Double Closed-loop Control System Research Articles

Optimized Trajectory Tracking for ROVs Using DNN + ENMPC Strategy

This study introduces an innovative double closed-loop 3D trajectory tracking approach, integrating deep neural networks (DNN) with event-triggered nonlinear model predictive control (ENMPC), specifically designed for remotely operated vehicles (ROVs) under external disturbance conditions. In contrast to single-loop model predictive control, the proposed double closed-loop control system operates in two distinct phases: (1) The outer loop controller uses a DNN controller to replace the LMPC controller, overcoming the uncertainties in the kinematic model while reducing the computational burden. (2) The inner loop velocity controller is designed using a nonlinear model predictive control (NMPC) algorithm with its closed-loop stability proven. A DNN + ENMPC 3D trajectory tracking method is proposed, integrating a velocity threshold-triggered mechanism into the inner-loop NMPC controller to reduce computational iterations while sacrificing only a small amount of tracking control performance. Finally, simulation results indicate that compared with the ENMPC algorithm, NMPC + ENMPC can better track the desired trajectory, reduce thruster oscillations, and further minimize the computational load.

Adaptive fast nonsingular terminal sliding mode tracking control for quadrotor with disturbances compensation and actuator saturation

This paper designs the double closed-loop control system combining nonlinear disturbances observers (NDOB) and an adaptive fast nonsingular terminal sliding mode (AFNTSM) for the tracking control problem of quadrotor under external disturbances, actuator saturation and unmodeled dynamics. The NDOB-AFNTSM method avoids the singularity problem. To compensate for disturbances, an adaptive strategy estimates the unknown bound of NDOB errors and the unknown bound of disturbances. Besides, new fast reaching law shortens convergence times. The dead zone method is chosen to avoid over-estimation and system divergence. Considering actuator saturation, this paper uses an anti-saturation auxiliary compensator to compensate for unknown saturation errors. System stability is confirmed by the Lyapunov direct method. The simulation results reveal that the proposed method improves convergence speed and accuracy, which is more robuster than similar methods. Finally, two comparative studies for performance indexes in simulation demonstrate the preferable performance of the proposed method.

An improved global fast terminal high-order sliding mode control strategy based on novel reaching law for improving PMLSM dynamic performance

Due to its better power density and efficiency, permanent magnet linear synchronous motor (PMLSM) is ideal for generating electricity with wave power device (WPD). Nevertheless, when the PMLSM for the WPD control system is impacted by changes in motor characteristics and interference from the outside environment, it typically is unable to deliver high-quality dynamic performance on schedule. Although sliding mode control (SMC) has been introduced to improve the robustness of the system greatly in recent decades, traditional methods still have problems such as slow dynamic response, poor anti-disturbance performance, large chattering amplitude, and phase delay. To solve the above defects in SMC, an improved global fast terminal high-order SMC strategy based on novel reaching law for improving PMLSM dynamic performance is proposed in this paper, as well as the sliding mode surface and control law of the speed controller is designed based on this control strategy. The proposed strategy would not only shorten the dynamic response time but also weaken the chattering magnitude in the sliding mode stage, improving the dynamic performance of both speed and current. The simulation results demonstrate that in comparison to the traditional PI and SMC control methods, the improved strategy proposed in this paper can further enhance the control performance of the linear motor, demonstrating the method’s enormous potential in the double closed-loop vector control system of PMLSM for WPD.

Automatic Control System of Manipulator Clamping Angle Based on Machine Vision

In order to shorten the time it takes for the manipulator components to reach the preset position and avoid the behavior state of excessively increasing the gripping angle, an automatic control system for the gripping angle of the manipulator based on machine vision is designed. According to the motion state of the mechanical components of the gripper, the connection sensitivity of the angular position sensor is controlled, and then the tension and pressure control structure is used to complete the on-demand adjustment of the clamping force action form and realize the construction of the application platform environment of the hardware structure of the automatic control system. On this basis, by grabbing the necessary manipulator operation image, following the principle of machine vision, completing the preprocessing, and then combining the kinematic functions to perform the force control switching operation and realizing the gripping force control based on machine vision, and completing the design and application of the automatic control system for the gripping angle of the manipulator. The experimental results show that when the motor driving speed is equal to 2000 PPS,4000 PPS, and 6000 PPS, the consumption time required for the manipulator components to reach the preset position will not exceed 9.5s. Compared with the double closed-loop control system, it is more in line with the application requirements of effectively controlling the behavior of the clamping angle.

Design of Double Closed Loop Control System for DC Motor Based on TX8C1010 Single Chip

In order to improve the operational strength and control precision of the DC motor, a double closed-loop control system based on TAIXIN TX8C1010 MCU is proposed. The single chip is based on a high-speed 8051 core, which is applied to home appliances and can realize the double closed-loop motor control algorithm. The given speed value provided by the potentiometer is compared with the real-time speed value and inputs the error to the speed loop function, which is based on the incremental PID algorithm, to calculate the reference current value. Then it is compared with the real-time current value and inputs the error into the current loop. The PWM signal duty cycle is determined by the output value of the current loop to adjust and stabilize the motor speed. The drive circuit outputs the PWM signal in the inverse amplification way to control the operation of the DC motor, and finally tests the function on the MAGTROL M-TEST7 motor test platform. The test results show that the control system has fast response speed, strong load capacity, good robustness and safety. Hence, TAIXIN TX8C1010 single-chip machine is feasible in the DC motor control. It can be improved to strengthen its load characteristics according to the parameters and applied to a variety of power DC motor control.

Modeling Analysis of Superbuck Converter in Dual-Loop Competition Control

A Battery Charge Regulator system based on the Superbuck converter is presented, the working principle of the Superbuck converter is analyzed and the small signal model is established. In order to meet the requirements of wide input range and high dynamic response of the Superbuck converter, a double closed-loop control system is designed with output voltage and output current dual loop control as outer loop and input current control as inner loop. The stability of the control loop is verified by the Bode plot, and the simulation result of the system in two working conditions are given to support the theoretical analysis.

Adaptive neural network–based trajectory tracking non-affine control of hovercraft with double-loop constraint performance

This study investigates the design of a trajectory tracking constraint control method for hovercraft non-affine formulations via neural network approximation. The tracking scheme comprises an outer closed-loop of attitude and an inner closed-loop of velocity (double-loop). First, a performance function is devised to constrain the attitude errors and then a transformed error function is introduced to simplify it into an equivalent unconstrained control system. Next, an improved asymmetric integral barrier Lyapunov function is designed to address asymmetric velocity constraint problems, which can guarantee the safe turning motion or performance required at high speed. Furthermore, neural networks are employed to estimate the unknown terms of each subsystem, which solve system uncertainties and non-affine dynamics problems. The double closed-loop control system is ultimately uniformly bounded by utilizing the Lyapunov stability principle. Finally, simulation results are presented to verify the tracking performance and effectiveness of the proposed control strategy.

A Novel Fuzzy Controller for Visible-Light Camera Using RBF-ANN: Enhanced Positioning and Autofocusing.

To obtain high-precision for focal length fitting and improve the visible-light camera autofocusing speed, simultaneously, the backlash caused by gear gaps is eliminated. We propose an improved RBF (Radical Basis Function) adaptive neural network (ANN) FUZZY PID (Proportional Integral Derivative) position closed-loop control algorithm to achieve the precise positioning of zoom and focus lens groups. Thus, the Levenberg-Marquardt iterative algorithm is used to fit the focal length, and the improved area search algorithm is applied to achieve autofocusing and eliminate backlash. In this paper, we initially adopt an improved RBF ANN fuzzy PID control algorithm in the position closed-loop in the visible-light camera position and velocity double closed-loop control system. Second, a similar triangle method is used to calibrate the focal length of the visible-light camera system, and the Levenberg-Marquardt iterative algorithm is used to fit the relation of the zoom potentiometer code values and the focal length to achieve the zoom position closed-loop control. Finally, the improved area search algorithm is used to achieve fast autofocusing and acquire clear images. The experimental results show that the ITAE (integrated time and absolute error) performance index of the improved RBF ANN fuzzy PID control algorithm is improved by more than two orders of magnitude as compared with the traditional fuzzy PID control algorithm, and the settling time is 6.4 s faster than that of the traditional fuzzy PID control. Then, the Levenberg-Marquardt iterative algorithm has a fast convergence speed, and the fitting precision is high. The quintic polynomial fitting results are basically consistent with the sixth-degree polynomial. The fitting accuracy is much better than that of the quadratic polynomial and exponential. Autofocusing requires less than 2 s and is improved by more than double that of the traditional method. The improved area search algorithm can quickly obtain clear images and solve the backlash problem.

Model-Based Design and Experimental Validation of Control System for a Three-Level Inverter

Considering the disadvantages of the traditional development pattern in an embedded control system for an inverter, that is, the single-process thinking and separation of software and hardware, a novel method, which is a model-based design, for developing a double closed-loop control system for the diode-clamped three-level inverter was proposed. System control models, including the PWM control algorithm model, the voltage control model, the neutral-point potential balancing model, and the frequency control model, were built with the MATLAB platform. The code-generation capacity and the operation effect were verified through a series of tests. The inverter with diode clamp and neutral-point potential control was developed. Codes were generated automatically and downloaded to the eZdsp28335 control chip. Experimental waveforms of the phase voltage, line voltage and current were analyzed under regulating the voltage and frequency. The experimental results demonstrate that the models and the generated codes are correct. Further studies have proven the feasibility of the system development model.

Research on Optimization Simulation of Three-level Inverter in UPS Based on PSO Algorithm

As a kind of precision power equipment, the uninterruptible power supply system (UPS) can supply power to the load through a DC-AC converter to ensure the uninterrupted power supply in case of grid failure. The voltage waveform output by the inverter has a decisive effect on the power quality, performance of the UPS system is determined by the power quality. Therefore, this paper aims to optimize the output power of the inverter from two aspects: on the one hand, the three-level space vector pulse-width modulation (SVPWM) technology is combined with the double closed-loop control system to improve the voltage waveform of the inverter; On the other hand, the PI parameters in the double closed loop are optimized by the particle swarm optimization (PSO) algorithm, the dynamic performance of the system is optimized, the adjustment time and overshoot are reduced, thus the control performance of the UPS system is improved.

Research on Trajectory Tracking Control of Inspection UAV Based on Real-Time Sensor Data.

In power inspection, uncertainties, such as wind gusts in the working environment, affect the trajectory of the inspection UAV (unmanned aerial vehicle), and a sliding mode adaptive robust control algorithm is proposed in this paper to solve this problem. For the nonlinear and under-driven characteristics of the inspection UAV system, a double closed-loop control system which includes a position loop and attitude loop is designed. Lyapunov stability analysis is used to determine whether the designed system could finally achieve asymptotic stability. Sliding-mode PID control and a backstepping control algorithm are applied to analyze the superiority of the control algorithm proposed in this paper. A PX4 based experimental platform system is built and experimental tests were carried out under outdoor environment. The effectiveness and superiority of the control algorithm are proposed in this paper. The experimental results show that the sliding mode PID control can achieve good accuracy with smaller computing costs. For nonlinear interference, the sliding mode adaptive robust control strategy can achieve higher trajectory tracking accuracy.

Prevention and Detection Research of Intelligent Sports Rehabilitation under the Background of Artificial Intelligence.

Artificial intelligence can bring convenience to human life. In the field of sports rehabilitation, the application of artificial intelligence is becoming more and more in-depth. This paper is aimed at studying the prevention and detection of sports rehabilitation in the context of artificial intelligence and proposing a compliance control method for lower limb rehabilitation robots based on artificial neural networks. In this paper, a double closed-loop control system is designed: the outer loop is an adaptive impedance control model based on sEMG feedback, and the purpose is to adjust the predicted desired joint trajectories. In the inner loop, a sliding mode iterative learning controller is designed to suppress periodic disturbance and abnormal jitter and achieve stable tracking of the target trajectory. Finally, the control method is simulated and verified by matlab/simulink, and a statistical experiment is done on the patient's recovery. The experimental results show that the use of artificial intelligence technology can effectively increase the sensitivity of the control system and improve the recovery rate of patients. Compared with the traditional sports rehabilitation control system, the sensitivity is increased by 22.7%, and the patient recovery rate is increased by 10.4%, which is of great significance in the field of sports rehabilitation.

Synchronous Generator Rectification System Based on Double Closed-Loop Control of Backstepping and Sliding Mode Variable Structure

Low-voltage and high-current direct current (DC) power supplies are essential for aerospace and shipping. However, its robustness and dynamic response need to be optimized further on some special occasions. In this paper, a novel rectification system platform is built with the low-voltage and high-current permanent magnet synchronous generator (PMSG), in which the DC voltage double closed-loop control system is constructed with the backstepping control method and the sliding mode variable structure (SMVS). In the active component control structure of this system, reasonable virtual control variables are set to obtain the overall structural control variable which satisfied the stability requirements of Lyapunov stability theory. Thus, the fast-tracking and the global adjustment of the system are realized and the robustness is improved. Since the reactive component control structure is simple and no subsystem has to be constructed, the SMVS is used to stabilize the system power factor. By building a simulation model and experimental platform of the 5 V/300 A rectification module based on the PMSG, it is verified that the power factor of the system can reach about 98.5%. When the load mutation occurs, the DC output achieves stability again within 0.02 s, and the system fluctuation rate does not exceed 2%.

Development of Double Closed-loop Vector Control Using Model Predictive Control for Permanent Magnet Synchronous Motor

Vector control has been widely used to manipulate the permanent magnet synchronous motor (PMSM), in which the traditional double proportional integral (PI) controllers have commonly been utilized. However, it is difficult to set the suitable parameters of PI and maintain the PMSM performance under the different operating regimes. In order to solve these problems, a double closed-loop model predictive control (MPC) strategy based on the vector control has been presented for PMSM in this paper. Firstly, a simplified discrete model of PMSM is established in the d-q coordinate system without considering the interference. Then, the MPC is developed to substitute the PI controller in the speed-loop and the q-axis current-loop, respectively, and a double closed-loop MPC control system is formed. Finally, some case studies are carried out. The simulation and experimental results demonstrate that the presented MPC strategy improves the dynamic and steady-state performance of PMSM.

Transient Analysis and Verification of a Magnetic Gear Integrated Permanent Magnet Brushless Machine With Halbach Arrays

The magnetic gear integrated permanent magnet brushless machine (MG-IPMBM) is a kind of compact structure with high torque density and complex electromagnetic energy transfer. In order to analyze the transient characteristics of the machine, an analysis method of field circuit coupling is proposed. Both magnetic gear (MG) and permanent magnet motor are magnetized by Halbach array. Combining the analytical model of the MG magnetic field with MATLAB motor simulation module, a joint simulation model of field circuit coupling double closed-loop control system based on the Simulink-analytical method is established. The electromechanical transient response characteristics of machine under load starting, load mutation, and overload self-protection are analyzed. The simulation results show that the output speed and torque of machine can accurately follow the given value, and its unique overload self-protection ability greatly improves the safety of machine operation. In addition, a prototype is made and an experimental platform is built. The experimental results show that the output torque follows quickly, the torque ripple is small, and transient performance of the machine is good.

PMSM double loop predictive control based on Luenberger observer

Predictive control strategy is applied to permanent magnet synchronous motor (PMSM) control system, it can achieve fast dynamic response and high-precision tracking control, but depending on the mathematical model of the (PMSM), the load disturbance will affect the control performance of system. Therefore, a control method based on the combination of Luenberger observer and predictive control is proposed to construct a double closed-loop predictive control system. Through the load disturbance estimated by the Luenberger observer, the torque current compensation produced is used as the feedforward compensation of the speed loop to achieve disturbance suppression. The simulation results under Simulink environment show that this method can improve the dynamic response performance and steady-state accuracy of the control system, and greatly improve the robustness.

Modeling and simulation of three-phase 6/4 switched reluctance motor speed control system

To improve the response ability of the motor and reduce the torque ripple, we take the three-phase 6/4 SRM as the research object, according to the structure and characteristics of the SRM speed control system, the SRM speed and current double closed-loop control system are established and the current chopping control mode is adopted to change the motor speed. According to the mathematical model of SRM, then the model of each module of the speed control system is established by Matlab/Simulink. The simulation and performance analysis are carried out. The results show that the simulation model double closed loop control method can improve the system response ability, improve the stability of the motor and reduce the torque ripple.

Harmonic current suppression method with adaptive filter for permanent magnet synchronous motor

ABSTRACT Due to the distortion of air gap magnetic field and the inherent non-linear characteristics of the inverter, the stator current of permanent magnet synchronous motor (PMSM) contains a large number of high order harmonic components, which distorts the current waveform and generates torque ripple, limiting the application of the PMSM in high speed and precision situations. To solve this problem, a harmonic current suppression method is proposed based on synchronous rotating coordinate transformation theory and adaptive filtering algorithm. The PMSM harmonic mathematical model is established and the coordinate transformation method is used to extract the 5th and 7th harmonic currents. On the basis of the original double closed-loop control system, the harmonic current loop is added, and the harmonic components are suppressed by injecting harmonic voltages. In order to improve the filtering effect and reduce the harmonic content, an adaptive filter is designed to replace the conventional low pass filter in the process of harmonic current extraction. According to the results of coordinate transformation, the adaptive filter continuously adjusts the parameters online through the least mean square (LMS) algorithm to extract the harmonic current. The simulation and experimental results proved the correctness and effectiveness of the proposed method.

Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition

Discovering the implicit pattern and using it as heuristic information to guide the policy search is one of the core factors to speed up the procedure of robot motor skill acquisition. This paper proposes a compound heuristic information guided reinforcement learning algorithm PI2-CMA-KCCA for policy improvement. Its structure and workflow are similar to a double closed-loop control system. The outer loop realized by Kernel Canonical Correlation Analysis (KCCA) infers the implicit nonlinear heuristic information between the joints of the robot. In addition, the inner loop operated by Covariance Matrix Adaptation (CMA) discovers the hidden linear correlations between the basis functions within the joint of the robot. These patterns which are good for learning the new task can automatically determine the mean and variance of the exploring perturbation for Path Integral Policy Improvement (PI2). Compared with classical PI2, PI2-CMA, and PI2-KCCA, PI2-CMA-KCCA can not only endow the robot with the ability to realize transfer learning of trajectory planning from the demonstration to the new task, but also complete it more efficiently. The classical via-point experiments based on SCARA and Swayer robots have validated that the proposed method has fast learning convergence and can find a solution for the new task.

A Novel Maximum Power Point Tracking Control Strategy for the Building Integrated Photovoltaic System

Thin-film photovoltaic technology has begun to be applied in building-integrated photovoltaics (BIPVs), and it is believed that thin-film photovoltaic technology has potential in building-integrated photovoltaic applications. In this paper, a hybrid approach was investigated which combined the maximum power point tracking (MPPT) algorithm of three-stage variable step size with continuous conduction mode (CCM)/discontinuous current mode (DCM). The research contents of this paper include the principle analysis of the maximum power point tracking algorithm, the design of the sampling period, and the design of a double closed-loop control system and correction factor. A system model was built in MATLAB/Simulink, and a comparative simulation was carried out to compare the performance of the proposed method with some traditional methods. The simulation results show that the proposed approach has the ability to fast-track and make the system run stably. Furthermore, it can make the system respond quickly to environmental changes. An experimental platform was built, and the experimental results validated and confirmed the advantages of the proposed method.