Control Algorithm Research Articles

Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy

Multi-area integrated energy systems (MA-IES) with a high proportion of renewable energy have become a trend in social development. The access to a large number of distributed energy sources makes the safe, stable, and economic operation of MA-IES suffer serious challenges. To reduce the frequency deviation and area control error (ACE) of MA-IES and to improve the economy of operation, this study proposed a hybrid modeling with data enhanced driven learning algorithm, which combines model-driven and data-driven algorithms, named combined proportion-integral-derivative and deep reinforcement learning (CPIDDRL). Firstly, the frequency deviation signals are collected and judged, and the smaller frequency deviation signals, which are sent to the model-driven part, are regulated by proportion-integral-derivative (PID) controllers. Then, the ACE and large frequency deviation signals are collected and transmitted to the data-driven part, and the Transformer deep learning network is applied to predict the multi-feature timeseries, which are applied to strategy selection for state-action-reward-state-action reinforcement learning. Finally, the model-driven part and data-driven part work together to generate adjustment commands every 4 s. The control effects of CPIDDRL, PID, Q-learning, and sliding model control algorithms are compared in two and four areas integrated energy systems. The results show that, compared to the comparison algorithms, the CPIDDRL reduces the mean values of frequency deviation and ACEs by at least 46.78% and 6.83%, respectively, and the total generation cost by at least 8.20%. CPIDDRL has practical significance in improving system stability, enhancing renewable energy integration capabilities, and promoting the development of smart grids.

The Precision Improvement of Robot Integrated Joint Module Based on a New ADRC Algorithm

This article analyzes the factors causing the precision error of the robot joint module, such as gear meshing disturbance, output elastic deformation, and load mutation. An improved active disturbance rejection control (ADRC) algorithm is proposed to overcome the uncertainty of nonlinear factors and variables of this research topic. The output precision loss of the joint module is introduced as a new input variable of ADRC, combined with the input variable of torque current of the joint module. The extended state observer is redesigned, and the online estimation of disturbance is realized. According to the disturbance estimation results, the current loop algorithm of permanent magnet synchronous motors is improved to compensate the torque disturbance of the robot joint module. The experimental results show that the improved ADRC algorithm can obviously suppress the disturbance of the joint module, weaken the meshing error and torque output deformation of the harmonic reducer gear, and improve the control accuracy of the joint module.

Design and Testing of Electric Drive System for Maize Precision Seeder

To improve the expandability, seeding accuracy, and operating speed range of the electric drive system (EDS) of precision seeders, this study constructed an EDS based on a controller area network (CAN) bus and designed a motor controller based on a field-orientated control (FOC) algorithm. Full-factorial bench and field tests based on seed spacing (0.1, 0.2, and 0.3 m) and operating speed (3, 6, 9, 12, and 15 km/h) were carried out to evaluate the performance of the EDS. The results of bench tests showed that seeding quality varied inversely with operating speed and positively with seed spacing. The average quality of feed index (QFI) at 0.1, 0.2, and 0.3 m seed spacing in bench tests was 88.38%, 96.67%, and 97.36%, with the average coefficient of variation (CV) being 20.13%, 16.27%, and 13.20%. Analysis of variance confirmed that both operating speed and seed spacing had a significant effect on QFI and CV (p < 0.001). The analysis of motor rotational speed accuracy showed that the relative error of motor rotational speed above 410 rpm did not exceed 2.24%, and the relative error had less influence on the seeding quality. The average QFI was 85.93%, 95.91%, and 96.24%, with the average CV being 21.12%, 15.50%, and 16.49% at 0.1, 0.2, and 0.3 m seed spacing in field tests. The methods and results of this study can provide a reference for the design and optimization of the EDS in a maize precision seeder and provide an effective solution for the improvement of maize yields.

Adaptive performance optimal control for flexible-joint robots with random noises: Design and experiment

This study developes a flexible performance optimal control scheme via reinforcement learning strategy and event-triggered mechanism for flexible-joint robots with random noise and non-affine input. It is notable that an event-triggered optimization mechanism is developed, which meets the optimality principle and saves communication resources. Nevertheless, the existing event-triggered strategy is unable to handle non-affine input, which limits the applicability of this method. To overcome the above problems, a modified event-triggered mechanism is proposed. At the same time, the optimal solution of the system is given by an optimized control algorithm based on the improved performance index function. In the controller design, neural network is used to deal with random disturbances and uncertainties, and an adaptive law is designed to replace the identifier structure. Besides, a flexible prescribed performance function is constructed to yield multiple prescribed performance behaviors by adjusting the key parameters, while the tracking error is stayed within a prescribed boundary. Finally, the effectiveness of the proposed control scheme is further demonstrated by simulation and the experiment of the 2-link flexible-joint robot on the Quanser platform.

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

Fertigation control system based on the mariotte siphon

This paper adopts the Mariotte siphon to simplify the nutrient solution preparation structure while improving the liquid mixing accuracy for nutrient solution. A liquid mixing model suitable for EC and pH regulation was constructed by combining fuzzy control algorithms, and a set of fertigation nutrient solution control equipment was designed and developed. During the experiment, comparison was made between the Fuzzy-PID algorithm and the traditional PID algorithm in terms of nutrient solution configuration. The results show that the Fuzzy-PID algorithm is smoother and more stable compared to the PID algorithm. Through analysis of the liquid mixing accuracy with the venturi type fertigation machine, it was found that the fertigation machine designed with the Mariotte structure is more accurate for liquid mixing, and can better meet the needs of crop growth.

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.

A learning‐based hierarchical energy management control strategy for hybrid electric vehicles

AbstractIn this work, a novel energy management control framework is developed for hybrid electric vehicles (HEVs) driving in car‐following scenarios. In order to enhance the energy efficiency while maintaining the driving safety, a hierarchical control approach consisting of an upper level speed tracking control scheme and a lower level energy management control strategy is proposed. For the upper level tracking control system, an iterative learning model predictive control (ILMPC) scheme is developed to guarantee the tracking performance and the driving safety simultaneously. Additionally, a model predictive control (MPC) algorithm is adopted at the lower level to optimize the torque distribution in real‐time based on the driving cycles generated by the upper level control system. With the proposed hierarchical control framework, HEVs are able to improve the energy efficiency significantly by taking the advantages of the operational repeatability. The convergence of the proposed control strategy is analyzed rigorously, and its effectiveness is illustrated through numerical simulations.

A Weighted AIMD Congestion Control Algorithm for Device-to-Device Communication in 5G Network

ABSTRACT Device-to-Device (D2D) communication offers promising benefits in 5G networks in terms of improved throughput and reduced latency. However, efficient congestion control remains a critical challenge to ensure optimal performance and resource utilization. Existing congestion control mechanisms often struggle with adapting to dynamic network conditions, leading to inefficiencies, such as underutilization of network resources or excessive packet loss. This paper addresses these challenges by proposing a novel Weighted AIMD congestion control algorithm tailored specifically for D2D communication in 5G networks (WACC-D2DC-5GN). Here, the Weighted AIMD Congestion Control Algorithm is proposed for estimating available bandwidth and Hunger Games Search Optimization to optimize the weight parameter of WACC. The proposed algorithm integrates weighted factors that dynamically adjust congestion window sizes based on real-time network feedback. Simulations are run on the NS3-mmWave module, NS3 LTE-A module. The simulation outcomes show that the proposed WACC-D2DC-5GN method attains 27.26%, 20.41%, and 23.26% higher SNR and 16.20%, 26.97%, and 30.34% higher Throughput when analysed with existing techniques like NexGen D-TCP: next generation dynamic TCP congestion control approach (NGD-TCP-CCA), user-centric channel allocation system for 5G higher-speed users by using ML to decrease handover rate (UCC-HSU-MLA), and efficient with reliable hybrid deep learning-allowed method for congestion control in 5G/6G networks (HDL-EMC-CN) respectively.

Design of LQ regulators with prescribed degree of stability for dual-rate systems based on reinforcement learning

This paper considers dual-rate systems, where the output is measured at a relatively slow rate while the control signal is adjusted at a faster rate. The output sampling time is an integer multiple of the input sampling time. The paper examines dual-rate inferential control systems, which consist of a fast model, a slow model, and a switch. Missing output samples are estimated using the fast single-rate model. The single-rate control algorithm is then implemented at the fast-sampling rate. The fast-sampling discrete-time model is derived from the plant’s continuous-time model using the first-order hold (FOH) element. A discrete LQ regulator is proposed for this plant model, with a prescribed degree of stability (all closed-loop eigenvalues are within the range 0 < λ < 1 in magnitude). The matrix gain is calculated offline, and an online method for calculating the regulator gain is provided. The regulator gain is calculated using policy iteration, specifically Hewer’s algorithm. Finally, it is demonstrated that the presented inferential control system remains effective in the presence of multiplicative unmodeled dynamics. The main contributions of the paper are: (i) Designing the LQ regulator with a prescribed degree of stability using reinforcement learning (RL) (generalized policy iteration); and (ii) Considering the robust stability of the inferential control system in the presence of multiplicative unmodeled dynamics using the lifting technique.

Optimisation of synchronisation control parameters for enhanced bilateral teleoperation system

Abstract Bilateral teleoperation systems encounter challenges in achieving synchronisation between master and slave robots due to communication time delays. This paper addresses the instability caused by these delays and proposes a solution through advanced control algorithms. Nonlinear optimisation algorithms might only sometimes deliver solutions in the allotted time, particularly when handling complicated, high-dimensional issues or when optimisation iterations are extensive. The study first develops a comprehensive mathematical model encompassing the dynamics and communication intricacies of both master and slave sides in teleoperation. By recognising the limitations of existing proportional-derivative controllers in compensating for communication errors, a sequential quadratic programming-proportional-integral-derivative (SQP-PID) controller is introduced. This controller accumulates and rectifies synchronisation delay errors, ensuring precise control without steady-state deviations. The proposed SQP-PID controller stands out for its ability to handle steady-state errors effectively, offering swift response and maintaining stability. Leveraging the SQP optimisation algorithm, it intelligently tunes the parameters, minimising synchronisation errors. The approach capitalises on the simplicity, performance, and robustness of the SQP-PID controller, providing a promising avenue for enhancing bilateral teleoperation systems’ accuracy and stability, maintaining initial discrepancy with a best fitness value of 0.98 % in varied operating conditions.

Power-Domain Interference Graph Estimation for Multi-hop BLE Networks

Traditional wisdom for network management allocates network resources separately for the measurement and communication tasks. Heavy measurement tasks may compete limited resources with communication tasks and significantly degrade overall network performance. It is therefore challenging for the interference graph, deemed as incurring heavy measurement overhead, to be used in practice in wireless networks. To address this challenge in wireless sensor networks, our core insight is to use power as a new dimension for interference graph estimation (IGE) such that IGE can be done simultaneously with the communication tasks using the same frequency-time resources. We propose to marry power-domain IGE with concurrent flooding to achieve simultaneous measurement and communication in BLE networks, where the power linearity prerequisite for power-domain IGE holds naturally true in concurrent flooding. With extensive experiments, we conclude the necessary conditions for the power linearity to hold and analyze several non-linearity issues of power related to hardware imperfections. We design and implement network protocols and power control algorithms for IGE in multi-hop BLE networks and conduct experiments to show that the marriage is mutually beneficial for both IGE and concurrent flooding. Furthermore, we demonstrate the potential of IGE in improving channel map convergence and convergecast in BLE networks.

Design of Neural Network-Based Multi-Fault Tolerant Control System for Unmanned Aerial Vehicles

Actuator failure seriously threatens the flight safety of unmanned helicopters. Considering the problem of multiple faults such as actuator bias and failure in unmanned helicopters, a composite fault-tolerant flight control algorithm is proposed. For actuator bias fault, a nonlinear fault observer is designed to estimate it in real time; for actuator failure fault, a same-dimensional auxiliary system is constructed and processed by neural network technology. The composite fault-tolerant flight controller of the unmanned helicopter is designed by backstepping method, and the Lyapunov stability theory is used to prove that the error signals of the closed-loop system are bounded and convergent. Simulation results show that the proposed control algorithm can improve the fault tolerance of the unmanned helicopter when multiple actuator faults occur, ensuring its safe flight.

A Comparative Study on Autonomous Vehicle Local Path Planning Through Model Predictive Control and Frenet Frame Method

<div>In recent years, autonomous vehicles (AVs) have been receiving increasing attention from investors, automakers, and academia due to the envisioned potentials of AVs in enhancing safety, reducing emissions, and improving comfort. The crucial task in AV development boils down to perception and navigation. The research is underway, in both academia and industry, to improve AV’s perception and navigation and reduce the underlying computation and costs. This article proposes a model predictive control (MPC)-based local path-planning method in the Cartesian framework to overcome the long computation time and lack of smoothness of the Frenet method. A new equation is proposed in the MPC cost function to improve the safety in path planning. In this regard, an AV is built based on a 2015 Nissan Leaf S by modifying the drive-by-wire function and installing environment perception sensors and computation units. The custom-made AV then collected data in Norman, Oklahoma, and assisted in the performance evaluation of the two control algorithms in this work. Both straight roads and curved roads are considered in the evaluation. For the purpose of saving costs and raising real-world implementation potential, the vision-only solution is applied in object detection and bird’s-eye-view coordinate data generation. MPC and Frenet coordinate system approaches are independently employed to generate a safe and smooth path for the AV using the collected data. The two methods are compared in terms of smoothness, safety, and computation time. Compared with the Frenet-based method, the proposed MPC method reduces the computation time by 80%, and the path smoothness is significantly improved.</div>

Research on constant tension yarn transfer control technology based on AGA-PID and FOC algorithm

To solve the problems of poor real-time tension tracking, large overshoot, long adjustment time, and large tension fluctuation error that exist in the current control system for yarn feeding in the circular weft knitting machine, this paper presents an optimized and improved adaptive genetic algorithm (AGA) for adjusting the PID (Proportional Integral Derivative) parameters of the tension control part of the yarn-conveying system (AGA-PID). The algorithm is combined with the field-oriented control technology of the permanent magnet synchronous motor to design a closed-loop control strategy for the yarn-conveying system of a circular weft knitting machine. The MATLAB-Simulink simulation model was used to analyze the external tension loop-based AGA-PID controller and permanent magnet synchronous motor dual closed-loop control strategy of the yarn transport control system. The experimental verification was conducted by constructing an experimental platform that simulates the real process of yarn transportation. The outcomes of system simulations and experiments have consistently demonstrated that the AGA-PID control algorithm outperforms the GA-PID and PID control algorithms in terms of overshooting, response speed, stability, and anti-jamming ability. The yarn transport control system, which is designed to operate based on an AGA-PID control algorithm, is capable of reducing the fluctuations in tension that occur during the transportation of yarn and exhibits enhanced control accuracy, effectively stabilizing yarn tension control during the conveying process. The application of this algorithm can enhance the overall elasticity uniformity and surface flatness of knitted fabrics, thereby facilitating the realization of adjustable and controllable local elasticity in fabrics.

Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm

UAV technology is advancing rapidly, and variable-sweep wing UAVs are increasingly valuable because they can adapt to different flight conditions. However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. To address these challenges, this study presents an innovative framework that integrates dynamic modeling with morphing control, optimized by the multi-agent deep deterministic policy gradient for two-sweep control (MADDPG–VSC) algorithm. This approach enables real-time sweep angle adjustments based on current flight states, significantly enhancing aerodynamic efficiency and overall UAV performance. The precise motion state model for wing morphing developed in this study underpins the MADDPG–VSC algorithm’s implementation. The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. Extensive simulations and real-world experiments consistently demonstrate that the proposed algorithm outperforms contemporary methods in multiple aspects, underscoring its practical applicability in complex aerial systems. This study advances control technologies for morphing-wing UAV formation and offers new insights into multi-agent cooperative control, with substantial potential for real-world applications.

Model-predictive optimal control of ferrofluidic microrobots in three-dimensional space

Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. This paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.

Design, and dynamic evaluation of a novel photovoltaic pumping system emulation with DS1104 hardware setup: Towards innovative in green energy systems.

Diesel engines (DEs) commonly power pumps used in agricultural and grassland irrigation. However, relying on unpredictable and costly fuel sources for DEs pose's challenges related to availability, reliability, maintenance, and lifespan. Addressing these environmental concerns, this study introduces an emulation approach for photovoltaic (PV) water pumping (WP) systems. Emulation offers a promising alternative due to financial constraints, spatial limitations, and climate dependency in full-scale systems. The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. Precise induction motor control is achieved through a controlled inverter. This work innovatively combines PV and WP emulation while optimizing system dynamics, aiming to develop a comprehensive emulator and evaluate an enhanced control algorithm. An optimized scalar control strategy regulates the water MP, demonstrated through MATLAB/Simulink simulations that highlight superior performance and responsiveness to solar irradiation variations compared to conventional MPPT techniques. Experimental validation using the dSPACE control desk DS1104 confirms the emulator's ability to faithfully reproduce genuine solar panel characteristics.

Leg prosthesises with active damping

The article considers various types of leg prosthesises with active damping based on pneumatic, hydraulic and electric drives. For each type of drive used, its advantages and disadvantages, features of design solutions were described with the aim of further selecting a prototype from existing knee modules of leading manufacturers to develop a domestic solution with characteristics and functionality not inferior to the best world samples. The article considers not only various design and circuit solutions, but also pays attention to the analysis of the applied control algorithms of leg prosthesises, allowing to implement various convenient options, for example, protection against tripping, ascent and descent on uneven surfaces, energy saving and energy recovery systems.

A self-adaptive fractional-order PID controller for the particle velocity regulation in a pneumatic conveying system

This paper presents the methodical development of a state-error-driven self-adaptive fractional-order proportional–integral–derivative (AFOPID) control algorithm to efficiently regulate the velocity of pneumatically conveyed particles at the desired set point and to prevent the blockage of particles in a pipe due to an imbalanced combination of their velocity and corresponding mass flow rate. The proposed fractional control law is constituted by adaptively modulating fractional orders of the integral and differential operators in the control based on the state-error variations in the velocity of solid particles. The particle’s velocity is measured and updated via electrostatic sensors in conjunction with cross-correlation signal processing algorithms. All the other fixed hyper-parameters associated with the proportional–integral–derivative (PID) control scheme are meta-heuristically optimized by using a genetic algorithm. The proposed AFOPID is benchmarked against conventional integer-order PID and the fractional-order proportional–integral–derivative (FOPID) controllers. Experiments are performed on a laboratory-scale test rig to comparatively analyze the aforesaid control schemes, where each controller is examined for three velocity set points and three disturbance levels. The experimental results validate the superior time optimality and robustness of the proposed AFOPID controller against bounded disturbances and abrupt velocity set-point variations by manifesting relatively faster settling time, low overshoots (and undershoots), and smaller steady-state fluctuations.