Design Of Control Algorithms Research Articles

Research on the Application of PID Control Algorithm and Fuzzy Control Theory in the Field of Temperature Control

In the field of industrial automation control systems, PID control algorithms are widely used in various control fields such as temperature, speed and position. Among them, fuzzy PID control can improve control accuracy and stability, can significantly improve product quality and production efficiency, in food processing, chemical engineering, pharmaceutical and textile industries have a wide range of applications. Therefore, this paper comprehensively discusses the theory of fuzzy PID control algorithm and its application in the field of temperature control. The advantages of fuzzy PID control in complex industrial environments are emphasized through a comparative analysis with other temperature control methods as well as traditional linear PID control. The research indicates that by optimizing the control algorithm and hardware design, the fuzzy PID control algorithm can reduce the system overshoot and steady state error, thus improving the control accuracy and response speed. At the same time, the introduction of multivariable PID controllers or the use of advanced multivariable control strategies can achieve coordinated control of multiple variables, thereby improving the performance of the entire system. In addition, the integration of advanced energy management system and data analysis technology can comprehensively monitor and optimize the management of energy consumption, further improving the energy efficiency of the system

Experimental analysis of genetic algorithm-enhanced PI controller for power optimization in multi-rotor variable-speed wind turbine systems

The direct power control (DPC) algorithm is one of the most popular linear techniques used to implement notable controllers, known for their simplicity and fast dynamic response. However, this approach has drawbacks that cause a decrease in the current quality and disturbances in the network. Therefore, this experimental work presents a simple and efficient solution that uses a proportional-integral regulator based on a genetic algorithm to regulate the power quality. The designed approach uses a pulse width modulation to produce control pulses for the operation of the rotor inverter of a doubly-fed induction generator-based multi-rotor wind system. This approach is first verified in MATLAB using a 1500 kW generator operating under different working conditions. Furthermore, the processor-in-the-loop (PIL) test using dSPACE 1104 is used to verify the efficacy and ability of the designed approach in enhancing the effectiveness of the power system under study. The results obtained in all tests demonstrate that compared to DPC, the designed approach reduces active power ripples with estimated percentages of 71.42%, 66.67%, and 70%, and the reactive power overshoot value is reduced with estimated percentages of 92.85%, 56.48%, and 79.21%. In addition, the experimental results (using the PIL test) confirm the ability of the designed control algorithm to enhance the energy and current quality, which makes this designed technique a suitable solution in the field of control.

Discrete-time low-computation spacecraft attitude tracking control algorithm design considering actuation stiff constraints

Discrete-time low-computation spacecraft attitude tracking control algorithm design considering actuation stiff constraints

Neuro-Fuzzy: Multivariable Identification of a Pumping System with Variable Demand

Water supply systems (WSS) comprise a set of equipment, works and services aimed at supplying water, covering domestic, industrial and public consumption. WSS face challenges arising from hourly variation in demand, influenced by society's consumption patterns. These variations cause fluctuations in system pressures and energy inefficiencies. As a way of analyzing this aspect, intelligent techniques were used to identify an automated WSS with variable demand for the development of computational models. Two multivariable and non-linear models were developed based on Artificial Neural Networks (ANN) and Neuro-Fuzzy system (NF). The objective is to enable simulations of operation scenarios, analysis, design and implementation of new intelligent control algorithms. To collect the data used in training and validation, experiments were carried out throughout the system's operating region. For cross validation, tests were carried out in operating regions different from those used for training. The models were evaluated using performance criteria, such as: Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Final Prediction Error (FPE) and Adjustment percentage. The results were obtained using an experimental bench and showed adjustments greater than 99%. The cross-validation results evaluated with performance indicators show the superiority of intelligent models in comparison to parametric and mathematical models in the multivariable and non-linear identification of water pumping systems for simulation and dynamics prediction purposes.

Comprehensive Control Analysis of Dual DC-DC Output Converter for Integration of Offshore Wind Turbine Systems

This paper presents an in-depth exploration of control state-space modeling tailored for high-power isolated dual output DC-DC (ISO-D2) converter, with a particular emphasis on elucidating system dynamics through the derivation of state-space matrices and transfer functions. Employing state-of-the-art analysis techniques, this study offers a systematic framework for comprehending the converter's behavior across varied operational scenarios. Through the derivation of state-space matrices encompassing state, input, and output parameters, the converter's dynamic response is encapsulated in a precise mathematical representation. Additionally, transfer functions are established to facilitate frequency domain analysis and stability evaluation. These derived models furnish invaluable insights into the performance characteristics of the converter, thus enabling the formulation of robust control strategies. Particularly, the derived state-space matrices and transfer functions serve as instrumental tools for the design of advanced control algorithms, crucial for optimizing the performance of high-power isolated dual output DC-DC (ISO-D2) converters, in real-world applications such as solar plants and offshore DC wind farms.

A general dynamic output feedback control for rectangular nonlinear discrete-time descriptor Markov jump systems

This paper studies the dynamic output feedback (DOF) control problem (CP) for rectangular discrete-time descriptor Markov jump systems (RDDMJSs) with Lipschitz nonlinear terms. There is no restriction on the relationship between the number of system equations and the dimensions of states. First, according to designing general DOF controllers, the resulted closed-loop systems are converted into square DMJSs (SDMJSs). Then, a novel sufficient condition is proposed in terms of linear matrix inequality (LMI) based on the stochastic Lyapunov theory, S-procedure and with free matrices, which guarantees the stochastic admissibility and the existence and uniqueness of solution of the closed-loop systems. Furthermore, by utilising the matrix decoupling technique (MDT) and introducing relaxation matrices, the controller design algorithm is given, and the general DOF controllers with free dimensions are designed. Last, two numerical examples and a practical application are provided to demonstrate the validity and superiority of the proposed methods.

Mathematical model based on nonlinear differential equations and its control algorithm

Abstract A practical examination of the traditional robotic arm (RA) in operation revealed a significant limitation in its ability to control the position of motion. This underscores the urgent need to enhance the current RA’s position control capabilities. Therefore, this study proposes the use of nonlinear differential equations (NDEs) to establish a mathematical model and the design of NDE-based RA motion control algorithms in conjunction with a central pattern generator neural network. A comparison of the control effects showed that the proposed method was highly fitted to the target trajectory. The joint node (JN) motion tracking trajectories of the three RAs were similar, up to 90–85% to the target trajectories of the JNs. In addition, the control of the motion position was similar up to 95–98% to the target motion position trajectories. The motion control algorithm based on NDEs was effective in improving the average execution time of the Pareto optimal frontier of the RA by 58.29%. The joint velocity and angle changes of the three types of RAs under the NDE control algorithm exhibited a high degree of similarity to the fluctuations observed in the expected and predicted curves. These observations contribute to an understanding of the effectiveness of the system observer in observing the joint angle changes. This indicates that the motion control based on NDEs can effectively enhance the tracking effectiveness of the JN positions of the RA, improve the control ability of the RA motion, and increase the joint stability of the RA.

Study on pH control of marine acid injection mixture based on linear active disturbance rejection

Abstract In pH control system of acid injection mixture, the flow rate is easily disturbed due to the constant change of displacement in the platform acid flow control loop and the platform water flow control loop. In this study, a control strategy is proposed, that is, both the inner and outer ring controllers are linear active disturbance rejection controllers. The controller adopts linear active disturbance rejection controller, and the pH value of the mixture is controlled by cascade ratio. On the basis of the second order controller, the second order linear active disturbance rejection controller is designed based on the electric throttle valve and mixing tank model, and the simulation platform of pH control system of acid injection mixture is built by MATLAB(MATrix LABoratory)/Simulink. The controller design and simulation results of linear active disturbance rejection control algorithm and PID(Proportional Integral Derivative) algorithm are compared respectively. The simulation results show that under the interference of external uncertainties, the designed controller can not only track pH value quickly, but also reduce the overshoot and the stability time when disturbed, and effectively improve the dynamic response performance and robustness of the system.

Network modulation at stable states.

Advances in microarray and sequencing technologies have made possible the interrogation of biological processes at increasing levels of complexity. The underlying biomolecular networks contain large numbers of nodes, yet interactions within the networks are not known precisely. In the absence of accurate models, one may inquire if it is possible to find relationships between the states of such networks under external changes, and in particular, if such relationships can be model-independent. In this paper we introduce a class of such relationships. The results are based on the observation that changes to the equilibrium state of a network due to an alteration in an external input are "small" compared to the change in the input, a phenomenon we refer to as network modulation. It relies on the stability of the state. One consequence of network modulation is that response surfaces containing expression profiles of different mutants of an organism are low-dimensional linear subspaces. As an example, the expression profile of a double-knockout mutant generally lies close to the plane defined by the expression profiles of the wild-type and those of the two single-knockout mutants. This assertion is validated using experimental data from the sleep-deprivation network of Drosophila and the oxygen-deprivation network of Escherichia coli. The linearity of response surfaces is crucial in the design of a feedback control algorithm to move the underlying network from an initial state to a prespecified target state.

A Survey of Real-time Simulation and Hardware-in-the-loop Technology

This paper introduces the definition and background of real-time simulation and hardware-in-the-loop technology, and explains the concept, development process and application field of real-time simulation and hardware-in-the-loop technology, as well as its importance and advantages in practical applications. The research methods and technologies for real-time simulation and hardware-in-the-loop technology include commonly used methods and technologies for model establishment, control algorithm design, system integration, and experimental verification. For the application of real-time simulation and hardware-in-the-loop technology, specific cases in different fields are also listed, such as the design of distribution automation terminal in the kernel, the research of virtual test system of hardware-in-the-loop for unmanned vehicles, etc. Finally, the current challenges of real-time simulation and hardware-in-the-loop technology are stated, and the future development trend of the technology is summarized.

Research on Tracking Control Technology Based on Fuzzy PID in Underwater Optical Communication

In order to realize effective laser communication on underwater mobile platforms, the active tracking and alignment technology of underwater laser communication (UWLC) is studied. Firstly, the servo control principle of the UWLC system is analyzed. Secondly, aiming at underwater disturbance, an adaptive fuzzy PID controller is designed to realize parameter self-tuning, thereby improving the system’s anti-disturbance ability. The designed algorithm was simulated, and the simulation results show that the adaptive fuzzy PID algorithm has better anti-disturbance ability and tracking performance than the traditional PID. Finally, an experimental platform was built for dynamic tracking experiments, and the results show that the dynamic tracking accuracy of the designed control algorithm was improved by 30.29% compared with the traditional control algorithm, which provides a certain reference for the development of laser communication on underwater moving platforms.

Enhanced control of grid-connected multi-machine wind power generation systems using fuzzy backstepping approaches

This research paper presents an approach for enhancing the performance of a multi-machine wind power generation system (WPGS) through the combination of nonlinear and intelligent control techniques. The suggested approach focuses on optimizing the WPGS, which utilizes permanent magnet synchronous generators (PMSGs) interconnected with the grid. To enable grid connection, a back-to-back configuration is employed for both the rectifier and inverter in a fifteen-switch configuration, with control achieved through pulse width modulation. The designed control algorithm is designed to effectively regulate the system and reduce the active and reactive power ripples. Initially, the WPGS model is provided. Subsequently, the fuzzy backstepping control (FBC) law is detailed, incorporating the Lyapunov stability technique. Fuzzy logic is used to adapt the gains in the backstepping approach, ensuring the control system can accommodate disturbances and system variations. As a result, this adaptive control approach enables optimal effusiveness of the entire system in both static and dynamic operational regimes. Experimental tests were conducted using MATLAB to compare the efficacy of the designed strategy against the traditional backstepping control approach. The obtained results demonstrated the robustness and effective reference tracking capabilities of the designed FBC approach, along with the complete elimination of speed overshoot across diverse wind conditions. These findings validate the designed control approach's effectiveness and superiority over the BC approach under varying conditions.

Development of a Mathematical Model of a Highly Maneuverable Robot for Simulation of Robots with Different Types of Designs

The article presents the results of developing a mathematical model and simulation modeling of a three-wheeled highly maneuverable mobile robot. A mathematical model of the robot in the state space has been developed. The possibility of using this robot with rotary modules for analyzing and testing control algorithms for robots of various designs has been considered. The relevance of the study is justified by the high costs of creating physical models of robots for experimental verification of control algorithms. The proposed three-wheeled mobile robot with rotary modules, where each wheel is simultaneously both a driving and a steering wheel, allows reducing these costs, while maintaining the ability to simulate part of the real operating conditions. Simplified diagrams of the robot are given, the main parameters and equations characterizing the change in linear and angular velocity are described. Particular attention is paid to modeling various types of robot drives: automotive type and with a differential drive. In the discrete state space, equations describing the dynamics of the robot were presented, which allows them to be used in software products for modeling. The results of simulation modeling obtained in the SimInTech software environment confirm the effectiveness of the proposed model. The analysis of the obtained results showed that the use of a three-wheeled robot with rotary modules allows successfully simulating the behavior of robots with other types of drives and creating conditions close to real ones for testing control algorithms. The proposed model can be used to improve the quality of design and testing of control algorithms in robotics, while reducing the costs of creating physical models of robots, as well as in the educational process.

Optimal estimation of SoC, SoH using machine learning models and design of control algorithm for active cell balancing in lithium ion batteries

Abstract Electric vehicles transform the automotive industry by replacing traditional vehicles powered by fossil fuels with less polluting and efficient vehicles. They are powered by rechargeable Li-ion batteries. While there are drawbacks associated with Li-ion battery technology, it's important to note that these challenges can be addressed or mitigated effectively. A battery management system ensures the tracking of all functions performed by the battery. An advanced battery management system should accurately estimate the battery's state of health and state of charge. This paper aims to develop machine learning models for the estimation of the state of health and state of charge and to implement cell balancing in a battery management system. A dataset of battery charging and discharging profiles was used to train various machine learning models for estimation. These methods are computationally less complex than conventional methods. In addition, a cell balancing algorithm is implemented to control the charging and discharging of individual cells in a battery pack and balance the state of charge of each cell. The machine learning solution is created using several machine learning models, including Gradient Boosting, Random Forest, Linear Regression, AdaBoost, and Multi-layer Perceptron. Among the models Gradient Boosting and Random Forest provide good MSE and R2 score. The use of machine learning algorithms for the assessment of state of health and charge estimation combined with the design of an efficient control algorithm for active cell balancing offers significant advancements in the battery management system to optimise the performance, reliability, and useful life of Li-ion batteries. The paper also presents a case study utilising a combination of deep learning-based SoC, SoH estimation algorithms in a simulated data set. A well-designed control algorithm for active cell balancing presents a holistic and effective approach to optimise the performance, reliability, and lifespan of Li-ion batteries.

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Integer and fractional-order model matching controller design algorithm with generalized reference model formulation for nonlinear time delay servo and regulatory problems: A real-time validation

Precise control algorithm of rotational speed of the valve-controlled hydraulic motor under load fluctuating conditions.

The present study focuses on the analysis and design of a novel fuzzy adaptive PID control algorithm, aiming to enhance the speed control accuracy of valve-controlled hydraulic motors under load fluctuating conditions. The method of model identification was adopted to calculate the transfer function of valve-controlled hydraulic motor based on the established hydraulic technology scheme. The chassis of the valve-controlled hydraulic motor is subsequently investigated, and a model for E-grade pavement is constructed using the harmonic superposition method. Through dynamic simulation, the load fluctuation range under two common operating conditions is determined. The fuzzy adaptive PID algorithm was subsequently designed in detail, with the error and its rate of change being considered as input parameters, while the increments of the proportional coefficient, integral constant, and differential constant were regarded as output parameters. Co-simulation data indicate that, compared with the PID algorithm, the average error of the fuzzy adaptive PID algorithm can be reduced by more than 50% and the rise time is reduced by 0.04 seconds. To validate the theoretical analysis, a tracked hydraulic chassis was developed and tested. At an expected speed of 200 rev/min, the average error decreased by 1.68 rev/min, while at an expected speed of 1000 rev/min, the average error reduced by 2.68 rev/min. The designed intelligent control algorithm can effectively improve the control accuracy and stability under load fluctuating conditions.

Efficient nonlinear model predictive and active disturbance rejection control for trajectory tracking of unmanned vehicles

This article proposes an efficient trajectory tracking control strategy of unmanned vehicles. The method is based on nonlinear model predictive control (NMPC) and active disturbance reject control (ADRC). The designed control algorithm considers three challenges including nonlinear characteristics, multiple constraints, and external disturbance. First, NMPC method is presented for the nonlinear vehicle model with multiple constraints. To relax inequality constraints and reduce the heavy calculation burden, the penalty term with the variable factor is added to the cost function, an improved continuous/generalized minimum residual method is proposed to solve NMPC online optimization problem. Then, an ADRC scheme is designed to estimate the unknown disturbance via extended state observer, and compensate them by feedback control law in real time, the corresponding parameters are obtained by a novel particle swarm optimization algorithm to further improve the control precision. It ensures the stability to a certain extent. Finally, the results indicate the designed algorithm can raise the calculation efficiency and meet the real-time requirements, and obviously increase the tracking accuracy and robustness performance.

3D dynamics and control of a snake robot in uncertain underwater environment

Abstract The snake robot can be used to monitor and maintain underwater structures and environments. The motion of a snake robot is achieved by lateral undulation which is called the gait pattern of the snake robot. The parameters of a gait pattern need to be adjusted for compensating environmental uncertainties. In this work, 3D motion dynamics of a snake robot for the underwater environment is proposed with vertical motion using the buoyancy variation technique and horizontal motion using lateral undulation. “The neutral buoyant snake robot motion in hypothetical plane and added mass effect is negligible”, these previous assumptions are removed in this work. Two different control algorithms are designed for horizontal and vertical motions. The existing super twisting sliding mode control (STSMC) is used for the horizontal serpentine motion of the snake robot. The control law is designed on a reduced-ordered dynamic system based on virtual holonomic constraints. The vertical motion is achieved by controlling the mass variation using a pump. The water pumps are controlled using the event-based controller or Proportional Derivative (PD) controller. The results of the proposed control technique are verified with various external environmental disturbances and uncertainties to check the robustness of the control approach for various path following cases. Moreover, the results of STSMC scheme are compared with SMC scheme to check the effectiveness of STSMC. The practical implementation of the work is also performed using Simscape Multibody environment where the designed control algorithm is deployed on the virtual snake robot.

Advanced Perturb and Observe Algorithm for Maximum Power Point Tracking in Photovoltaic Systems with Adaptive Step Size

Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&O algorithm has some limitations such as oscillations and steady-state error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m² and 2000w/m²) and see the system response. The performance of the enhanced P&O algorithm has been evaluated using MATLAB simulations.

Cooperative learning‐based practical formation‐containment control with prescribed performance for heterogeneous clusters of UAV/USV

SummaryIn this paper, a new approach for formation‐containment control with prescribed performances is introduced for heterogeneous autonomous vehicles involving a cluster of leader unmanned aerial vehicles (UAVs) and follower unmanned surface vessels (USVs). We introduce a two‐layer distributed control system: The upper layer focuses on guiding the UAVs to form a scalable lattice while synchronizing their movement along a predefined path, and the second layer guides the USVs to enter the convex hull formed by the UAVs, ensuring collision‐free operation with static/dynamic objects. To prevent collisions and ensure lattice formation, a set of well‐defined bump functions are utilized in the design of the backstepping control algorithm. Managing virtual controls, we incorporate a nonlinear dynamic surface control (NDSC), while a universal barrier function enhances the convergence of formation tracking errors. Furthermore, each USV employs a cooperative adaptive learning neural network to handle uncertainties in heterogeneous vehicle models. Utilizing the Lyapunov theorem, the stability of the formation‐containment of UAV/USV is achieved, and all signals in the formation‐containment systems are semiglobal uniform ultimate bounded (SGUUB). A simulation example showcases the effectiveness of our proposed approach, highlighting contributions in collision avoidance, synchronization speed, and adaptive learning. Our work advances the heterogeneous formation‐containment literature towards more realistic scenarios with safety‐critical considerations amidst multiple layers of uncertainties and unknown parameters.