Traditional Control Algorithm Research Articles

Distributed active vibration control for helicopter based on diffusion collaboration

The active vibration control technology has been successfully applied to several helicopter types. However, with the increasing of control scale, traditional centralized control algorithms are experiencing significant increase of computational complexity and physical implementation challenging. To address this issue, a diffusion collaboration-based distributed Filtered-x Least Mean Square algorithm applied to active vibration control is proposed, drawing inspiration from the concept of data fusion in wireless sensor network. This algorithm distributes the computation load to each node, and constructs the active vibration control network topology of large-scale system by discarding the weak coupling secondary paths between nodes, achieving distributed active vibration control. In order to thoroughly validate the effectiveness and superiority of this algorithm, a helicopter fuselage model is designed as the research object. Firstly, the excellent vibration reduction performance of the proposed algorithm is confirmed through simulations. Subsequently, specialized node control units are developed, which utilize STM32 microcontroller as the processing unit. Further, a distributed control system is constructed based on multi-processor collaboration. Building on this foundation, a large-scale active vibration control experimental platform is established. Based on the platform, experiments are carried out, involving the 4-input 4-output system and the 8-input 8-output system. The experimental results demonstrate that under steady-state harmonic excitation, the proposed algorithm not only ensures control effectiveness but also reduces computational complexity by 50%, exhibiting faster convergence speed compared with traditional centralized algorithms. Under time-varying external excitation, the proposed algorithm demonstrates rapid tracking of vibration changes, with vibration amplitudes at all controlled points declining by over 94%, proving the strong robustness and adaptive capability of the algorithm.

Decoupling modeling design and high-precision position control of fast steering mirror

For a two-axis fast steering mirror (FSM) based on the flexible chain structure support and voice coil motor drive, the two axes are strongly coupled. This strong coupling may decrease the system positioning accuracy. In this study, the electric kinematics model associated with the voice coil motor and the motion and frequency equations associated with the flexible chain structure were analysed. A comprehensive system model was established considering shaft coupling, a digital controller with compensation coupling was designed, and the method of active compensation was used for decoupling. The experimental results showed that compared with the traditional non-decoupling control algorithm, the decoupling control method decreased the coupling degree from 7% to 0.8% and reduced the positioning accuracy error from 1.5% to 0.3%.

Vibration IOT Control Algorithm for Steel Structures of Concrete Buildings with Elastic Boundary Constraints

Abstract Because of the influence of steel structure’s elastic properties in concrete buildings, the traditional vibration Internet of Things (IOT) control algorithm of steel structures has the problem of low control efficiency. Based on the research subject of concrete buildings, a corresponding steel structure model is established, and the corresponding vibration control standard is determined according to the characteristics of the model. In this model, the boundary elastic constraints of the steel structure are set according to its structural characteristics, and the vibration characteristics of the steel structure are analyzed under the constraints. The external vibration signal is simulated, and the response of the building’s steel structure under different signal excitations is observed. According to the response characteristics of the steel structure, the vibration controller is designed and installed, the real-time vibration signal is input into the controller, and finally the vibration control of steel structure of concrete building is realized. By comparison with the traditional vibration IOT control algorithm, it is concluded that the vibration control method designed by combining vibration frequency and vibration amplitude has smaller error and shorter time cost, that is, the designed vibration IOT control algorithm has more advantages in control efficiency.

Evaluation and deployment of a unified MPPT controller for hybrid Luo converter in combined PV and wind energy systems

AbstractThis work emphasizes the development and examination of a Hybrid Luo Converter integrated with a unified Maximum Power Point Tracking (MPPT) for both grid and independent hybrid systems. The primary objectives of this hybrid system are to efficiently harness power from intermittent and variable renewable sources while elevating low-voltage energy inputs to utility-grade levels. Unlike previous studies employing specific MPPT algorithms for solar and wind sources, this work aims to simplify the control system by utilizing a unified MPPT controller. This research also introduces a novel approach involving dual-lift hybrid Luo converters to create hybrid systems, operating exclusively or concurrently based on the availability of renewable resources. To maximize power generation from all renewable sources, a unified MPPT algorithm is developed. The hybrid system, incorporates 500 W wind and 560 W PV systems, the innovative Luo converter, and the unified MPPT controller. A comprehensive comparative analysis is presented, comparing the hybrid system's performance with that of traditional control algorithms, such as the Perturb & Observe, and Radial Basis Function Network controllers. The successful prototype of the converter validates the practicality of the proposed approach.

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

Laser power stabilization system based on variable universe fuzzy proportional‐integral‐differential

AbstractA variable universe fuzzy proportional‐integral‐differential (PID) algorithm was designed and first application for a laser power stabilization system. To validate the algorithm's effectiveness, it is applied to the simulation model of Simulink and the experimental system based on an acousto‐optic modulator. The results demonstrate that the variable universe fuzzy PID presents higher control precision, better robustness, and can suppress long‐term drift phenomena compared with the traditional feedback control algorithm. The algorithm design method in this paper has significant reference and guidance value for laser power stabilization system.

SDN-Based Congestion Control and Bandwidth Allocation Scheme in 5G Networks

5G cellular networks are already more than six times faster than 4G networks, and their packet loss rate, especially in the Internet of Vehicles (IoV), can reach 0.5% in many cases, such as when there is high-speed movement or obstacles nearby. In such high bandwidth and high packet loss network environments, traditional congestion control algorithms, such as CUBIC and bottleneck bandwidth and round-trip propagation time (BBR), have been unable to balance flow fairness and high performance, and their flow rate often takes a long time to converge. We propose a congestion control algorithm based on bottleneck routing feedback using an in-network control mode called bottleneck routing feedback (BRF). We use SDN technology (OpenFlow protocol) to collect network bandwidth information, and BRF controls the data transmission rate of the sender. By adding the bandwidth information of the bottleneck in the option field in the ACK packet, considering the flow fairness and the flow convergence rate, a bandwidth allocation scheme compatible with multiple congestion control algorithms is proposed to ensure the fairness of all flows and make them converge faster. The performance of BRF is evaluated via Mininet. The experimental results show that BRF provides higher bandwidth utilization, faster convergence rate, and fairer bandwidth allocation than existing congestion control algorithms in 5G cellular networks.

A control method for the single-phase three-leg unified power quality conditioner without a phase-locked loop

The single-phase three-leg unified power quality conditioner (UPQC) can achieve the functions of voltage compensation, reactive power compensation, and harmonic compensation. However, traditional control algorithms require a phase-locked loop to obtain the real-time phase angle of the grid voltage, which undoubtedly increases algorithm complexity. To simplify the phase-locked calculation, this paper proposes a control method without the phase-locked loop for the single-phase three-leg UPQC. In the proposed scheme, the instantaneous value of the grid voltage is employed to realize the grid integration control. Then, the load voltage reference is calculated in real time using a second-order generalized integrator. Moreover, a simple algorithm for reactive power and harmonic compensation is discussed, further simplifying the control algorithm. Finally, a small-scale experimental platform is built, and the effectiveness of the proposed method is verified by the experimental results.

A GWO-PID controller with advanced optimization features for DC-DC converters

This work seeks to use both traditional control algorithms and advanced optimization algorithms to enhance the performance of a DC-DC converter. The chosen algorithm was Proportional-Integral-Derivative (PID) based on gray wolf optimization (GWO). The PID controller is known for its ease of control and wide range of industrial applications. This type of controller has been used successfully in many types of systems, such as power electronics, automation systems, robotics, etc., due to its ability to effectively optimize the system's parameters with minimal effort from the user. To test this new technique on a DC-DC converter different simulations were conducted using MATLAB environment where various parameters were set that can simulate various uses for the DC-DC converter within electrical systems. After conducting these tests it was found that PID based on GWO controller had good performance (rise time 0.0004 sec, settling time 0.0001sec) when compared with other traditional controllers (rise time 0.00416 sec, settling time 0.000323sec), reliability, efficiency higher accuracy, low cost, etc. As expected GWO showed better results than conventional methods like PID or PI controllers due to the fact that it’s an evolutionary approach that allows more flexibility during the configuration process.

Analysis of control algorithms for water quality testing robots

The research background of the water quality detection robot lies in effectively monitoring and assessing the quality of water bodies to address the increasingly severe issue of water pollution. This technology, employing advanced sensors and machine learning algorithms, facilitates automated acquisition of water quality data, enabling real-time analysis and reporting of various indicators in the water bodies. Consequently, it provides crucial evidence for safeguarding the environment and human health. This article investigates the problem of posture control in water quality detection robots operating in complex underwater environments. It compares three control algorithms: the traditional PID fuzzy control algorithm, and a BP neural network-based PID algorithm. Through experimental simulation, these algorithms are evaluated based on their differences in system dynamic performance. The results indicate that the utilization of the BP neural network PID algorithm significantly enhances both stability and control precision of the robot when dealing with non-linear environments characterized by uncertainties. This algorithm combines traditional PID control with neural network technology, allowing for adaptive optimization of PID controller parameters through training the neural network. As a result, it overcomes limitations associated with traditional PID and fuzzy control algorithms while improving overall performance.

A Comparison Study between Traditional and Deep-Reinforcement-Learning-Based Algorithms for Indoor Autonomous Navigation in Dynamic Scenarios.

At the beginning of a project or research that involves the issue of autonomous navigation of mobile robots, a decision must be made about working with traditional control algorithms or algorithms based on artificial intelligence. This decision is not usually easy, as the computational capacity of the robot, the availability of information through its sensory systems and the characteristics of the environment must be taken into consideration. For this reason, this work focuses on a review of different autonomous-navigation algorithms applied to mobile robots, from which the most suitable ones have been identified for the cases in which the robot must navigate in dynamic environments. Based on the identified algorithms, a comparison of these traditional and DRL-based algorithms was made, using a robotic platform to evaluate their performance, identify their advantages and disadvantages and provide a recommendation for their use, according to the development requirements of the robot. The algorithms selected were DWA, TEB, CADRL and SAC, and the results show that-according to the application and the robot's characteristics-it is recommended to use each of them, based on different conditions.

Distributed region reaching consensus control for uncertain networked multi‐robot systems

AbstractThe main objective of this paper is to advance the concept of region reaching control for a single robot to the study of region reaching consistency for uncertain fully‐actuated multi‐robot systems formulated by Lagrange dynamics. By introducing a directed network communication topology and utilizing the pinning‐like control methods, a distributed region reaching consensus control strategy is proposed. The desired region shape can be specified by selecting an appropriate potential objective function. A feature of the developed region reaching consensus algorithm is fully distributed, which is also an important generalization of pinning‐like set point tracking scheme based on the typical proportion‐integration‐differentiation control scheme. More importantly, the scope of consistency in this paper can be explicitly defined, which is different from traditional consistency control algorithms. Subsequently, numerical examples are presented to illustrate the performance of the designed controllers.

GWO-PID of Two-phase Hybrid Stepping Motor for Robotic Grinding Force

The use of the computer program MATLAB is prominent in many studies that simulate many industrial systems. The current simulation aims to build a suitable simulation model representing the Two-phase Hybrid Stepping Motor (2Ph-HSM). This type of motor is employed in a specific application to produce a force called automatic grinding force. To control the force, motor speed, and location, we need to add control systems, so two methods have been proposed, one of which is traditional, namely proportional, integral, and derivative (PID) control and the other is intelligent, called Gray Wolf Optimization (GWO). The current work also aims to use traditional control algorithms and advanced optimization algorithms that were chosen for their ease of control and possibility of use in many industrial applications. By setting appropriate specifications for the simulation model and after conducting prescribed tests that simulate different applications of the motor’s work within electrical systems, the results demonstrated good motor performance, better response, and high accuracy, in addition to speed. The goal is to design and tune a proportional, integral, and derivative (PID) controller by gray wolf optimization (GWO) using the transfer function (TF) of a 2Ph-HSM. To adjust the parameters of conventional controllers using advanced optimization, a suitable mechanism and technique were selected from advanced optimization techniques, where the gray wolf technique algorithm was chosen as an optimization technique and Integrated Time Absolute Error (ITAE) to adjust the parameters of conventional PID controller.

Energy-Saving Control Method for Factory Mushroom Room Air Conditioning Based on MPC

The energy consumption of the mushroom room air conditioning system accounts for 40% of the total energy consumption of the mushroom factory. Efficient and energy-efficient mushroom factories and mushroom houses are the development direction of the industry. Compared with maintenance structure transformation and air conditioning equipment upgrading, energy-saving technology based on regulation methods has the advantages of less investment and fast effectiveness, which has attracted attention. The current methods for regulating air conditioning in edible mushroom factories include simple on/off thermostat control or PID. In the field of energy efficiency in commercial building air conditioning, a large number of studies have shown that compared with traditional control algorithms such as classic on/off or PID control, model predictive control can significantly improve energy efficiency. However, there is little literature mentioning the application of MPC in factory mushroom production rooms. This paper proposes a data-driven MPC and PID combined energy-saving control method for mushroom room air conditioning. This method uses the CNN-GRU-Attention combination neural network as the prediction model, combined with prediction error compensation and dynamic update mechanism of the prediction model dataset, to achieve an accurate prediction of indoor temperature in mushroom houses. Establish an objective function for air conditioning control duration and temperature, use the non-dominated sorting genetic algorithm II (NSGA-II) to solve for the optimal control sequence of the air conditioning in the control time domain, and use the entropy weight method to determine the optimal decision quantity. Integrate rolling optimization, feedback mechanism, and PID to achieve precise and energy-saving control of the mushroom room environment. The experimental results show that compared with the on/off thermostat and PID controller, the designed controller reduces power consumption by 12% and 5%, respectively, and has good application and demonstration value in the field of industrial production of edible mushrooms.

Research on Intelligent Control Method of Launch Vehicle Landing Based on Deep Reinforcement Learning

A launch vehicle needs to adapt to a complex flight environment during flight, and traditional guidance and control algorithms can hardly deal with multi-factor uncertainties due to the high dependency on control models. To solve this problem, this paper designs a new intelligent flight control method for a rocket based on the deep reinforcement learning algorithm driven by knowledge and data. In this process, the Markov decision process of the rocket landing section is established by designing a reinforcement function with consideration of the combination effect on the return of the terminal constraint of the launch vehicle and the cumulative return of the flight process of the rocket. Meanwhile, to improve the training speed of the landing process of the launch vehicle and to enhance the generalization ability of the model, the strategic neural network model is obtained and trained via the form of a long short-term memory (LSTM) network combined with a full connection layer as a landing guidance strategy network. The proximal policy optimization (PPO) is the training algorithm of reinforcement learning network parameters combined with behavioral cloning (BC) as the reinforcement learning pre-training imitation learning algorithm. Notably, the rocket-borne environment is transplanted to the Nvidia Jetson TX2 embedded platform for the comparative testing and verification of this intelligent model, which is then used to generate real-time control commands for guiding the actual flying and landing process of the rocket. Further, comparisons of the results obtained from convex landing optimization and the proposed method in this work are performed to prove the effectiveness of this proposed method. The simulation results show that the intelligent control method in this work can meet the landing accuracy requirements of the launch vehicle with a fast convergence speed of 84 steps, and the decision time is only 2.5 ms. Additionally, it has the ability of online autonomous decision making as deployed on the embedded platform.

Stable skill improvement of quadruped robot based on privileged information and curriculum guidance

Quadruped robots have attracted many researchers due to their unique advantages. In the field of control, deep reinforcement learning (DRL) saves the complex and tedious design of traditional control algorithms and motivates the robot to learn the motion patterns by itself, which is a promising alternative approach. However, due to the lack of prior information that is difficult to obtain in the physical world, policy usually exhibits a single motion state when faced with multiple scenarios. In this paper, we adopt a teacher–student policy architecture that uses the student policy to implicitly identify the teacher policy containing a privileged information, enabling the robot to adjust its motion depending on the environment it is in. We propose the synthetic terrain-command curriculum that assigns different levels of command curriculum depending on the difficulty of the terrain, which is useful for improving policy adaptation and stability. Policy is often trapped in local optima due to the nonlinearity of the robot system and unreasonable parameters. We propose a self-correction mechanism to jump out of this state in time, so that the policy can be steadily improved. We demonstrate our policy on the real quadruped platform named SDUQuad-48, and the results show that the learned policy exhibits state-of-the-art performance in both high-speed locomotion and complex terrain adaptation.

A Comprehensive Review: Mitigating Techniques for Power Variability Due to Integration of Renewable Energy Sources With Grid

Renewable energy sources like solar panels and wind turbines are becoming more important for generating power. The inconsistent and unreliable output of solar panels and wind turbines is due to factors like clouds, weather conditions, and wind speed that cause their energy production to fluctuate. Connecting intermittent sources to the utility grid creates difficulties in different technical areas, such as ensuring a steady and reliable power supply, protecting the system from disruptions, controlling the distribution of generated power, and maintaining a consistent level of power quality. In this situation, adjusting the amount of electricity produced by an intermittent energy source to keep the power grid stable is essential. This issue must be resolved as soon as possible. The extension to these locations diminishes the grid's strength. It leads to a scenario where a solar power plant is integrated into a subpar electric grid. However, integrating solar power into a shoddy AC infrastructure has problems with power quality (PQ) problems, which restricts penetration levels. This review article suggests various FACTS devices and various conventional, adaptive, and AI-based algorithms to reduce PQ issues brought on by a weak grid and increase renewable energy source (RES) penetration levels. This paper discusses the PQ issues that occur when RES like solar power plants (SPP) and wind power plants (WPP) or both as hybrid are connected to the electrical grid. It also explores different ways to reduce the fluctuation in power output from the solar panels. It analyzes the numerous power quality issues that arise with solar penetration and PQ mitigation methods using several FACTS devices and control algorithms, including traditional control, adaptive control, and AI-based control algorithms. For the benefit of engineers and academics working in this field of study, various research publications have been carefully evaluated, organized, and placed on this paper for convenient reference. It also briefly discusses the plan for controlling battery storage system for solve this problem.

Load frequency control strategy for islanded multimicrogrids with V2G dependent on learning‐based model predictive control

AbstractSystem reliability and stability can be significantly improved by the interconnected operation of multimicrogrids, and electric vehicles (EVs) provide a more flexible solution for frequency control, which also present challenges for frequency control. Therefore, a load frequency control (LFC) strategy for multimicrogrids with vehicle to grid (V2G) dependent on learning‐based model predictive control (MPC) is proposed. First, a controller‐interconnected multimicrogrid topology is proposed; thus, a multimicrogrid consisting of microturbines (MTs), distributed power sources, and EVs and their random power constraints is established. Second, a control parameter adaptive algorithm based on learning‐based MPC is designed. The real‐time frequency offset and EV station output power boundary are used as the state set, adjustable parameters of the MPC controller are used as the action set, and reward function is set with frequency deviation so that the adaptive adjustment of the weight parameters of the MPC controller is realised. Additionally, the improved MPC controller designed in this paper can transform the frequency control process into an optimization problem, which is well adapted to various random constraints in the control process. In addition, the deep deterministic policy gradient (DDPG)‐MPC double‐layer controller can prevent machine learning controller failure. Finally, the simulation results show that, compared with traditional control and MPC algorithms, the learning‐based MPC controller applied to the controller interconnection structure can exchange information between submicrogrids. Moreover, based on the experience accumulated in the prelearning process, the controller parameters can be updated according to the environmental state in real time, thereby significantly improving the robustness and rapidity of the multimicrogrid frequency control process. Meanwhile, compared with a traditional DDPG controller, the proposed controller with double‐layer coupling structure can better ensure the safe operation of the multimicrogrid system when the machine learning agent fails and cannot output actions normally.

Transient power equalization control strategy of virtual synchronous generator in isolated island microgrid with heterogeneous power supply

In the parallel supply system of synchronous generator and virtual synchronous generator, the physical structure and control structure of the two kinds of power supply are quite different, and it is difficult to distribute the transient power of the two kinds of power supply evenly when the load changes abruptly. Especially in the case of sudden load increase, virtual synchronous generator bears too much load in the transient process because of its fast adjustment speed, and even causes short-term overload, which makes the capacity of virtual synchronous generator can not be fully utilized. In view of this problem, firstly, the mechanism of transient power uneven distribution of two heterogeneous power sources is explained from the differences of frequency modulation, voltage regulation and output impedance. Secondly, virtual speed regulation, virtual excitation and dynamic virtual impedance are added to the traditional virtual synchronous generator control to simulate the speed regulation characteristics and electromagnetic transient characteristics of the synchronous generator, so as to realize the transient and steady-state power equalization between heterogeneous power supplies when the virtual synchronous generator and the synchronous generator run in parallel. Thirdly, in order to ensure the fast dynamic response characteristics of the virtual synchronous generator in independent operation mode, the traditional virtual synchronous generator control algorithm is still maintained in independent operation mode, and the mode switching control link based on state tracking is designed to realize smooth switching between the two working modes. Finally, the hardware in loop experiment results based on RT-LAB show that the proposed control method can realize the transient and steady-state power equalization when the virtual synchronous generator and the synchronous generator operate in parallel, it can keep the fast voltage regulation and frequency modulation ability when the virtual synchronous generator operates independently, and can realize smooth switching between independent and parallel operation modes.

An Intelligent Maximum Power Point Tracking Strategy for a Wind Energy Conversion System Using Machine Learning Algorithms

Background: Machine Learning (ML) techniques have successfully replaced traditional control algorithms in recent years due to their ability to carry out complicated tasks with significant efficiency and accuracy. Objective: The main objective of the current work is to investigate and compare the performances of different ML models in modeling Maximum Power Point Tracking (MPPT) control for a wind turbine system. The main advantage of the designed MPPT based on ML is that it does not require any detailed mathematical model or prior knowledge of the system, such as turbine parameters or aerodynamic properties, unlike traditional MPPT techniques. Methods: The ML models included in this study were Support Vector Machines, Regression Trees, and Ensemble Trees. Their design was performed through a training process, and their performances were evaluated based on various metrics. During the training phase, the ML models were selected to understand the basic concept of the control strategy and extract essential hidden connections between the inputs and the output of the system. Results: The effectiveness of the control method was investigated using MATLAB/Simulink. The findings of this study revealed that ML models were effective in modeling the MPPT for the studied wind power system, which provides an interesting and sophisticated alternative to classical control methods for wind systems. Conclusion: The ML models designed allow for optimal operation of the system with a simple structure that is independent of system parameters and wind speed measurement and is adaptable for any kind of system.