frequency control under various applied conditions, including multi-step load perturbation, renewable power plant integration, communication time delays, and generation rate constraints.

This paper dealing with a novel algorithm based on features of fuzzy- proportional–integral–derivative controllers to estimate heat flux on the inverse heat conduction problems. The main structure of Fuzzy- proportional–integral–derivative is a proportional–integral–derivative controller in which the proportional, integrator and the derivative gains are obtained online by fuzzy system. The input of the algorithm is the measured temperatures within the model. In each time-step, the smart controller calculates the proper heat flux in order to adjust the measured temperature with the desired input temperature. The model studied a flat plate with an insulated surface and an active level that affects the variable heat flux at the time. The variation of heat flux with time can be considered to be constant, step, and triangular. The measured temperatures are obtained at the active and inactive surface with numerical simulation. The effect of noise level at the measurement temperatures on the accuracy of the proposed method is investigated. The estimations and error analysis indicate that this algorithm is very successful in estimating the different forms of heat flux with different amounts of noise and the different thermocouple positions in the wall. The accuracy of the proposed sequence method is higher than that of the Tikhonov method.

Milk is one of the important dairy foods, which forms an essential building block in the feed formulation for infant and growing children, and adults alike. However, the quality of the final product largely depends on the temperature of the pasteurization process. It is, therefore, a necessity to ensure that optimum temperature is maintained during pasteurization process, as over-temperature kills all the essential nutrients contained in the final product and similarly, low temperature is not desirable as the final product will not yield the desired nutritional value. As a result, the application of optimal temperature control scheme is a critical requirement for milk pasteurization. It is, on this background, that this paper presents the use of a Proportional (P), integral (I), derivative (D) abbreviated as PID controller for optimal control of temperature in the milk pasteurization process. The milk pasteurization temperature was modeled based on the first law of thermodynamics, while three different tuning techniques namely; Zigler-Nichols (ZN), Chien-Hrones-Reswick (CHR) and Cohen-Coon (CC) were employed to tune the PID controller for optimal control of the milk pasteurization temperature. The control schemes were simulated in MATLAB/Simulink, and the performance of each tuning technique was evaluated using the rise time, settling time, peak amplitude, and overshoot. Results showed that ZN tuned PID controller gave the lowest rise time, settling time, and peak amplitude of 0.177s, 0.34s, and 0.993, respectively, while the lowest overshoot of 0% was attained by both ZN and CHR. Based on these results, CC tuned PID controller exhibited moderate rise time of 1.02s, settling time of 6.49s, and overshoot of 5.67%, indicating that its performance is comparatively preferred with respect to other tuning techniques investigated. The results of this research find application in diary industries as it provides insight into the appropriate tuning technique for the PID controller to ensure optimum temperature control during milk pasteurization.

integral and differential of fractional order are important notion that is often used in dealing with Frechet geometry. Based on pseudo-operations given by monotone and continuous function g, we study pseudo-derivative and pseudo-integral of fractional order through this paper.

Quadrotor control has been noted for its trouble as the consequence of the high maneuverability, system nonlinearity and strongly coupled multivariable. This paper deals with the simulation depend on proposed controller of a quadrotor that can overcome this trouble. The mathematical model of quadrotor is determined using a Newton-Euler formulation. Fractional Order Proportional integral derivative (FOPID) controller tuned by genetic algorithm (GA) is investigated to control and stabilization the position and attitude of quadrotor using feedback linearization. This controller is used as a reference to compare its results with Proportional integral derivative (PID) controller tuned by GA. The control structure performance is evaluated through the response and minimizing the error of the position and attitude. Simulation results, demonstrates that position and attitude control using FOPID has fast response, better steady state error and RMS error than PID. By simulation the two controllers are tested under the same conditions using SIMULINK under MATLAB2015a.

In this paper, using the State Dependent Riccati Equation (SDRE) method, we propose a Robust Optimal integral Sliding Mode controller (ROISMC) to guarantee an optimal control law for a quadrotor which has become increasingly important by virtue of its high degrees of manoeuvres ability in presence of unknown time-varying external disturbances and actuator fault. The robustness of the controller is ensured by an integral Sliding Mode controller (ISMC). Subsequently, based on Luenberger linear state estimator, the control algorithm is reformed and the actuator’ s faults are detected. Moreover, design of the controller is based on Lyapunov method which can provide the stability of all system states during the tracking of the desired trajectory. The stability of suggested algorithm is verified via the execution of sudden maneuvers subjected to forcible wind disturbance and actuator faults while performing accurate attitude and position tracking by running an extensive numerical simulation. It is comprehended that the proposed optimal robust method can achieve much better tracking capability compared with conventional sliding mode controller.