High Control Accuracy Research Articles

Research on a New Topology and Reactive Power Compensation Application of Reinjection Multilevel Voltage Source Converter

Based on the principles of reinjection converter technology and submodule operation, a novel topology for a reinjection multilevel voltage source converter (RMVSC) is proposed. In this topology, submodules are dynamically connected in series as the reinjection circuit. This new topology retains all the functions of the RMVSC while offering flexible control of the reinjection circuit and ease of modularization, making it highly suitable for high-voltage, high-power applications. A circulating drive pulse sequence was developed for the 7-level serial submodule RMVSC, ensuring dynamic stability of the submodule voltage and maintaining low harmonic distortion when interfaced with the grid. Building on this, a double-loop control strategy—comprising an outer power loop and an inner current loop—was proposed for the static synchronous compensator (STATCOM) of RMVSC. A simulation model was constructed in PSCAD/EMTDC, and the simulation results confirm the excellent performance of the proposed topology and the effectiveness of both the modulation strategy and the double-loop control strategy. The RMVSC-STATCOM demonstrates continuous and bidirectional reactive power regulation, with high control accuracy and superior compensation current quality.

Longitudinal SCAS design and simulation for civil aircraft simulator

Abstract In this paper, the principle and benefits of stability and control augmentation system (SCAS) are introduced. Four schemes of the longitudinal SCAS for civil aircraft in normal mode are analyzed and compared. One of them, i.e., C * response type, is selected. Its architecture is determined. Then, based on a typical civil aircraft, the flight control law of the longitudinal SCAS is designed. A simulation model of the longitudinal SCAS is established through MATLAB software. Finally, the simulation and verification of the longitudinal SCAS model is carried out. The simulation results show that the designed longitudinal SCAS is feasible and can be applied to engineering practice. Compared with either normal load or pitch rate command response type, with proportional and integral feedback for mixed control signals of pitch rate and normal load, C * not only improves the short period characteristics but also effectively separates phugoid and short period frequencies, thus achieving high control accuracy.

Application of derivative and integral terminal sliding modes in leader-follower type systems"

Subject matter: The study focuses on the control methods for dr20 type robot swarms, specifically on the derivative and integral terminal sliding mode control combined with a nonlinear disturbance observer. The problem of effective swarm control is highly relevant in the current conditions of automation and robotics, especially in the context of performing complex tasks in limited space and in the presence of disturbances. Goal: The development and analysis of a simulation model for the movement of a robot swarm using advanced control methods to ensure system accuracy and stability. The research aims to improve the control methods for robot swarms, enhancing their efficiency and reliability in various operational conditions. Tasks: 1) Develop a simulation model of a robot swarm in the CoppeliaSimEDU environment, considering all necessary parameters for modeling real operating conditions. 2) Implement control algorithms for the leader and followers to maintain the swarm structure and avoid collisions. 3) Conduct a series of experiments to test the effectiveness of the proposed methods, analyzing the results in terms of stability and control accuracy. Methods: Modeling in CoppeliaSimEDU, implementing control algorithms based on derivative and integral terminal sliding mode control, applying a nonlinear disturbance observer to improve system stability. The applied methods allow for the consideration of various disturbances and ensure high control accuracy. Results: he proposed control model allows achieving high following accuracy and collision avoidance even in complex conditions. Experiments have shown that the control methods ensure the stability and accuracy of the robot swarm's movement, reducing the response time to external disturbances. The research results demonstrate that the use of derivative and integral terminal sliding mode control combined with a nonlinear disturbance observer significantly enhances the efficiency of multi-robot systems. Conclusions: The use of advanced control methods significantly improves the efficiency of multi-robot systems, ensuring their reliability and accuracy in real operating conditions. The proposed methods can be applied in various fields where the coordination of a large number of robots is required, including logistics, rescue operations, and environmental monitoring.

PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace

Improving the accuracy of the temperature field prediction model for continuous annealing line strips and enhancing the model’s adaptability to full-size strips are key technical challenges in continuous annealing lines. This paper developed a continuous annealing temperature prediction model based on a variable step-size strategy for the heating section, even-heat section, slow-cooling section, and fast-cooling section of the continuous annealing line. To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. Additionally, due to the limited production of certain strip gauges, providing insufficient data for optimization, this study introduces a combined file approach to address gauge vacancies. The experimental results indicate that the optimized model with variable step size can control the absolute prediction error to less than 4 °C, improving prediction accuracy by 61.9% and prediction speed by 26.8% compared to the traditional equal-step prediction model. This study verified that the merger method is effective for addressing side gauge vacancies, while the proposed method is suitable for resolving middle gauge vacancies. The main technical contribution of this study is the establishment of a high-precision prediction model for continuous annealing temperature of variable step length strips, ensuring high temperature control accuracy for full-gauge strips when passing through the continuous annealing production line.

Bending/force switching control of pneumatic soft actuator based on multi-sensor fusion

Abstract The soft actuator has unlimited freedom, and can realize the grasping of irregular or fragile objects. A pneumatic soft actuator is designed in this paper, which consists of a series of symmetrically distributed pneumatic chambers. The soft actuator consists of a top expansion layer composed of 20 chambers and a bottom restraint layer. The Yeoh model and the finite element method (FEM) were employed to analyze the bending of the soft actuator. To improve the tracking performance, a PID strategy of the endocrine system (ES-PID) based on the neuroendocrine thyroid hormone regulation mechanism was constructed to tune the bending deformation. To achieve steady and precise force control, a bending/force switching control algorithm based on multi-sensor fusion is designed to realize high control accuracy. The bending/force switching control strategy mainly consists of two feedback loops for bending control, a force control, and a switching control. In the experimental process, the bending control is the primary control, and the force control is the primary control when the switching condition is satisfied. In order to prevent the occurrence of a false switching action, a switching factor is constructed using the method of successive comparison of neighboring values. Finally, several experiments are carried out to verify the effectiveness and feasibility of the control algorithm.

Analysis of Optimization Algorithms Used in Permanent Magnet Synchronous Motor Control According to Different Performance Indices

Permanent magnet synchronous motors (PMSM) can be produced at lower costs with new developments in magnet technology and are widely used in many industrial areas due to their low energy consumption. The widespread use of PMSM brings with it the requirement for high accuracy control performance. In order to achieve high performance accuracy, vector control technique is generally preferred. However, the parameter values of the controllers used in this technique are very important for motor performance. Tuning these parameters by optimization techniques instead of classical methods has become a popular topic. Nowadays, modern control methods show more effective control behavior than classical control methods, which has made the use of modern control methods widespread and studies on modern control methods have intensified. In this study, the analysis of different optimization algorithms used in the control of an PMSM according to their performance indices is investigated in Matlab-Simulink environment. As a result of simulation with different optimization algorithms under the same conditions, the analysis of optimization algorithms using different performance indices such as integral of the absolute value of the error (IAE), integral of the square of the error (ISE), Integral of the Square of the Error Multiplied by Time (ITSE), and Integral of the Absolute Error Multiplied by Time (ITAE) is carried out.

The non-destructive control of mechanical properties of ceramics on the silicon nitride and silicon carbide based by acoustic emission method

The acoustic emission (AE) in reaction bonded SiC and Si3N4 ceramics and hot pressed Si3N4+TiN composites were investigated during the bending tests. A clear correlation between some AE parameters and specimen strength was established for all materials. Two methods for non-destructive control of materials strength have been proposed. High accuracy of control (error not more than 3%) in comparison with traditional estimation by sample average (error of 15% and higher) was observed.

Fixed-time second-order sliding mode output feedback trajectory tracking control for an autonomous surface vehicle with model uncertainties and prescribed performance

This paper investigates the fixed-time trajectory tracking control problem of autonomous surface vehicle system with asymmetric performance constraints. The external disturbances, model uncertainties and the difficulty of velocity measurement are all taken into account in the controller design. Asymmetric performance constraints are firstly studied to improve the transient and steady-state performance of the tracking error system. Next, a fixed-time extended state observer is developed to estimate the unknown velocity and external disturbances. Both the fixed-time first-order and the second-order PID-type sliding surfaces are designed. Further, two continuous output feedback controllers are built to achieve high control accuracy and low energy consumption and to achieve fixed-time stability of the closed-loop system. Finally, abundant simulations for CyberShip II demonstrate the effectiveness of the proposed algorithms.

Research on an improved deadbeat model predictive current control of double three-phase open-winding permanent magnet motor

Model predictive current control has high control accuracy and good control performance, and has been widely applied in the control strategy of dual three-phase permanent magnet synchronous motors. This article proposes an improved deadbeat model predictive current control (DMPCC) method by analyzing the mathematical model of a dual three-phase open-winding permanent magnet synchronous motor. One inverter predicts and calculates the optimal voltage vector through the deadbeat model, while the other inverter calculates the optimal voltage vector through the value function rolling optimization. This method reduces the computational workload of the controller, avoids the delay problem of the traditional model predictive current control (MPCC) method, improves the system's response speed, effectively suppresses the current in the harmonic sub-plane, and reduces torque ripple. The simulation results have verified the accuracy and speed of the above control method.

Direct Torque Control Space Vector Modulation for Induction Motor Driven by Matrix Converter

This study proposes enhancing induction motor (IM) drive systems by developing a DTC-MC with SVPWM integration to reduce ripple. DTC-MC is effective for precise torque control in AC drives, offering high control accuracy by isolating stator flux and torque. The method excels in sensor-less speed control, maintaining unity input power factor at low speeds, and constant switching frequency for rapid torque adjustments. Combining DTC-MC with SVPWM improves simplicity, dynamic behavior, and torque response. The DTC-SVM approach further refines the torque response by correcting flux and torque discrepancies. MATLAB/SIMULINK simulations validated the approach, showing a robust dynamic response and significantly reduced motor torque ripple and control of speed.

Microstructure formation mechanisms and property regulation methods during ceramic additive manufacturing

Anisotropic surface lamellar defects, dimensional shrinkage, and bending strength are important scientific issues that constrain vat photopolymerization additive manufacturing of biological, electronic, and core ceramics with high precision and complex structures. Through the single-factor experiment of photoinitiator concentration, average particle size, volume fraction, and refractive index constant in ceramic-resin slurry, this work studies the photopolymerization characteristics and profile curves, surface lamellar defects, and crosslinking density, verifies the reliability of the photopolymerization profile equations affected by the slurry composition, and acquires the anisotropic property formation mechanisms and control methods. The single-point scanning photopolymerization profile shape is parabolic, resulting in x-, y-, and z-direction lamellar defects before and after sintering (1550 °C), with no obvious weakening. After the quantitative description of the lamellar defects, the average surface roughnesses before and after sintering of the green body are 6.82 and 4.92 μm, respectively. Additionally, the laser energy attenuation along the penetration direction induces a gradient distribution of crosslinking density in the photopolymerization profile region. The crosslinking density gradient distribution of the decay from the slurry surface toward the depth direction is influenced by the slurry component concentration, leading to a gradient of cured powder concentration, which induces anisotropic behaviors. Through the crosslinking density controlling, the average bending strength in x(y) and z directions is increased by 63.19 % (46 %), the difference in anisotropic strength is reduced by 22.55 %, and the dimensional shrinkage difference of x and z directions is reduced by 48.32 %. This provides a theoretical and experimental research basis for ceramic vat photopolymerization with high dimensional accuracy, excellent performance, and morphology control.

Research on the Optimization of the PID Control Method for an EOD Robotic Manipulator Using the PSO Algorithm for BP Neural Networks

Large-scale explosive ordnance disposal (EOD) robotic manipulators can replace manual EOD tasks, offering higher efficiency and better safety. This study focuses on the control strategies and response speeds of EOD robotic manipulators. Using Adams to establish the dynamic model of an EOD robotic manipulator and constructing a hydraulic system model in AMEsim, a co-simulation model is integrated. This study proposes a PID control strategy optimized by the particle swarm optimization (PSO) algorithm for a backpropagation (BP) neural network and simulates the system’s step response for analysis. To address the vibration issues arising during the manipulator’s motion, B-spline curves are used for trajectory optimization to reduce vibrations. The PSO algorithm optimizes the connection weight matrix of the BP neural network, solving the potential problem of local minima during the training process of the BP neural network, thereby enhancing the global search capability, learning efficiency, and network performance. Simulation results indicate that compared to traditional BP+PID control, genetic algorithm (GA)+PID control, and whale optimization algorithm (WOA)-BP+PID control, the PSO-BP+PID algorithm control rapidly tunes the PID control parameters Kp, Ki, and Kd. Under the same step function conditions, the overshoot is only 1.37%, significantly lower than other methods, and the settling time is only 14 s. After stabilization, there is almost no error, demonstrating faster response speed, higher control accuracy, and stronger robustness. This research has theoretical value and reference significance for the control methods and improvements in EOD robotic manipulators.

Research on trajectory tracking control method for agricultural robot permanent magnet synchronous motor servo system based on improved EMD threshold wavelet filtering

In agricultural robots, trajectory tracking can be affected by disturbances such as road slopes or bumps, leading to sudden changes in path curvature and reduced control accuracy. To address this issue, we propose a servo motor drive control method based on an improved Empirical Mode Decomposition (EMD) threshold. A mathematical model integrating the drive and control of the servo motor is established, and the driving performance of the servo motor is analyzed. By detecting the speed and position of the servo motor, the specified three-phase current values for motor drive control are derived. The actual current of the motor is collected using Hall current sensors and denoised with an improved EMD threshold wavelet filtering method. Within the servo motor drive control model, the system calculates the difference between the given three-phase current values and the actual motor current, and this difference is then used to adjust the motor control via an input linear amplifier, ensuring integrated drive control. Simulation and experimental results show that the proposed trajectory tracking control method for the agricultural robot’s permanent magnet synchronous motor servo system exhibits high control accuracy, strong anti-interference ability, and good performance, making it more suitable for practical applications.

Towards Transparent Control Systems: The Role of Explainable AI in Iterative Learning Control

This paper presents a novel approach to improving the performance and interpretability of Iterative Learning Control (ILC) systems through the integration of Explainable Artificial Intelligence (XAI) techniques. ILC is a powerful method used across various domains, including robotics, process control, and traffic management, where it iteratively refines control inputs based on past performance to minimize errors in system output. However, traditional ILC methods often operate as "black boxes," making it difficult for users to understand the decision-making process. To address this challenge, we incorporate XAI, specifically SHapley Additive exPlanations (SHAP), into the ILC framework to provide transparent and interpretable insights into the algorithm's behavior. The study begins by detailing the evolution of ILC, highlighting key advancements such as predictive optimal control and adaptive schemes, and then transitions into the methodology for integrating XAI into ILC. The integrated system was evaluated through extensive simulations, focusing on robotic arm trajectory tracking and traffic flow management scenarios. Results indicate that the XAI-enhanced ILC not only achieved rapid convergence and high control accuracy but also maintained robustness in the face of external disturbances. SHAP analyses revealed that parameters such as the proportional gain (Kp) and derivative gain (Kd) were critical in driving system performance, with detailed visualizations providing actionable insights for system refinement. A crucial metric for control precision was the root mean square error (RMSE), which was reduced to as low as 0.02 radians in the robotic arm case, indicating extremely precise tracking of the intended route. Similarly, the ILC algorithm effectively maintained the ideal traffic density within the predetermined bounds in the traffic management scenario, resulting in a 40% reduction in congestion compared to baseline control measures. The resilience of the ILC algorithm was also examined by introducing changes to the system model, external disturbances, and sensor noise. The algorithm demonstrated a high degree of stability and accuracy in the face of these disruptions. For instance, in the robotic arm case, adding noise to the sensor readings had a negligible effect on the algorithm's performance, increasing the RMSE by less than 5%. This integration of XAI into ILC addresses a significant gap in control system design by offering both high performance and transparency, particularly in safety critical applications. The findings suggest that future research could further enhance this approach by exploring additional XAI techniques and applying the integrated system to more complex, real-world scenarios.

PARALLEL PLATFORM CONTROLLER BASED ON ADAPTIVE DIFFERENCE ALGORITHM – PART 2

There are two main approaches to motion control on parallel platforms: joint space control and workspace control. Joint space control is an easy-to-implement semi-closed-loop strategy, but its control effect could be better. The workspace control is to obtain the real-time position of the parallel platform through the forward solution and close the speed and position loop of the parallel platform in the workspace. This paper uses a Model Predictive Controller (MPC) to control the parallel platform with workspace control as the research goal. The loss function is constructed based on the swarm intelligence optimization idea, and the adaptive difference algorithm is used to optimize the parameters of MPC. This part uses MATLAB to perform simulation experiments to complete the S-shaped velocity trajectory planning algorithm. In addition, the control effect of MPC and position-loop PI controller in a robust disturbance environment is compared. Experiments show that MPC has the advantages of low energy consumption and high control accuracy.

A compact compliant robot for the grinding of spherical workpieces with high force control accuracy

A robotic grinding system requires a force-controlled grinding module to provide a consistent surface roughness and a robot arm to position the grinding module to reach a wide range of surface area on a workpiece. Existing pneumatic grinding modules are heavy and bulky and cannot provide very accurate force control. Articulated 6-axis robot arms are often used for positioning the grinding module, but they require a large accommodation space and have limited access to the surface of a spherical workpiece. This paper proposes a compact 3-axis grinding robot with no grinding surface limitations on spherical workpieces. The robot employs torque-controlled actuators so that a human operator can easily teach grinding paths to the robot. The proposed grinding module uses series elasticity to generate very low reflected inertia and friction. Hence, accurate grinding force control can be achieved. The grinding module also has a small size and low noise. Experimental results verify the high accuracy of grinding force control when compared with existing counterparts. Through an illustration of removing the parting line of a helmet hardshell, the grinding robot can effectively reduce the surface roughness of workpieces that are sensitive to the grinding force. It is expected that the proposed robot can be easily reconfigured to grind workpieces of different geometries.

Distributed state constraint containment control of multi-AUV formation with unknown control direction

The control problem of multi-autonomous underwater vehicle (multi-AUV) formation systems is a hot issue in current research. The research goal is to make the formation have a more comprehensive ability to deal with emergencies under higher control accuracy. In this paper, input saturation, disturbances, and system uncertainties are considered. To achieve the convergence of system containment errors, a state constraint containment control method is proposed for multi-AUV systems with unknown control direction. Combined with unknown control direction, new auxiliary variables are constructed to compensate the input saturation. On this basis, the follower reference trajectories and errors are defined. To constrain some system states, the tan-type barrier Lyapunov function (TBLF) is proposed. The estimated values of system uncertainties and disturbances can be obtained by neural network. Compared to other works, the amount of data generated by neural network is further reduced by the optimized adaptive law in this study. The unknown control direction problem is handled based on Nussbaum function, and its limitation range is expanded. The designed control method ensures the stable operation of formation systems. The containment errors can converge to a small neighborhood of zero. By comparing the numerical simulation results with other work, the advantages of the control algorithm proposed in this paper has been verified.

An Advanced Control Method for Aircraft Carrier Landing of UAV Based on CAPF–NMPC

This paper investigates a carrier landing controller for unmanned aerial vehicles (UAVs), and a nonlinear model predictive control (NMPC) approach is proposed considering a precise motion control required under dynamic landing platform and environment disturbances. The NMPC controller adopts constraint aware particle filtering (CAPF) to predict deck positions for disturbance compensation and to solve the nonlinear optimization problem, based on a model establishment of carrier motion and wind field. CAPF leverages Monte Carlo sampling to optimally estimate control variables for improved optimization, while utilizing constraint barrier functions to keep particles within a feasible domain. The controller considers constraints such as fuel optimization, control saturation, and flight safety to achieve trajectory control. The advanced control method enhances the solution, estimating optimal control sequences of UAV and forecasting deck positions within a moving visual field, with effective trajectory tracing and higher control accuracy than traditional methods, while significantly reducing single-step computation time. The simulation is carried out using UAV “Silver Fox”, considering several scenarios of different wind scales compared with traditional CAPF–NMPC and the nlmpc method. The results show that the proposed NMPC approach can effectively reduce control chattering, with a landing error in rough marine environments of around 0.08 m, and demonstrate improvements in trajectory tracking capability, constraint performance and computational efficiency.

An immune optimization deep reinforcement learning control method used for magnetorheological elastomer vibration absorber

On-board precision equipment installed in vehicles is susceptible to vibrations caused by irregularities on the road surface, which may decrease accuracy and damage to components. Installing a vibration absorption system in the vehicle is necessary to mitigate the impact of vehicle vibrations on the equipment. Vibration systems widely use vibration absorbers based on magnetorheological elastomer (MRE). Previous research has mainly used Proportional-Integral-Derivative (PID), fuzzy control, and ON-OFF algorithms to control the vibration absorption system. However, these algorithms exhibit poor control accuracy and adaptability in uncertain environments. To enhance the adaptive capabilities of vibration absorbers in time-varying environments and to maximize their performance, this study explores the use of the Twin Delayed Deep Deterministic Policy Gradient (TD3) control algorithm. Additionally, we proposed an Immune Optimization Twin Delayed Deep Deterministic Policy Gradient (IO-TD3) control algorithm to address the low-efficiency issue in the initial training stages. Simulation results show that, compared to the TD3 algorithm, Double Q-Deep Deterministic Policy Gradient (Double Q-DDPG) algorithm, Deep Deterministic Policy Gradient (DDPG) algorithm, and Deep Q-Network (DQN) algorithm, the IO-TD3 algorithm significantly exhibits faster convergence speed and improved stability. Finally, we apply the IO-TD3 algorithm to the MRE absorber controller and verify the controller's performance using the Monte Carlo method. The results show that the controller using the IO-TD3 control algorithm has high control accuracy and stability.

Automation of control of geometrical characteristics of worm and bevel gears by means of coordinate measuring

The article contains the results of research on the automation of control of the geometric characteristics of gears. The use of coordinate measuring machines can significantly improve the productivity and accuracy of measurements. However, their use for testing gears requires the use of special programs for carrying out and processing the results of measurements of products with complex surface shapes. The use of software for metrological control of the geometric characteristics of worm and bevel gears makes it possible to achieve high accuracy of control and measurement work. To automate the control of geometric characteristics of worm and bevel gears, an additional module has been created for the standard program. With its help, all points of the measured curved surface of gears, obtained by contact method according to a standard measurement program, are structured into a single data array with a measurement protocol. Based on these data, the module generates a profile of the measured surface of a wheel tooth and builds a geometric contour of the profile of the measured tooth. The result of the module's operation is the formation of a general profile of the entire gear and its comparison with the original (theoretical) profile of the gear as a whole. The control process itself is carried out in a short time interval, which makes it possible to use the proposed approach to automating the control of gear profiles in small-scale production.