Strongly stable GPC-based PID controller

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

In the recent years, Buck converters have been widely involved in a variety of the everyday applications such as smartphones and PCs. Buck converters can provide better and steadier performance when integrating a control system in the design. Therefore, it is interesting to work on this integration and gain the required efficiency in term of the gained voltage. In this paper, PID controller is adopted to control the output voltage of the Buck converter. An optimization is achieved on the performance of the Buck converter using two bio-inspired algorithms namely, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The voltage controlled Buck converter system is simulated using Matlab environment to validate the proposed PID controller system. In this study, the voltage regulation process of Buck converter is investigated based on many working disturbances such as the change in the supply voltage, reference voltage, and load resistance in order to verify the robustness of the proposed PID controller. Finally, the feedabck voltage control system of the Buck converter is implemented experimentaly in real-time to validatde the simulated PID controller.

The parameters of fixed structure PID controller are difficult to determine, especially for non-minimum phase system. Recently, there has been significant progress in combining adaptive technology with PID controller. Most of them use pole assignment method, but the choosing closed-loop system poles and solving equation online restrict their development. The self-tuning PID controller can overcome this problems, but the stability analysis of closed loop system is difficult and global convergence of the algorithm cannot be established. This paper presents a new self-tuning feedforward controller and solves the above problems. The controller proposed is based on the generalised minimum variance control strategy. >

Design of a Data-Driven PID Controller using Operating Data

A new design method for PID controller using user-specified gain margin and phase margin for delayed systems has been proposed. The proposed PID controller is particularly useful in auto-tuning. The controller parameters are calculated using several approximations and elimination processes. The robustness of the proposed controller is verified by different simulation examples.

Based on parameter self-tuning fuzzy PID controller, a fuzzy inference method is utilized to realize automatic regulating PID parameter, and the application of the controller in a system is studied with MATLAB in this paper. The combination of PID controller system and fuzzy controller system combines the convenience of PID control together with the flexibility of fuzzy control, and takes advantage of the traditional control, which has a great practical significance. The results of simulation show that fuzzy PID parameter self-tuning controller has a better control effect than the traditional one, and can improve the static and dynamic properties of the system well. DOI: http://dx.doi.org/10.11591/telkomnika.v14i2.7674

The purpose of this paper is to improve dynamic characteristics of dc-dc converters in the information and telecommunications system by using a proportional and integral gain changeable control method. Generally, stability becomes worse and a limit cycle oscillation occurs when large PID control gains are used. In the proposed method, the proportional gain is risen during a transient state to improve the transient response and avoid the limit cycle oscillation. Also, the integral gain is set to a small value in the continuous conduction mode and a large value in the discontinuous conduction mode. As a result, the proposed method has a superior transient response and high stability to a conventional fixed gain PID control method.

In order to solve the difficult problem that how to reduce the overshot and shorten the regulating time of the PID controller based on BP neural network, a self-tuning PID controller based on improved BP neural network is presented. The parameters of the PID controller are calculated by an improved BP Neural Network according to the input and output and the error of the PID controller. It is introduced the dynamic adjustment for activation function in the output layer, and the dynamic adjustment for learning rate to improve the Fletcher-Reeves conjugate gradient method. In the simulation experiments in the Matlab 7.0, two plants are selected to test the performance of the proposed PID controller, and also the rand noise are added to the input to test the robustness of them. From the simulation results, the overshoot are lower than those of controllers by using the steepest descent method and the Fletcher-Reeves conjugate gradient method; the regulating time is also shorter than those of controllers by using the steepest descent method and the Fletcher-Reeves conjugate gradient method; the proposed control algorithm is more robust than those of controllers by using the steepest descent method and the Fletcher-Reeves conjugate gradient method.

In this article, a robot manipulator is controlled by the PID controller in a closed loop system with unit feedback. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). In this case the Ant colony Optimization algorithm (ACO) is used to find the best gain parameters of the PID. The Ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located, this concept is applied to tuning PID parameters by minimizing the objective function such that the robot manipulator has improved performance characteristics. This work uses the Matlab Simulink environment, First, after obtaining the system model, the ant colony algorithm is used to determine the proper coefficients 𝐾p, 𝐾i, and Kd in order to minimize the trajectory errors of the two joints of the robot manipulator. Then, the parameters will implement in the robot system. According to the results of the computer simulations, the proposed method (ACO-PID) gives a system that has a good performance compared with the classical PID.

In this work, a hybrid PID controller, combining Zeigler–Nichols (ZN) theory and the dominant pole placement, has been developed for the automatic voltage regulator (AVR) systems. Furthermore, the reduced order models (ROMs) of AVR systems, namely, AVR-1, AVR-2, and AVR-3, have been established using Particle Swarm Optimization (PSO), Big Bang Big Crunch (BBBC), and Pade’s approximation, respectively. Then, the proposed PID controller has been applied to both the original and developed lower order models of AVR to check the efficacy and robustness of the developed hybrid controller. It is shown that the proposed hybrid PID controller provides a very small value of the percentage peak overshoot (%M p ) and settling time (t s ). Also, the gain margin (GM) and phase margin (PM) are found to be in the desired range. Finally, the sensitivity analysis has also been performed to check the robustness of the proposed controller. The sensitivity function, complementary sensitivity function, and various performance indices have been evaluated for the proposed controller based on Bode’s Integral (BI), Tyreus–Lyuben (T–L), and Internal Model Control (IMC) approaches. The comparative studies of the results show that the proposed controller has smaller values of performance indices and sensitivity function.

This paper introduces a controller model for the integration, of a wind system and photovoltaic system into the utility grid. The problem of integration of hybrid renewable energy based hybrid renewable energy generation system is that it relies heavily on weather conditions, PV and wind air speed. So, there is a necessity for developing control techniques for grid integration for such hybrid system, including a method for voltage and current control that stabilises the voltage and current. A PID control is developed in this work that is suitable for hybrid renewable energy system. Then, the system behaviour and performance are studied. For different weather conditions and fault condition systems, stability is also considered when there are change weather conditions in PV and air speed or a fault in the system. This paper advocates that the proposed PID controller gives a good performance. In this paper, solar energy is hybrid with wind energy. The developed MATLAB/SIMULINK design improves for integration of hybrid system performance and the results are presented.

A possible approach to the calculation of standard controller parameters based on the localization method is considered. It is shown that the controllers designed in this way completely solve the problem of stabilization of first- and second-order nonlinear nonstationary plants. Variants of implementation of standard controllers and recommendations for calculating their parameters are presented and the properties of the obtained automatic systems are investigated. It is noted that a general characteristic feature of systems with the proposed PID controller is the occurrence of fast motion components against the background of slow work processes. Results of numerical modeling illustrate the main properties of the systems considered.

For the stability problem of the active magnetic levitation ball, the application of the PID controller on the system is determined. Firstly, the linearized mathematical model of the active magnetic levitation ball system is established. Then the PID parameters are adjusted by the trial method. The influence of three PID parameters on the system is introduced, and a PID controller is designed for a set of actual systems. The performance is simulated and experimentally studied. The research results show that the active electromagnetic suspension ball system based on PID controller has high anti-interference performance and stability.

Generally Quad-rotor type Unmanned Aerial Vehicles are unstable in nature, so to stabilize it, attitude controller is used. This paper presents a Fuzzy Logic based PID control system (FPID) for attitude stabilization of Quad-rotor Unmanned Aerial Vehicle (UAV). The Fuzzy logic controller updates the PID controller gains efficiently in such a way that it efficiently stabilizes the attitude of quad-rotor UAV. This method enhances the capabilities of conventional PID Controller to a dynamic PID Controller. The propose FPID control technique for attitude controlling of Quad-rotor UAV was tested on developed prototype of Quad rotor UAV for experimental purpose. Experimental results proved that proposed Fuzzy Logic based PID (FPID) controller quickly stabilized Quad-rotor UAV and its response time and settling time is reasonably good in attitude stabilization control application.

This paper proposes a new offline learning algorithm for a data-driven proportional-integral-derivative (DD-PID) controller based on an extended fictitious reference iterative tuning (E-FRIT). PID controllers have been used in many process systems. However, it is difficult to maintain a good control performance using PID controllers with fixed control parameters because of the nonlinearity of the systems. The DD-PID controller has been proposed as an effective control system for nonlinear systems. This controller can tune its PID parameters adaptively at each equilibrium point of the system output. In the conventional DD-PID controller, the PID parameters are learned so as to minimize a criterion of the FRIT method. However, the FRIT method is based on minimization of the error in system output, and therefore, the criterion is insufficient for systems, such as chemical process systems, for which the stability of a closed loop system is essential. In order to solve this problem, the E-FRIT method has been proposed; a penalty for input variation is incorporated in the criterion for this method. In the present study, an offline learning rule of PID parameters was derived based on the E-FRIT criterion. According to the rule, the PID parameters that are taken into stability can be calculated. The effectiveness of the proposed method was evaluated by simulations of a polystyrene reactor system. The simulation confirmed that the proposed DD-PID controller yields better control result than the conventional learning method.