High Steady-state Accuracy Research Articles

Self-learning for translational control of elliptical orbit spacecraft formations

PurposeThis paper aims to investigate the relative translational control for multiple spacecraft formation flying. This paper proposes an engineering-friendly, structurally simple, fast and model-free control algorithm.Design/methodology/approachThis paper proposes a tanh-type self-learning control (SLC) approach with variable learning intensity (VLI) to guarantee global convergence of the tracking error. This control algorithm utilizes the controller's previous control information in addition to the current system state information and avoids complicating the control structure.FindingsThe proposed approach is model-free and can obtain the control law without accurate modeling of the spacecraft formation dynamics. The tanh function can tune the magnitude of the learning intensity to reduce the control saturation behavior when the tracking error is large.Practical implicationsThis algorithm is model-free, robust to perturbations such as disturbances and system uncertainties, and has a simple structure that is very conducive to engineering applications.Originality/valueThis paper verified the control performance of the proposed algorithm for spacecraft formation in the presence of disturbances by simulation and achieved high steady-state accuracy and response speed over comparisons.

Research on the characteristics of electro-hydraulic position servo system of RBF neural network under fuzzy rules

A radial basis function neural network PID controller under fuzzy rules (FUZZY-RBF-PID) was designed for the electro-hydraulic position servo system under the influence of uncertain factors such as load mutation, and load stiffness change. Firstly, the mathematical model of the system is established, and the frequency domain and time domain analysis of the system are carried out. Secondly, based on the analysis results, a radial basis function (RBF) neural network PID controller is designed, and fuzzy rules are innovatively used to adjust the learning rate of PID parameters in the RBF neural network learning algorithm in real time. Thirdly, the simulation results show that under the action of the FUZZY-RBF-PID controller, the unit step response of the system has high steady-state accuracy, fast response speed, and under the condition of large load stiffness, the system can recover to the steady-state value faster after being disturbed. At the same time, when the input signal is the sinusoidal signal of 10 HZ, the system under the action of the FUZZY-RBF-PID controller has no obvious phase lag phenomenon, and the tracking error is minimal. The proposed method can effectively improve the comprehensive performance of the electro-hydraulic position servo system under the influence of uncertain factors.

A New Sliding-Mode Observer-Based Deadbeat Predictive Current Control Method for Permanent Magnet Motor Drive

This article proposes a new deadbeat predictive current control (DPCC) method based on a sliding-mode observer (SMO), which is applied in the field of permanent magnet motor control. A novel DPCC control method based on SMO is proposed according to the inherent issues of DPCC, which can effectively suppress internal parameter mismatch disturbances and external disturbances in the current loop. The mathematical model and derivation process of the proposed method are introduced. A simulation model is built and the effectiveness of the proposed method is verified. An experimental platform is built and the superiority of the proposed method is verified based on comparative experiments. Experimental results show that the proposed algorithm has strong robustness to the motor parameter mismatch. Compared with extended state observer (ESO) and adaptive observer (AO), the proposed algorithm has faster response speed and higher steady-state accuracy.

Designing and Application of Modified SCSO-Based LADRC Controller for Dicing Saw Chuck Table Systems

In this paper, the linear active disturbance rejection control (LADRC) is applied to the position control of a dicing saw chuck table, which is used to improve the steady-state accuracy and anti-interference ability of the control system. Aiming at the problem that LADRC parameters are difficult to adjust, a parameter adjustment method based on an improved sand cat swarm algorithm is proposed. The nonlinear convergence factor strategy and multiple chaotic map optimization strategy are used to improve the algorithm. In order to verify the performance of the proposed method, the improved sand cat swarm optimization algorithm is compared with the standard sand cat swarm optimization algorithm, grey wolf optimization algorithm and salp swarm optimization algorithm. The final experimental results show that using the improved sand cat swarm optimization algorithm to optimize the LADRC controller has higher steady-state accuracy, stronger anti-interference ability, and better optimization results.

Design and optimization of high-precision servo control system in precision positioning technology

Abstract High-precision control technology, as a general technology in the field of manufacturing and assembly, is widely used in high-end manufacturing equipment such as chip packaging equipment and high-speed CNC machining centers. In this thesis, a permanent magnet synchronous motor is used as the actuator motor of a high-precision servo control system, and vector control strategy and sliding mode observer are adopted to study and realize the servo control of permanent magnet synchronous motor based on sliding mode observer. The current, speed, and position simulation experiments of the high-precision servo control method are also carried out to compare the positioning accuracy before and after the use of the servo control method and to study the performance of the method in precision positioning. The experiments prove the correctness and effectiveness of the SMO-based PMSM high-precision servo control method and the current response speed of the servo control in this paper is fast, and the speed and position estimation errors are maintained within 1.6 r/min and 0.15 rad in steady state condition, and within 20 r/min and 20 rad in the sudden-change condition, which is able to contribute to 11.91% and 19.30% of the machine’s X-axis and Y-axis positioning accuracy improvement. The SMO-based PMSM high-precision servo control method has high steady-state accuracy and immunity to disturbances and can be used for the precision positioning of machine systems.

Comparative study between PI, FLC, SMC and Fuzzy sliding mode controllers of DFIG wind turbine

In speed control, double-fed-induction generator (DFIG) has attracted attention in wind energy conversion systems (WECs). These systems can offer higher performances by controlling converters that connect the generator windings to the grid network. Therefore, different control strategies have been used. We can find the proportional integral (PI) and sliding mode control (SMC) which offer better performances. However, PI has constant gains that can’t change with the external variation and SMC has a chattering problem. Therefore, a hybrid control system that combines fuzzy logic control (FLC) and SMC to perform fuzzy sliding mode control (FSMC) is proposed. The hybrid system can improve the FLC and SMC robustness. The DFIG modeling and each of the control strategies have been detailed. To demonstrate the effectiveness of the control strategies, a comparative study of different control strategies (PI, FLC, SMC and FSMC) is described and performed using MATLAB/Simulink software. The results obtained from the present study show that FSMC is more robust and efficient than the other controllers (PI, FLC and SMC). It has high performances (low settling time, high steady state accuracy) assuring a perfect power decoupling with minimal ripples and errors.

Analysis of thermo-mechanical coupling characteristics of feed system of woodworking center based on fuzzy control algorithm

With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent.

A speed loop control method of a laser tracing measurement control system

In a laser tracking control system, a motor drives the load to achieve continuous forward and reverse rotation speed control, and the magnitude of the load changes periodically during the motor rotation to realize tracking. Based on these characteristics, we propose an integral-separated improved variable universe fuzzy proportional-integral (PI) control method with torque feedforward compensation method in this study. The proposed method achieves the control performances of fast response, high steady-state accuracy, and strong anti-interference. The conventional fuzzy PI control method achieves good performance only in the unidirectional acceleration of a motor; therefore, we have improved its fuzzy rules, and designed a novel universe contraction–expansion factor. Additionally, a load torque observer is introduced to observe and compensate for the external load and to further improve the anti-interference performance of the tracking control system. The experimental results demonstrate that the integral-separated improved variable-universe fuzzy PI control method achieves better performance in the forward and reverse speed regulation processes of the motor. The overshoot is 92% less than that of the conventional PI control when the target speed is set to ±500 rpm. The Luenberger observer is introduced to observe and compensate for the load torque. After the stabilizing the speed and applying an external load of 0.2 N m to the motor, the maximum speed reduction is observed to be 59% less than that obtained using the conventional PI control. The improved PI control method satisfies the speed loop control requirements of the laser tracking control system and can improve the steady-state tracking accuracy and robustness of the system.

State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network

State of charge (SOC) estimation is the core algorithm of the battery management system. However, the commonly used model-based, data-driven, or experiment-based methods struggle to independently achieve accurate SOC estimation under different working conditions and temperatures, which affects battery performance and safety. To this end, this paper proposes an online SOC estimation method that combines the model-driven and double-data-driven approaches. The unscented Kalman filter (UKF) based on the first-order RC model is used to achieve robust SOC estimation, while the data-driven long short-term memory (LSTM) neural network is used to achieve fast SOC estimation. The former model has an excellent dynamic performance and the latter has high steady-state accuracy. The SOC estimation results are input into the SOC estimation model of series LSTM so that the stable but inaccurate SOC values estimated by UKF in the first part and the accurate but fluctuating SOC values estimated by LSTM can be correlated and corrected, achieving a fast and accurate SOC estimation under various working conditions. The estimation results show that the above method has strong robustness and high accuracy, and effectively reduces model complexity and data redundancy. In addition, the root mean square error of SOC estimation under different working conditions is controlled within 1–2.3% at 0 °C, 25 °C, and 45 °C, which is better than the traditional single-SOC estimation method.

Reinforcement Learning Based Sliding Mode Control for a Hybrid-STATCOM

The hybrid static synchronous compensator (hybrid-STATCOM) is characterized with a wide compensation range and low dc-link voltage, which is a cost-effective reactive power compensator for medium voltage level application. However, the coupling part of the hybrid-STATCOM is time-varying, and its system model is nonlinear, which causes a great challenge for the controller design. This paper proposes a model-free reinforcement learning (RL) based sliding mode control (RL-SMC), which provides the inverter voltage as the control action of the hybrid-STATCOM to compensate the load reactive power and harmonic. The proposed RL-SMC is computationally efficient with high steady-state accuracy, fast response, and good robustness. First, an agent-environment framework (AEF) is proposed to enable RL. Then, the comprehensive design procedure of the RL-SMC is proposed. Finally, simulation and experimental results are carried out to verify the validity and effectiveness of the proposed RL-SMC under different load and grid conditions.

Hierarchical fusion detection algorithm for sleep spindle detection.

Sleep spindles are a vital sign implying that human beings have entered the second stage of sleep. In addition, they can effectively reflect a person's learning and memory ability, and clinical research has shown that their quantity and density are crucial markers of brain function. The "gold standard" of spindle detection is based on expert experience; however, the detection cost is high, and the detection time is long. Additionally, the accuracy of detection is influenced by subjectivity. To improve detection accuracy and speed, reduce the cost, and improve efficiency, this paper proposes a layered spindle detection algorithm. The first layer used the Morlet wavelet and RMS method to detect spindles, and the second layer employed an improved k-means algorithm to improve spindle detection efficiency. The fusion algorithm was compared with other spindle detection algorithms to prove its effectiveness. The hierarchical fusion spindle detection algorithm showed good performance stability, and the fluctuation range of detection accuracy was minimal. The average value of precision was 91.6%, at least five percentage points higher than other methods. The average value of recall could reach 89.1%, and the average value of specificity was close to 95%. The mean values of accuracy and F1-score in the subject sample data were 90.4 and 90.3%, respectively. Compared with other methods, the method proposed in this paper achieved significant improvement in terms of precision, recall, specificity, accuracy, and F1-score. A spindle detection method with high steady-state accuracy and fast detection speed is proposed, which combines the Morlet wavelet with window RMS and an improved k-means algorithm. This method provides a powerful tool for the automatic detection of spindles and improves the efficiency of spindle detection. Through simulation experiments, the sampled data were analyzed and verified to prove the feasibility and effectiveness of this method.

Research on Control Strategy of Solid State Transformer Based on Improved MPC Method

As the core equipment of smart grid, solid-state transformer (SST) needs to have good power quality regulation capability under grid side faults and load side faults. To ensure that the high-voltage side converter works at unity power factor and smooth DC voltage. At the same time, the three-phase output voltages need to have high steady-state accuracy and strong anti-interference capability. In this paper, an improved model predictive control scheme was proposed. First, a theoretical modeling analysis of the high and low voltage side of a solid-state transformer was performed. Then, power and voltage prediction models are developed for the high-voltage side converter and low-voltage side converter of the SST, respectively. Three voltage vectors are selected for prediction by the cost function, and the equivalent voltage vector in the two-phase stationary coordinate system is synthesized and modulated to control the converter. Finally, simulation was conducted to compare and analyze various operating conditions of SST under different control schemes. The control effect of solid-state transformer under improved model predictive control is better than traditional finite control set model predictive control and proportional integral control, which can better regulate power quality.

Hybrid Prediction-Based Deadbeat Control for a High-Performance Shunt Active Power Filter

This paper proposes a hybrid current predictive deadbeat tracking control method to address the problem that a conventional deadbeat current tracking control has evolved into a deadbeat control in the digital implementation process. First, the reference current prediction link provides in advance the harmonic compensation reference current at two sampling periods by adopting adaptive forward linear prediction and repetitive prediction algorithms. It realizes the switching between the two current prediction algorithms by setting load dynamic occurrence discriminant conditions. Second, the inverter output current historical and harmonic compensation reference current data is used to predict the output current of the inverter. It can effectively predict the output current of the inverter while considering measurement noise suppression. The problems of the deadbeat control method are its strong mathematics model dependence and weak ability to resist high-frequency interference. Therefore, this study analyzes the robust stability of the control system under the uncertainties of the output filter inductor and equivalent resistance parameters of the active power filter and the measurement noise transmission in the system. Finally, the simulation and experimental results indicate that the proposed method based on hybrid current prediction has a high steady-state compensation accuracy and a fast dynamic response speed under steady-state and load mutation conditions. Thus, the proposed method can still effectively compensate the harmonic current under the condition of uncertain output filter inductor and its equivalent resistance parameters.

Improvement and Simulation of PV Power Generation Control Algorithm Based on MPPT

As the traditional PV MPPT algorithm cannot meet both the tracking speed and steady-state accuracy, a new solution idea is provided to improve the traditional conductivity increment method on basis of the slope of the PV cell output P-V curve. The algorithm dynamically changes the step size by combining duty cycle and power and exponential functions, and rapidly adapts to variation in the external environment, solving the slow tracking speed and low steady-state accuracy when the traditional conductance incremental method is subject to sudden changes in illumination. The emulation results indicate that the modified algorithm and the alterable-step increment conductivity method can be simulated in the same environment using MATLAB/Simulink software. The emulation results indicate that the modified algorithm, compared with the traditional algorithm in tracking the MPPT speed and amplitude, can effectively reduce the tracking time with high steady-state accuracy.

Adaptive LuGre Friction Compensation for Servo System Based on Backstepping Control and Feedforward Control

Aims: The study aims to improve the position control accuracy of a class of permanent magnet synchronous motors under friction. Background: Permanent magnet synchronous motor and servo system are important parts of modern industry, in which the friction effect is a typical nonlinear factor. To overcome the nonlinear friction effect, it is necessary to design a compound feedforward algorithm to improve the motion control accuracy. Objective: The objective of the study is to design a compound adaptive friction feedforward controller to overcome the nonlinear friction effect in the servo system while ensuring tracking accuracy. Methods: A compound algorithm combining velocity-acceleration double feedforward and adaptive friction feedforward is proposed to ensure the control accuracy, and then the backstepping control is used to ensure strict convergence. Finally, the friction parameter observer is used to estimate the parameters, and the performance of the control system is simulated in the Simulink module Results: Compared to Pure Friction Feedforward Compensation (PFFC) and Adaptive Friction Compensation (AFC), Adaptive Backstepping Feedforward Friction Compensation (ABFFC) has a faster convergence speed, higher steady-state accuracy, and less friction nonlinear effect Conclusion: The servo system with adaptive backstepping and feedforward friction compensation improves the accuracy and convergence of control performance. Moreover, the adaptive permanent magnet synchronous motor control system can effectively overcome the nonlinear friction effect.

Proximate Fixed-Time Prescribed Performance Tracking Control of Uncertain Robot Manipulators

This article solves the problem of proximate fixed-time prescribed performance trajectory tracking for robot manipulators in the presence of bounded external disturbances and parametric uncertainties. A novel prescribed performance function (PPF) is first presented. A sliding surface with the prescribed performance tracking errors is constructed and a nonsingular proximate fixed-time terminal sliding mode prescribed performance control (FTSMPPC) is developed. It is proved that the position tracking error satisfies the prescribed performance boundaries all the time and globally converges to a preset small region centered on the origin within fixed time and then converges to the origin asymptotically. The proposed FTSMPPC provides faster transient performance quantified and higher steady-state accuracy by the proposed PPF. The effectiveness and improved performances of the presented approach are validated by simulations and experiments.

Phase locked-loop with decaying DC transient removal for three-phase grids

Frequency and phase of the power grid, which are critical for reliable control and protection of grid-tied devices, are generally detected by the closed-loop phase locked-loop (PLL). In highly inductive high-voltage transmission systems, decaying DC (DDC) components with large amplitude can be easily introduced by load disturbances and/or grid abnormalities, leading to severe performance degradation of the PLL during the transient. Focusing on this issue, in this paper, modifications to the conventional synchronous reference frame (SRF)-PLL have been made to address the short-term disturbances including the DDC component, and the system operation is divided into the normal state and the DDC-transient state. The SRF-PLL is only adopted for the normal state where the DDC component is negligible. In the presence of a significant DDC component, as well as disturbances including negative-/zero-sequence components and harmonics, the weak effectiveness of the conventional SRF-PLL is proved, and an efficient DDC component extraction method, with a detection time of 0.5 grid cycle, is introduced for the three-phase system. The real-time amplitude and phase of the positive-sequence component can be efficiently extracted via the proposed scheme, by exploiting the transient signal properties in the dq-frame and assuming a constant grid frequency during the short transient. Finally, a proper design of switching logic has been proposed to allow for the fast and precise transition between the normal and the DDC-transient state, thereby ensuring high steady-state accuracy as well as short-term DDC transient immunity. Hardware-in-the-loop based experiments have been used to verify the effectiveness of the proposed PLL technique.

Active disturbance rejection control for hydraulic systems with full‐state constraints and input saturation

Input saturation and disturbances often exist in hydraulic systems, reducing internal state stability and tracking response performances of the system as well as complicating the design of advanced non-linear controllers. A practical method named active disturbance rejection controller is proposed by backstepping method for full-state constrained hydraulic systems to constrain tracking errors in the desired boundaries and deal with input saturation as well as disturbances. To achieve the previously mentioned objectives, the disturbance compensation strategy and dynamic auxiliary systems are integrated into the barrier Lyapunov theory for the first time. It is proved that all states of hydraulic systems are kept within the ultimate boundaries with satisfactory dynamic performance and high steady-state accuracy. In addition, when disturbances are constants, asymptotic tracking is theoretically achieved. The performance of the control strategy is verified by experimental results.

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

This paper proposes a dynamic high-type control (DHTC) method based on an interval type-2 fuzzy logic controller (IT2FLC), which is used in the photoelectric tracking system to improve the steady-state accuracy and response speed. Adding integrators to the traditional multi-loop feedback control loop can increase the system type, thereby speeding up the response speed and improving the steady-state accuracy, but there is a risk of integral saturation. Switching the type dynamically according to the system state can avoid integral saturation while retaining the advantages of the high-type. Fuzzy logic control (FLC) can dynamically change the output value according to the input change and has the advantages of fast response speed and strong ability to handle uncertainties. Therefore, in this paper, the FLC is introduced into the high-type control system, and the output of the FLC is used as the gain of the integrator to control the on-off to achieve the goal of dynamic switching type, which is successfully verified in the experiment. IT2FLC introduces a three-dimensional membership function, which further improves the FLC’s ability to handle uncertainties. From the experimental results, compared with T1FLC, IT2FLC’s ability to handle uncertainties is significantly improved. In addition, in order to speed up the calculation speed of IT2FLC, this paper proposes an improved type-reduction algorithm, which is called weighted-trapezoidal Nie-Tan (WTNT). Compared with the traditional type-reduction algorithm, WTNT has faster calculation speed and better steady-state accuracy, and has been successfully applied to real-time control systems, which has good engineering application value. Finally, in order to reduce the interference of human factors and improve the automation level of the system, a multi-population genetic algorithm (MPGA) is used to iteratively optimize the parameters of the FLC, which improves the output accuracy. On the experimental platform of the flexible fast steering mirror (FFSM), the control effects of the traditional controller, T1FLC and IT2FLC are compared, which proves that the IT2FLC-DHTC system has a faster response performance, higher steady-state accuracy, and stronger ability to handle uncertainties.

Adaptive feed-forward friction compensation through developing an asymmetrical dynamic friction model

This article presents an adaptive feed-forward method to compensate for the dynamic friction in the machine tool systems so that the tracking errors of the machine axes can be reduced. To achieve this aim, a dynamic friction model is firstly established by constructing a continuous and differentiable two-segmented arc tangent curve to characterize the asymmetrical and nonlinear static friction phenomenon in relation to the positive and negative velocity ranges of the machine axes. Subsequently, a new feed-forward method is proposed to compensate for the friction by constructing an adaptive controller with three advantages, i.e. no need of velocity and acceleration measurement, large adaptive rate and high steady-state accuracy. During the model establishment, a discontinuous projection mapping in the learning process is constructed. Especially, the stability of the adaptive controller is strictly proved by theoretical derivation. Besides, a new two-step adaptive method is formulated to analytically calculate the unobservable parameter z. Finally, both simulations and experiments confirm that the proposed compensation method can reach to lower tracking errors, especially in the velocity reversal area, by comparing with the existing approaches.