Well-known PID Research Articles

Designing a multi-stage PD(1+PI) controller for DC–DC buck converter

Power electronic converters have a broad scope of applications in various fields of electrical energy utilization. Their fast dynamics and nonlinear structure make their control process has various complexities. Hence, the design of high-speed and powerful controller plays a vital role in the performance quality of these widely used devices. In this paper, a multi-stage Proportional–Derivative (PD) controller cascaded with One plus Proportional–Integrator (1+PI) controller is designed to control DC–DC buck converters. Studies show that transferring the integrator action to the second part of the cascaded controller improves the performance and speed up the converter’s response. The proposed controller parameters are optimally implemented using the Mayfly optimization algorithm (MOA). Also, various comparisons have been made with well-known PID and FOPID controllers to illustrate the suggested controller efficiency, and the closed-loop system behavior in varying conditions has been examined. The closed-loop system’s better time and frequency domain characteristics with the PD(1+PI) controller indicate the proposed controller supremacy than others one.

Robustness of Model-Predictive and Passivity-Based Control in the Three-Phase DC/AC Converter Application

In modern controller design, various solutions for controlling power converter systems can be found depending on their applications, speed of working, pulse width modulation (PWM) techniques, switching frequencies—fc, and different load types. The need to manipulate control parameters can be often observed in classical structures, e.g., well-known PID, repetitive, or deadbeat control particularly sensitive to distinct parameters uncertainties. The purpose of this paper is to present an improved version of controllers designated for a UPS that will be considerably resistant to model changes. Proposed control techniques are independent of unexpected output filter changes: Lf—filter coil inductance and Cf—filter capacitor conductance. The second aspect of this paper is to compare effectiveness of modified predictive MPC (model-predictive control) and feedback PBC (passivity-based control) controllers in reducing output voltage total harmonic distortion (THD) for various load values. The biggest distortions of output voltage were observed during experiments with nonlinear RC load. Both simulation and experimental verification of mismatching parameters were performed and examined. Thanks to the proposed solution, the output voltage THDv quality factor was reduced below 8% in an efficient way for all the applied loads and stayed at the level of 1% when well-matched filter parameters were provided.

Suspension Regulation of Medium-Low-Speed Maglev Trains Via Deep Reinforcement Learning

This article studies the suspension regulation problem of medium-low-speed maglev trains (mlsMTs), which is not well solved by most model-based controllers. We propose a sample-based controller by reformulating it as a continuous Markov decision process (MDP) with unknown transition probabilities. Then, we propose a reinforcement learning algorithm with actor-critic neural networks to solve the MDP. To reduce estimation errors in the Q-function, we adopt a double Q-learning scheme and propose a novel initialization method to accelerate the convergence by exploiting the PID controller. Finally, we illustrate that the proposed controllers outperform the existing PID controller with a real dataset from the mlsMT in Changsha, China, and are even comparable to model-based controllers, which, however, assume that the complete information of the model is known, via simulations. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—The control problem of levitation systems is essential for maglev trains. The advanced control methods require an exact dynamical model, which is difficult to establish in practice due to uncertainties and complex dynamics. Reinforcement learning (RL), as a model-free method, learns a controller directly from data. We are the first to propose deep RL algorithms to address the levitation control problem. By learning the real dataset provided by CRRC, the proposed algorithms outperform the well-known PID controller significantly. In particular, our controller responses 50 times faster than PID even without additional efforts on modeling. Moreover, the proposed initialization method can be applied in a variety of RL-based control problems to improve performance.

Modified Variable Structure Estimation and Control for Constrained Landing on Mars

Landing on Mars is one the most important space missions undergoing various system and environmental uncertainties. Subsequently, an exact model to represent the dynamic system cannot be provided in advance. In this regard, the present paper has focused on designing an integrated estimation and control algorithm bringing accurate navigation in the presence of uncertainties for the landing problem. The proposed algorithm has been shaped based on the variable structure control framework. This algorithm alleviates the requirement of Jacobian via substituting statistical linearization for the analytical one and utilizing generalized matrix inverse theory. Performance of the proposed algorithm has been investigated via Monte Carlo simulations in the presence of different uncertainties including stochastic initial conditions of the coupled Mars lander dynamics, atmosphere instability and modeling errors of the exerted forces and moments, time delay of actuators, geometric constraint of the landing site as well as the saturation limitations of actuators. In addition, the obtained results have been compared to the results obtained from combination of well-known extended Kalman filter and PID controller. This study clearly shows the precise and robust performance of the proposed integrated estimation and control algorithm and proves its superiority against existing widespread algorithms.

Design of Temperature Controller for Irradiation Experiment in Nuclear Reactor

Indian nuclear research reactors provide facilities to carry out irradiation experiments on the core structural materials to assess the radiation damage in these materials. The radiation damage in the material strongly depends on the temperature of irradiation. The temperature-controlled instrumented irradiation capsule (TCIIC) is a device which facilitates irradiation of specimens at a constant higher temperature in a reactor. This paper discusses design of PID controller to establish a constant temperature in the TCIIC system. The control parameters are estimated through placement of dominant pole-zero in the “S-plane” and a stable region of the PID controller parameters was defined. The time response result from the proposed method has been compared with other well-known PID controller design methods and also verified experimentally. The proposed method shows that the controller parameters can provide a stable and reliable performance of temperature control needed in the irradiation experiment.

Hybrid Dec-POMDP/PID Guidance System for Formation Flight of Multiple UAVs

This paper proposes a guidance system with a new hybrid architecture for multiple fixed-wing unmanned aerial vehicles (UAVs) formation flight. The proposed architecture is hybrid in the sense of combining two control policies found in guidance systems: the Decentralized Partially Observable Markov Decision Process (Dec-POMDP) policy and the well-known PID controller. The Dec-POMDP policy part enables every UAV to avoid collision with any other UAV from the formation. The PID part is activated for regions far from the collision risk zone and is implemented in such a way that each UAV is completely controlled by only its own data, independent of its neighbors. The major novelty of the proposed system, compared to other hybrid approaches, is its decentralized nature which favors overcoming the usual problems that come with centralization such as the high dependence on a central decision node, a leader, or a ground station. Inherited from both parts, the overall system performance is robust in noisy and uncertain environments. Simulations were extensively performed to show the effectiveness of the proposed guidance system in distinct scenarios.

Memory-Efficient, Accurate and Early Diagnosis of Diabetes Through a Machine Learning Pipeline Employing Crow Search-Based Feature Engineering and a Stacking Ensemble

The early diagnosis of diabetes helps in avoiding the major risks associated with the disorder. The proposed research involves the design of a machine learning pipeline which generates the most representative feature subset of minimal size that predicts the onset of Diabetes with highest accuracy. It employs a novel diabetes dataset which is gender-neutral and representative enough unlike the well-known PID dataset. The machine learning pipelines involve multiple feature engineering pipelines to generate a reduced feature subset which is fed into multiple heterogeneous classifiers. The feature engineering involves feature selection as well as feature extraction. The former uses the ANOVA filter and Crow Search Optimization algorithm. The latter employs the Singular Value Decomposition. The classification is performed on the preprocessed dataset using a wide range of heterogeneous classifiers like Naive Bayes’, Logistic Regression, K-Nearest Neighbor, Decision Trees, Support Vector Machine, Random Forest, AdaBoost, and GradientBoost as base learners followed by their stacking ensemble. The performance evaluation of each machine learning pipeline is done through Repeated Stratified K-fold Cross Validation using the metrics of accuracy, precision, recall, F1 Score and area under Receiver Operating Characteristic curve. For each pipeline, the number of features in the preprocessed dataset varies and the highest accuracy of 98.4% is achieved with Crow Search algorithm through a stacking ensemble of multiple heterogeneous classifiers. A comparative analysis with a recent related work on the same dataset shows that the proposed feature engineering pipelines with the same set of classifiers outperform with improved accuracy using a feature set of reduced size.

Frequency stability of interconnected power systems using atom search optimization algorithm

This paper explores the dynamic controller design issue for load frequency control (LFC) of a practical interconnected power system model. In view of this, the three-degree-of-freedom proportional-integral-derivative (3DOF-PID) controller architecture and LFC performance analysis for the proposed atom search optimization (ASO) is presented. The consistency of the form and acceptability of the well-known PID controller’s responses ultimately enforces to use in this work. The proposed ASO algorithm efficiently combines search space discovery and exploitation that yield promising solutions at the termination condition. The test system investigated is a four-area model with each region consisting of identical thermal unit. The system is also connected in one area with an interline power flow controller. The simulation results presented showed the superiority of ASO based 3DOF-PID controller in terms of settling time, peak variance, and magnitude of oscillation.

Optimization of the Energy Consumption of Electric Motors through Metaheuristics and PID Controllers

The synthesis of electric motor control systems, seeking optimal performance, is a well-known and studied field of automation to date. However, the solutions often use very elaborate mathematical foundations and sometimes require considerable algorithmic complexity. Another approach to the same problem, which offers very interesting results, is the use of artificial intelligence methods to generate controllers. Intelligent methods allow the use of bio-inspired approaches to solve complex problems. This article presents a method to adjust the parameters of a controller for DC motors based on two components in the objective function: High productivity and efficiency. This can be achieved using well-known and low algorithmic complexity PID controllers, and metaheuristic artificial intelligence techniques to adjust a controller to obtain optimal behavior. To validate the benefits of the methodological proposal, a simulator of a DC motor has been rigorously constructed, respecting fundamental physical principles. The adjustment system based on metaheuristics (genetics algorithms) has been designed to work on the simulator and constitutes the central contribution of the paper. This system has been designed to establish the parameters of a PID controller, optimizing its behavior in relation to two variables of interest, such as performance and energy efficiency (a non-trivial problem). The results obtained confirm the benefits of the approach.

Inverted Pendulum Nonlinear Controllers Using Two Reaction Wheels: Design and Implementation

Reaction wheels have been extensively used to control and stabilize a wide range of applications due to the angular momentum exchange that they provide to mechanical systems. However, there exist several limitations associated with actuator saturation and the presence of singularities that lead towards the use of different controllers and devices. Recently, a new and unusual configuration using two reaction wheels was proposed and illustrated in an inverted pendulum to drive and control it using a simple and well-known PID controller. In this context, this paper improves the previous results on the same system by proposing two more sophisticated nonlinear controllers: a nonlinear proportional-derivative and a sliding mode controller. The proof of the stability of each controller is also provided, and the asymptotic stability is proven. A friction model is experimentally updated into the differential equations of the pendulum and also included for the controllers designing. A real-time application verifies the performance of the controllers using low-cost hardware. Results based on the analysis of different performance indices highlight the improvement of applying these two nonlinear control techniques comparing to the previous paper using this configuration of actuators.

SIMPLE DATA APPROXIMATION FOR COMPUTER AND CONTROLLER-AIDED DEVICES

INTRODUCTION&#x0D; Development of mechatronic systems involves finding an optimal balance between the basic mechanical structure, sensor and actuator implementation, automatic information processing and overall control. Mechatronic systems are characterized by a combination of basic mechanical devices with a processing unit monitoring and controlling it via number of actuators and sensors. Therefore sensors are significant in the process of providing usable output to microcontrollers. Wide range of sensors are available for constructing mechatronic systems. Sensors can be divided into two big groups: Active and Passive. Other type of classification is by the means of detection used in the sensor. Some of the means are electric, chemical, radioactive etc. Various types of sensors are classified by their measuring objectives for example light sensors, temperature sensors, flow sensors etc.&#x0D; MATERIALS AND METHODS&#x0D; In the process of constructing a mechatronic system a proper setup and signal processing must be provided. There exist certain problems with several sensors, therefore sometimes additional circuits for signal conditioning are made to linearize the output with hardware, but some researchers and developers try to linearize the signal using software.&#x0D; In modern manufacturing equipment very complex systems of devices and sensors are made therefore, they must function correctly because they are the main control parameters. It is particularly important that such control parameters that bring about a correct actual behavior in relation to the reference behavior of such a system are available as a function of time. This means that the parameters must be such that the actual behavior of the system corresponds as closely as possible to the reference behavior. Some examples of such systems are:&#x0D; &#x0D; Robot arms, which move a tool, such as a laser or burr removing tool, for example, which is to be guided along a particulary contour line of a workpiece.&#x0D; Heating systems which are intended to impart a particulary temperature profile to a workpiece.&#x0D; &#x0D; The input data of sensors is crucial for mechatronic systems.&#x0D; A large part of the system is equipped with sensors that read the most important parameters – location coordinates, altitude, compass readings, distance to the barrier (for robots and unmanned aerial vehicles), temperature (heaters and coolers), lighting, etc.&#x0D; Often, some types of sensors give floating data, processing which, a computer or controller acting under an algorithm develops non-physical, inexecutable commands for the final control elements. This results in an increasing load of engines, heating elements, and other actuators, as well as inappropriately increasing energy consumption.&#x0D; The well-known PID algorithm and numerical approximation with built-in MatLab or MATCAD functions does not provide a solution for autonomous systems with controllers that have limited memory and speed of operation.&#x0D; RESULTS&#x0D; New methods that approximate sensor data and are applicable to both analogue and PWM (Pulse-Width-Modulation)-controlled devices have been developed in the paper.&#x0D; The first proposed – derivative - method relates to the restriction of the function direction coefficient module. The second method – the growth bisection method enables smooth sensor data to be obtained.&#x0D; The derivation method is based on limitation of the maximum function increment to a specified level. The growth bisection (proportional) method is based on comparison of the increment module with the increment in the previous step and its proportional decrease by multiplying by a predefined constant. Both methods take up some lines in the control program code, and most mechatronic equipment is capable of real-time operation.&#x0D; CONCLUSIO&#x0D; Dynamic data background connection allows to obtain a self-learning system adapting to the nature of incoming data – a higher number of data will be used in case of minor changes; in contrast, only the last data saved will be used for a rapid change. A system response delay is negligible.

Tuning of linear active disturbance rejection controller with robustness specification

Active disturbance rejection control (ADRC) treats all the model uncertainties and all the external disturbances as a generalized disturbance. It uses an extended state observer (ESO) to estimate the generalized disturbance in real time, and compensate it using a state-feedback control law, thus can achieve good disturbance rejection performance. For linear ADRC (LADRC), the parameters can be tuned via the bandwidths of the ESO and the feedback control, thus an LADRC can be regarded as a fixed-structured controller with several parameters to tune, just like a PID controller. To help tuning the parameters of LADRC, a tuning rule is proposed in this paper, with the aim to minimize the load disturbance attenuation performance in the integral of time square error sense, under the constraint of a specified robustness measure for the first-order processes with deadtime. The tuning rule is tested for a variety of benchmark systems and the gravity drained tanks case, and the performances are compared with the well-known PID tuning methods.

Digital PI-PD controller design for arbitrary order systems: Dominant pole placement approach

In this paper, a digital PI-PD controller design method is proposed for arbitrary order systems with or without time-delay to achieve desired transient response in the closed-loop via dominant pole placement approach. The digital PI-PD controller design problem is solved by converting the original problem to the digital PID controller design problem. Firstly, parametrization of the digital PID controllers which assign dominant poles to desired location is done. After that the subset of digital PID controller parameters in which the remaining poles are located away from the dominant pole pair is found via Chebyshev polynomials. The obtained PID controller parameters are then transformed into the PI-PD controller parameters by considering the closed-loop controller zero and the design is completed. Success of the proposed design method is firstly demonstrated on an example transfer function and compared with the well-known PID controller methods from the literature through simulations. After that the design method is implemented on the fan and plate laboratory system in a real environment.

COMPUTER BASED NON TRADITIONAL CONTROL SYSTEM COURSE TEACHING IN INDONESIA ELECTRONICS ENGINEERING POLYTECHNICS EDUCATION

Visual simulation is expected to provide simulation results with a view that allows the students to see and feel like doing the practice of creating a control system. Software control system simulation applications are expected not only easy to use but also interactive to the needs of the computer specifications used are not too sophisticated. In this study the computer based control system course teaching was designed using the combination of an electronics control plant and control system simulation which is called nontraditional control system course teaching. The modelling simulation software application is a solution to see the performance of the controlled system which was developed to implement a control system which is light and easy-to-operate. Spreadsheets can be used to simulate the electronics controlled plant in the development of linear control system which is easy to operate with minimum computer specification that can be used by students. The result of proposed simulation confirms the well-known PID controller effects, and verified by the response of the real DC motor speed electronics control plant developed and can be used as a visual simulation for students of electronics engineering polytechnics students. Modeling simulations to visualize the performance of electronics control circuits are used to overcome the limitations of resources in the laboratory on the administration of polytechnic education in Indonesia. In its implementation in the laboratory, lecturers are encouraged to improve the spreadsheet programming capabilities. The development of this study is that non-traditional electronic control teaching can be implemented through internet of things and cloud computing.

DOB-based piezoelectric vibration control for stiffened plate considering accelerometer measurement noise

This paper presents a composite control strategy for the active suppression of vibration due to the unknown disturbances, such as external excitation, harmonic effects and control spillover, as well as high-frequency accelerometer measurement noise in the all-clamped stiffened plate. The proposed composite control action based on the modal approach, consists of two contributions including feedback part and feedforward part. The feedback part is the well-known PID controller, which is widely used to increase the structure damping and improve its dynamic performance close to the resonance frequencies. In order to get better performance for vibration suppression, the weight matrixes is optimized by chaos sequence. Then an improved disturbance observer (IDOB) as the feedforward compensation part is developed to enhance the vibration suppression performance of PID under various disturbances and uncertainties. The proposed IDOB can simultaneously estimate the various disturbances dynamically as well as measurement noise acting on the system and suppress them by feedforward compensation design. A rigorous analysis is also given to show why the IDOB can effectively suppress the unknown disturbances and measurement noise. In order to verify the proposed composite control algorithm (IDOB-PID), the dSPACE real-time simulation platform is used and an experimental platform for the all-clamped stiffened plate active vibration control system is set up. The experimental results demonstrate the effectiveness, practicality and strong anti-disturbances ability of the proposed control strategy.

Understanding PID design through interactive tools

Hundreds of PID design methods are available in literature. Many of them are very similar and sometimes it is not straightforward to understand their purposes. This paper presents an interactive software tool to help in the study and understanding of several well-known PID tuning rules. Frequency- and time-domain responses are analyzed in order to show the robustness and performance properties of each method. Furthermore, a free tuning option is available to provide comparisons with user-defined rules or other existing tuning methods. A wide range of stable process models are included in the tool, where model-reduction techniques are applied for high-order processes to obtain simple models for design purposes.

Differential Neuro-Fuzzy Controller for Uncertain Nonlinear Systems

In general, output-based controller design remains an important research area in control theory. Most of the existing solutions use a state estimation algorithm to reconstruct a plausible approximation of the real state. Then, one can apply a nonlinear controller, based on fuzzy logic, for example, to enforce the system trajectories to a desirable stable equilibrium point. Nevertheless, the aforementioned method may not be suitable for uncertain systems affected by external noises. State observers based on the system's structure cannot be applied in those cases. However, some sort of adaptive estimation may be developed. This paper deals with a fuzzy controller that was designed using the state observer solution when the dynamic model of a plant contains uncertainties or it is partially unknown. Differential neural network (DNN) approach is applied in this uninformative situation. A new learning law, containing an adaptive adjustment rate, is suggested to enforce the stability condition for the observer's free parameters. On the other hand, nominal weights are adjusted during the preliminary training process using the least mean square method. Lyapunov theory is used to obtain the upper bounds for the weight's dynamics. The proposed method seems to be a more advanced option to control uncertain systems when the state available information is reduced. Even when several options exist to control this class of nonlinear systems such as PID, the method introduced here uses the knowledge on the system behavior and enforces the reconstruction of the immeasurable states. This last issue is an extra advantage because it serves as a general software sensor. The well-known two-link manipulator is used to show the effectiveness of the proposed algorithm. A couple of cases are used here: the full actuated and the under-actuated systems. In both situations, the controller achieves a better performance than the well-known PID controllers and a fuzzy controller using the estimated states produced by a high-order sliding-mode observer. A practical example showing how the fuzzy controller based on the estimated states produced by the differential neural network observer is also presented. The system used to test the controller is the anaerobic digestion. In this case, the benefits of this output-based controller are also demonstrated.

Evaluation and Development of Advance Control Strategies Which Uses Pattern Recognition Technique for Nonlinear System

A general controller evaluation method is introduced, based on robustness criteria of well-known PID Controller formulate for process of non-linear systems using experimental of coefficients for flank wear of dynamic models obtained are calibrated with the actual conditions. These developed models will be used for the simulation of flank wear and using control variable such as cutting speed; the flank wear will be controlled. For model validation, the flank wear is estimated using a non-linear model. In the present work, an attempt has been made to control the flank wear during turning of on-line cutting process using the Fuzzy Logic Controller and Neural network based on self-tuning of PID controller approaches. Those approaches are treat the material as dynamic system and involve developing state space models from available material behavior model. The evaluation of performance criteria can be compared for those approaches of PI controller with Neural network and Fuzzy PID controller based on self-tuning of PID controller. Simulation studies are carried-out for the non-linear system using MATLAB software.

Comparison of some well-known PID tuning formulas

Criteria based on disturbance rejection and system robustness are proposed to assess the performance of PID controllers. A simple robustness measure is defined and the integral gains of the PID controllers are shown to be a good measure for disturbance rejection. An analysis of some well-known PID tuning formulas reveals that the robustness measure should lie between 3 and 5 to have a good compromise between performance and robustness.

A refinement procedure for PID controller tuning

The use of optimization is an alternative to the well-known automatic PID tuning rules. However, for a proper approach to the optimization problem, the stability of the resulting closed-loop must be guaranteed. A framework for the PID parameter optimization, with stability guarantees, is proposed in this paper. The framework is intended to be used to refine an initial choice for the PID parameters. The way to the solution is based on to the characterization of the set of PID achievable designs and the establishment of a condition that will ensure us to move on to the appropriate subset of achievable stable PID designs. The optimization procedure is applied to tune a PI controller on an unstable system and also on a non-minimum phase system.