Effective control strategies are necessary for enhancing wind turbine efficiency and lifespan. The purpose of this paper is to control the rotational speed of a wind turbine by adjusting the pitch angle of the blades. Initially, a one-degree-of-freedom closed-loop controller is designed based on the complementary sensitivity function, followed by a PID and a Modified PID controller. Finally, their performance in tracking the desired rotational speed of the wind turbine, which includes step and sinusoidal signals, is compared. The 1DOF controller introduced oscillation, while the PID controller eliminated oscillation but exhibited steady-state error. To preserve the advantages of the PID controller and eliminate steady-state error, an integrator term (1/s) has been added to the controller. Consequently, the Modified PID controller has been designed. The results of the Modified PID controller show no steady-state error or peak overshoot percentage for step inputs. While, the PID and the one-degree-of-freedom closed-loop controller in tracking the step reference input result in steady-state errors of 1.464% and 0.497% and peak overshoot percentages of 23.688% and 84.389%, respectively. During the implementation of the one-degree-of-freedom closed-loop controller, significant oscillations occur while tracking the desired input. The oscillations occurring in the rotor speed can lead to increased fatigue in various wind turbine components, which is an undesirable outcome. However, no oscillations are observed in rotor speed with the PID and Modified PID controllers. Finally, according to the obtained results, the Modified PID controller demonstrates superior performance compared to the other controllers in tracking the desired rotor speed

In this paper, a satellite attitude control with observer-based Modified proportional-integral-derivative (PID) controller is studied in the presence of disturbance and uncertainty. First-order dynamic has been used to model the reaction wheel as control actuator with considering the practical limit of the maximum output torque. In observer method, saturation and windup are feedbacks to control algorithm to modify control signal. control gains have been obtained by optimization method based on genetic evolutionary algorithm with penalty method and for the performance criterion of the absolute mean of the pointing error. To evaluate the performance, a comparison study has been done between Modified controller and classic controller versus control parameters, phase plane diagram, limit cycle, uncertainties, amplitude and frequency of external disturbance. To fair comparison, all conditions in optimization and initial values are selected identical for two controllers. Comparing results show better performance in the Modified controller, anti-windup, and avoid saturation. In the face of perturbations and limit cycle diagrams, the performance of the Modified controller is clearly comparable to that of a classical controller. In addition, the performance of the two controllers is studied versus moment of inertia, actuator model, disturbance frequency, disturbance amplitude, and maximum momentum uncertainties. The behavior of Modified controller is generally more appropriate and pointing accuracy is better. For example, control accuracy in Modified PID is about 15% better than classical algorithm under moment of inertia uncertainty.

In this paper, the performance of a single-axis attitude control with pulse-width pulse-frequency (PWPF) modulation is enhanced using a Modified proportional-integral-derivative (PID) controller for arigid satellite with on-off thruster actuators. For this purpose, the well-known observer-based PIDapproach is utilized. The on-off thruster actuator is modeled with a constant delay followed by asecond-order binomial transfer function. The modulator update frequency is limited to 40 Hz as an inputto the on-off thruster actuators. In this study, the design criteria of pointing accuracy, overshoot of theattitude response, fuel consumption, and the number of thruster firings are considered for a step externaldisturbance (with different values). The parameters of the observer-based PID controller are tuned using parametric search method. Simulation results show that the fuel consumption and settling time of theobserver-based approach are considerably decreased with respect to those of PID controller with PWPFmodulator. Moreover, the overshoot of the observer-based approach is omitted. Finally, the robustnessof the observer-based Modified PID controller is investigated in presence of uncertainties in satellitemoment of inertia and thrust level of on-off actuators.

The purpose of this paper is to design a fractional order PID (FOPID) controller base on stochastic algorithms for attitude control of quadrotor. The model is based on usage of Fractional calculus in the PID controllers which can provide novel and higher performance extension for FOPID controllers. Here, the main goal is to design an optimal PID and FOPID controller using stochastic algorithms. Therefor two stochastic algorithms are used for optimal tuning of FOPID parameters. Genetic Algorithm (GA) and Particle Swarm Optimization are compared in minimizing the performance criteria formula which leads to a better performance in controlling of the plant. A quadrotor is installed on an experimental test stand includes of accelerometers, gyroscopes and microcontroller with one and three degrees of freedom to test the performance of PID controller. Kalman filter is used to reduce the noises by combination of accelerometer and gyroscope output. The result of the experiments in the paper shows that the quadrotor can perform desired motions successfully with the controller in the response of step input and while it is subjected to external disturbances.

In this paper, designing optimal PID controller using Modified particle swarm optimization is presented. The advantage of this new method compared to conventional methods of controller design is that it is not limited to a certain class of systems. In designing phase, sum of rising time, settling time, overshoot and integral of squared-error are minimized. There kind of particle swarm optimization algorithms such as ePSO, mPSO and sPSO are compared with other methods of optimization including Ant Colony. The results clearly show how superior the new proposed method is to the other methods. The proposed method differs from the other optimization algorithms in such a way that, the proposed algorithm does not need a velocity equation. The position of the particle is updated directly by extrapolating the current particle position with the global best particle position obtained so far.

The salt obtained from salt sources has a low purity level and contains contaminants. The primary contaminants in the brines were eliminated in this investigation by using analytical separation (titration) techniques. Following the purification method, sodium hydroxide (NaOH) was added to magnesium chloride (MgCl2) to make magnesium hydroxide (Mg(OH)2) coagulate in pH control. This was done by PID and Self-Tuning PID (STPID) control. Using STPID control, hydrochloric acid (HCl) at a rate of 20% was employed as an effective acid current, MgCl2 as a coagulant, and NaOH at a rate of 10% as a neutralization base throughout the process. The coagulation technique was carried out with pH values of 7, 9, and 11, respectively. The pH of the medium was adjusted using the PID and STPID algorithms, as well as an on-line computer control system. As the system model, ARMAX was employed. As a forcing function, a pseudo-random binary sequence (PRBS) was used to identify the dynamics of the process to be controlled, and the system output was measured. The Bierman algorithm was used to evaluate the model parameters. The STPID controller's tuning parameters were calculated. Following the coagulation method, an analytical titration procedure was used to find out if there are any trace amounts of Mg(OH)2 in the current environment, and a settlement percentage of 90% to 95% was found. To get the best coagulation, a pH value of 11 was chosen as the optimal value based on the performed calculations.

Interactions between input and output variables are a prevalent challenge in the design of multi-loop controllers for multivariable processes, and they can be a major stumbling block to obtaining good overall performance of a multi loop control system. The deconstructed dynamic interaction analysis is proposed to solve this limitation by decomposing the multi loop control system into a series of n independent SISO systems, each with its own PID controller. The multivariable decoupler and multi loop PID controller is applied to Two Tank Conical Interacting System (TTCIS). This TTCIS is chosen as benchmark problem used by many researchers. Firstly, the Mathematical modelling of TTCIS is derived using First principal model. The non-linear system is linearized using Jacobian matrix and decomposed into multiple SISO systems. The controller design for the process is then obtained, and an RGA matrix is constructed to minimise the interaction effects. To demonstrate the efficiency of the suggested strategy, simulation results using TTCIS are provided.

In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. The adaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinear characteristics of wind variations as plant input, wind turbine structure and generator operational behavior demand for high quality adaptive controller to ensure both robust stability and safe performance. Thus, a reinforcement learning algorithm is used for online tuning of PID coefficients in order to enhance closed loop system performance. In this study, at start the proposed controller is applied to two pure mathematical plants, and then the closed loop WECS behavior is discussed in the presence of a major disturbance.