Adaptive Design of PID Controllers Based on an Alternative Method to Root Locus ⋆

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.

Motivation: In designing linear feedback systems, tracking quality specifications are typically mapped into admissible domains in the complex plane and then the root locus technique is used to synthesize a controller that places closed loop poles in the desired locations. This methodology motivates the first goal of this chapter: to generalize the notion of admissible domains and the root locus technique to LPNI systems. Accomplishing this goal leads to a quasilinear method for PID controller design.

The design and implementation of a multi-stage PID (MS-PID) controller for non-inertial referenced UAVs are highly complex. Symmetrical multirotor UAVs are unstable systems, and it is thought that the kinematics of the symmetrical UAV rotor, such as the quadrotor and hexacopter resembles the kinematics of an inverted pendulum. Several researchers have investigated the structure and design of PID controllers for high-order systems during the last decade. The designs were always concerned with the enhanced response, robustness, model reduction and performance of PID controllers. An accurate tuning process of such a controller depends on the engineer’s experience level. This is due to the number of variables and hyperparameters tuned during the process. An adaptive genetic algorithm (AGA) is utilized to optimize the MS-PID controllers for controlling the quadrotor in this study. The proposed method optimizes the offline-planned approach, providing several possibilities for adapting the controllers with various paths and or varying weather conditions. The MS-PID parameters are optimized in parallel, as every PID controller affects the other controller’s behavior and performance. Furthermore, the proposed AGA generates new chromosomes for “new solutions” by randomly developing new solutions close to the previous best values, which will prevent any local minima solution. This study intends to investigate the design and development of a highly tuned robust multi-stage PID controller for a symmetrical multirotor UAV. The work presents a model for a non-referenced inertial frame multirotor UAV (quadcopter). Once the model is defined, a robust multi-stage PID controller for the non-inertial referenced frame symmetrical multirotor UAV is designed, tuned, and tested. A genetic algorithm (GA) will be used to tune the MS-PID controller. Finally, the performance comparison between the proposed and conventional methods is presented. The results show that the proposed method provides stability improvement, better transient response, and power consumption.

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

Both linear PID controllers and fuzzy controllers are designed and implemented for a boost converter. Comparison between the two controllers is made in the aspect of design, implementation and experimental results. Design of fuzzy controllers is based on heuristic knowledge of the converter and tuned using trial and error, while the design of linear PID and PI controllers is based on the frequency response of the boost converter. Implementation of linear controllers is quite straightforward, while implementation of fuzzy controllers has its unique issues. A comparison of experimental results indicates that the performance of the fuzzy controller is superior to that of the linear PID and PI controllers. The fuzzy controller is able to achieve faster transient response, has more stable steady-state response, and is more robust under different operating points.

ABSTRACTIn this article, we propose practical rules for tuning event-based PID controllers with two sampling strategies: symmetric send-on-delta (SSOD) and regular quantification (RQ). We present a detailed analysis about the effect of the derivative term of the controller when using SSOD or RQ and some guide lines are given to select the derivative filter coefficient. The two sampling strategies are compared, showing that, even when both of them lead to similar controlled output response, systems with RQ have better robustness properties than those with SSOD. The study is based on the describing function and the results are applicable to process with dynamic responses of different types: with time delays, non-minimum phase, under-damped response, etc. The rules presented here are given in terms of phase and gain margins that are measures of robustness used in the design of continuous PID controllers. This allows the application of conventional PID tuning methods to the case of event-based PID. The tuning rules are very simple and can be used for tuning PID, PI, PD and other controller structures.

The main advantage of one-cycle control method is its ability to reject input disturbance in one-cycle. But despite of this great ability, it can not provide good responses in following commands and rejection of load disturbance. In this work, it is shown that it is possible to overcome these problems by the use of another controller. Although the idea of using output feedback has been used in previous works, the design of controller has become simpler in this work, by consideration of a simple model for one-cycle controller. In proposed method, difficult mathematical modeling is avoided. Based on decoupling of effects of feedback and input voltage disturbance, a simple model for one-cycle controller has been given. Therefore, by employing a conventional averaging method and the model of one-cycle controller, design of PID controller has become straightforward.

The mathematic model considering magnetic flux loss of Maglev train and the design of PID controller are studied. During the process of analysis and design of PID controller, some new performance indexes, which were firstly put forward by Wang Guangxiong and are used to judge sufficient stable degree for controlling unstable system, are studied. Bode plot is used to validate the modified phase margin. Compared to Wang's conclusion, the more widely accepted phase margin is brought forward. Furthermore, the PID controller and performance indexes are validated by nonlinear simulation model.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

On the design of multivariable PID controllers via LMI approach

PID controller design with a new method based on proportional gain for cruise control system

Robust Design of PID and IMC-Based Controllers in the Time Domain

This paper compares the performance of four different controllers implemented on two multi agent robots to stabilize its motion from one station to another during delivery tasks. The controllers are; multi-position controller, PID controller, fuzzy logic controller and fuzzy-PID controller. The aim of this paper is to control the mobile robot robustly to arrive its target destination. The robots and station coordinates are recognized using machine vision system and all programming is carried out in LabVIEW. The paper compares the transient response and steady state error of each of controller and experimental results show that the Fuzzy-PID controller produced the best performance and good trajectory of robot from its current position to its target position. It had a better convergence rate when compared with other controllers like PID and Fuzzy logic controllers.

This paper concerns design and implementation of PID and fractional-order PID (FOPID) controllers to control position of an electromagnetically suspended ferromagnetic ball in a magnetic levitation (Maglev) system in real time. The Maglev system, manufactured by Feedback Instruments (Model No 33-210) is used as a platform to test the performance of proposed controllers. Parameters of PID and FOPID controllers are tuned by firefly algorithm (FA). FA is a metaheuristic algorithm based on movement of fireflies toward more attractive and brighter ones. PID and FOPID controllers are implemented in MATLAB and SIMULINK environment inside PC using fractional-order modeling and control toolbox. Controller in the SIMULINK environment inside PC is connected to the Maglev system through Advantech card. Effectiveness of proposed controllers is tested by checking the ability of the suspended ball to track a reference signal. Step change, sine wave, and square wave are used as reference signals. Real-time results have revealed satisfactory transient and steady-state responses over the contemporary existing controllers. FOPID controller showed better results compared to PID.

Robust tuning of PI controllers based on taguchi method