High Control Accuracy Research Articles

Self‐optimization of the V/P switchover and packing pressure for online viscosity compensation during injection molding

AbstractInjection molding (IM) is one of the essential forming methods for thermoplastic polymers, which is widely used in modern industries such as automobiles, electronics, and medical industries. At present, the machine parameters of the IM machine (IMM) have achieved sufficient high control accuracy and repeatability. However, the viscosity of thermoplastic melt is still easily influenced by the external environment, such as the fluctuations in batches, the compounding in recycled materials, and so on. Conventional IM equipment cannot sense and conduct adjustments correctly, which leads to the production of rejects. A real‐time monitoring and controlling model employed for viscosity compensation was established in this article, which could monitor viscosity fluctuations and implement self‐adjustment in the IM process. Three kinds of polypropylenes (PP) with different viscosity and materials with different percentages of recycled pellets were randomly added into the barrel for comparison. The results revealed that the pressure integral relative to time is able to monitor the melt viscosity and illustrate the IMM to optimize the V/P switchover and packing pressure in the current molding cycle. The part weight could achieve a higher stability and the model could bring about a decrease in weight fluctuations of 50% to 70%.

A multi-modal brain-computer interface based on threshold discrimination and its application in wheelchair control.

In this study, we propose a novel multi-modal brain-computer interface (BCI) system based on the threshold discrimination, which is proposed for the first time to distinguish between SSVEP and MI potentials. The system combines these two heterogeneous signals to increase the number of control commands and improve the performance of asynchronous control of external devices. In this research, an electric wheelchair is controlled as an example. The user can continuously control the wheelchair to turn left/right through motion imagination (MI) by imagining left/right-hand movement and generate another 6 commands for the wheelchair control by focusing on the SSVEP stimulation panel. Ten subjects participated in a MI training session and eight of them completed a mobile obstacle-avoidance experiment in a complex environment requesting high control accuracy for successful manipulation. Comparing with the single-modal BCI-controlled wheelchair system, the results demonstrate that the proposed multi-modal method is effective by providing more satisfactory control accuracy, and show the potential of BCI-controlled systems to be applied in complex daily tasks.

Adaptive coordinated motion constraint control for cooperative multi-manipulator systems

Constrained motion and redundant degrees of freedom control exist in a multi-manipulator collaboration system. In other words, the multi-manipulator collaboration technology must solve the problems of uncertain environment interaction and coordinated control. Few studies have been conducted on the coordination control of a multi-manipulator, and the control effect is not good. To solve the coordinated motion problem of the multi-manipulator cooperative system, this study divides the multi-manipulator coordinated motion into two forms, namely coupled and superimposed motions, and proposes an adaptive coordinated motion constraint scheme under different motion forms. The coupled and superimposed motions are investigated through coordinated handling and coordinated drawing circle tasks, respectively. The proposed coordinated control scheme has a good effect. Without position detection and positioning, the kinematic constraint algorithm can maintain the relative motion relationship between end-effectors. When an external disturbance occurs, the slave manipulator can automatically adjust based on the position of the main manipulator, avoiding error accumulation. The experimental results show a maximum trajectory tracking error of 2.131 mm and maximum attitude error of 0.176°, indicating that the proposed control scheme has strong adaptive ability and high control accuracy.

An implicit discretization-based adaptive reaching law for discrete-time sliding mode control systems

In conventional reaching law approaches, the disturbance suppression is achieved at the cost of high-frequency chattering or increasing the complexity of algorithm such as adding a high-order disturbance compensator. This paper presents the design and analysis of a novel implicit discretization-based adaptive reaching law for discrete-time sliding mode control systems. First, the implicit Euler technique is introduced into the design of discrete reaching laws, and it is proved to be able to eliminate numerical chattering completely. By using a self-adaptive power term, the newly designed reaching law can obtain an arbitrarily small boundary layer of sliding surface, and at the different phases of sliding mode motion, the adaptive power parameter can automatically regulate its value to guarantee globally fast convergence without destroying the accuracy of sliding variable. Then, based on a predefined trajectory of sliding variable, the discrete-time sliding mode control law is developed to realize high control accuracy without additional design. Compared with previous methods, the main contribution of proposed reaching law lies in that it can acquire high-precision sliding mode motion and simultaneously eliminate numerical chattering in spite of complex uncertainties only by adjusting the adaptive power parameter. Finally, a simulation example on the piezomotor-driven linear stage is provided to verify the theoretical results.

A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells

The booming personalized and customized demands of customers in Industry 4.0 pose great challenges for manufacturing enterprises in terms of flexibility and responsiveness. Nowadays, many effective dynamic scheduling approaches have been proposed for manufacturing systems to quickly respond to changes in customer demands, where, however, the implementation of an automatic programming method with high control accuracy and low control delay is still challenging. The above unaddressed issue brings about a lot of labor-intensive and time-consuming manual offline programming work when adjusting the scheduling scheme to meet dynamic customer demands, resulting in limited flexibility and responsiveness in current manufacturing systems. To bridge this gap, a bi-level adaptive control architecture enabled by an automatic programming method is proposed and embedded into a digital twin manufacturing cell (DTMC). The bi-level architecture aims to automatically map an input task scheduling scheme with a batch of jobs into a group of control programs through a behavior model network and a set of event models embedded in DTMC. It also provides an adaptive program modification mechanism to quickly adapt to the dynamic adjustment of the scheduling scheme caused by the changing of customer demands or production conditions, thus equipping DTMC with strong flexibility and responsiveness. Based on the bi-level architecture, a DTMC prototype system is developed, where its application and evaluation results demonstrate the feasibility and effectiveness of the proposed approach.

Design and Experimental Study of Ultrasonic Vibration Feeding Device With Double Symmetrical Structure

In view of the limitations of traditional feeder driving mode in bulk material conveying, a new scheme of ultrasonic vibration feeding device with double symmetrical axis structure is proposed based on the existing traveling wave ultrasonic material conveying device. The outer sides of the upper and lower straight beam sections of the symmetrical conveying vibrator contain convex tooth structures, and piezoelectric ceramic sheets are pasted on the inner side; Based on the piezoelectric driving principle and the quarter wavelength principle, the conveying vibrator is dynamically designed, the influence of geometric size on the vibrator frequency is analyzed, the bending vibration mode of the vibrator is selected as 18 order, and the principle prototype of ultrasonic vibration feeding device is made. The resonant frequency difference measured in the experiment is close to that obtained by theoretical analysis, Moreover, the amplitude distribution of the conveying vibrator tooth surface is basically consistent with the finite element analysis results, and the influence law of material types on the conveying rate is compared and analyzed. The test results show that the ultrasonic vibration feeding device has a certain conveying capacity, the conveying rate is adjustable in a certain range, has high control accuracy, and meets the engineering application.

Model Predictive Current Control Algorithm Based on Joint Modulation Strategy for Low-Inductance PMSM

The disc coreless permanent magnet synchronous machine (PMSM) has many advantages. The inductance is far lower than that of the machines with the core structure. However, the general converter is designed for general machines. The current fluctuations are large when the low-inductance PMSM (LIPMSM) is driven by the general converter. To solve the above problem, the integrated design concept is adopted to design the converter, including the modulation algorithm, control algorithm, and the corresponding topology. The joint modulation strategy is proposed to increase the modulation frequency, which adjusts the output voltage by using the bus circuit and inverter circuit. The model predictive current control (MPCC) algorithm based on the joint modulation strategy is proposed, which has a small computational burden and high control accuracy. The most important feature of the proposed MPCC is that it has a modulation algorithm. To further reduce the current fluctuations, the bus voltage is adjusted in real time according to the bus voltage optimization algorithm. A new topology of the converter is designed to implement the algorithm function. Finally, the correctness and feasibility of the proposed method are verified. Compared with the general converter, the new one has a great improvement on the LIPMSM.

Ship RBF neural network sliding mode PID heading control

In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.

Recursive Terminal Sliding-Mode Control Method for Nonlinear System Based on Double Hidden Layer Fuzzy Emotional Recurrent Neural Network

Aiming at the problem of the coexistence of nonlinearity and uncertainty in the control systems, a novel control method which combines the double hidden layer fuzzy emotional recurrent neural network (DHLFERNN) and the recursive terminal sliding mode control (RTSMC) is proposed. Firstly, a novel double hidden layer fuzzy emotional recurrent neural network (DHLFERNN) is designed. The proposed DHLFERNN can be considered as a combination of a fuzzy neural network (FNN) and a double hidden layer recurrent neural network (DHLRNN) in the framework of brain emotional learning (BEL), which could make the controller obtain higher nonlinear approximation ability. Secondly, a recursive terminal sliding mode control (RTSMC) method based on the DHLFERNN is proposed. In this method, the nonlinear sliding-mode equivalent control term is approximated by the proposed DHLFERNN, which makes the controller possess a good nonlinear approximation ability when the nonlinear model cannot be obtained exactly. Finally, the stability of closed-loop system is proved by the Lyapunov method, and the adaptive law of each parameter in DHLFERNN is derived. The proposed method is verified on an inverted pendulum system, and the comparison with other control methods proves that the proposed method has faster convergence speed and higher control accuracy.

NSGA-II algorithm-based LQG controller design for nuclear reactor power control

The design of reactor power control system is closely related to the safety and economy of nuclear power plants. To improve its power control performance, this study takes a pressurized water reactor as the research object. The nonlinear dynamic mathematical model and its state space model were first derived; Then the Linear Quadratic Gaussian (LQG) optimal control method was adopted to design the reactor power controller; Furthermore, the multi-objective optimization of the linear quadratic regulator (LQR) weighting coefficient is carried out based on the elitist nondominated sorting genetic algorithm version II (NSGA-II). The results show that the LQG controller optimized based on NSGA-II method not only has fast response, high control accuracy and good stability, but also has good robustness and can obtain superior reactor power control performance.

Control Strategy of Microgrid Inverter Based on H∞ State Feedback Repeated Deadbeat Control

Aiming at the voltage distortion at the microgrid public connection point caused by nonlinear loads, a H∞ state feedback deadbeat repetitive control strategy is proposed to rectify the total harmonic distortion of the output voltage. Firstly, through establishing the state space of the repetitive controller, introducing state feedback, combining the H∞ control theory, and reformulating the system stability problem as a convex optimization problem with a set of linear matrix inequality (LMI) constraints to be solved, high stability control accuracy can be guaranteed and antiharmonic interference strengthened. Secondly, by introducing deadbeat control technology to improve the transient response speed of the system, changes in output voltage caused by load changes can be quickly compensated. Compared with the existing methods, the designed control method has the advantages of good stability, low harmonic content, and fast convergence speed, and the results are easier to verify. Finally, the simulation verifies the effectiveness of the proposed control strategy.

Neural network adaptive sliding mode control without overestimation for a maglev system

This study focuses on the high-performance control of a reluctance-motor maglev system (RMMS), while strong uncertainty brings great difficulties. As an effective robust control method, sliding mode control (SMC) suffers from chattering which brings challenges to practical applications. To overcome it, a novel neural network adaptive sliding mode control (NNASMC) method is proposed, which combines a new radial-basis-function-neural-network (RBFNN) compensator with a new adaptive sliding mode controller. Different from the existing results, this method not only compensates for the uncertainty, but also avoid overestimating the switching gain. Moreover, the controller, based on a generalized-form switching gain, needs no prior knowledge of the uncertainty. The stability of NNASMC is analyzed by a Lyapunov function. Through experiment and simulation, NNASMC is compared with SMC and an SMC with a double hidden layer recurrent neural network (SMC-DHLRNN) method. The necessities of compensator and adaptive switch gain are verified by simulation. Experimental results show that NNASMC reduces chattering significantly, achieves high-accuracy control of an RMMS, and has good disturbance-rejection ability.

Broad-RBFNN-Based Intelligence Adaptive Antidisturbance Formation Control for a Class of Cluster Aerospace Unmanned Systems with Multiple High-Dynamic Uncertainties

This paper investigates the antidisturbance formation control problem for a class of cluster aerospace unmanned systems (CAUSs) suffering from multisource high-dynamic uncertainties. Firstly, to estimate and compensate the uncertainties existing in CAUS coordinate dynamics, an adaptive antidisturbance formation control law, which is combined by a robust adaptive control law and the second order disturbance observer, has been designed. Secondly, aiming at the adverse influences caused by the nonlinear time-varying nonlinearities existing in the formation flight dynamics, the radial basis function neural network (RBFNN) is introduced. Furthermore, considering the rapidly varying characteristics of the aforementioned formation flight nonlinearities, a novel board RBFNN (B-RBFNN) has been constructed and utilized to improve the approximation and compensation performance. In virtue of the fusing of the B-RBFNN and the second-order disturbance observer-based adaptive formation control law, the rapid response rate and the higher control accuracy of the formation control system can be achieved. As a result, a novel B-RBFNN-based intelligence adaptive antidisturbance formation control algorithm has been established for CAUS trajectory coordination and formation flight. Numerical simulation results are proposed to illustrate the effectiveness and advantages of the proposed B-RBFNN-based intelligent adaptive formation control method for the CAUS.

Robust control of a space robot based on an optimized adaptive variable structure control method

Space robot has been playing an increasingly important role in on-orbit service missions. Dynamic coupling exists between the space robot arm and the floating base, and an inaccurate dynamic model will deteriorate the control accuracy of space robot motion. This paper proposed an optimized adaptive variable structure control method to realize coordinate motion control of space robot base and its arm. The controller can adapt its gain to match system uncertainties and external disturbances so that the error dynamics converge to the origin following a parabola-like path which is close to the natural behavior of a second-order system. Therefore, the controller will eliminate the chattering phenomenon and reduce the settling time. Further, taking the motion error as the objective function, the gain parameter is optimized by adopting the modified Gaussian barebones differential evolution method. Numerical simulations aimed at verifying the space robot dynamic model and the effectiveness of the controller are carried out based on Simscape Multibody. The results prove that the theoretical dynamic model of the space robot is accurate. In addition, the controller is demonstrated to present reduced settling time and higher control accuracy in comparison with the boundary layer sliding mode control method.

Insights into carbon production by CO2 reduction in molten salt electrolysis in coaxial-type reactor

With carbon capture and utilization (CCU) technologies, it is possible to take CO2 from air and transform it into valuable carbon products, such as graphene and nanotubes. In this study, CO2 was reduced to solid carbon by molten lithium carbonate salt electrolysis. The reactor used for the electrolysis was a coaxial-type unit, where a cylindrical nickel cathode was placed inside a cylindrical stainless steel vessel; such a coaxial-type reactor has not been used before for molten salt electrochemical reduction of CO2. This reactor design allows studies of voltage-current characteristics, which have previously not been conducted for this specific process. As a result, voltage efficiency of this process could be determined along with effect of temperature to cell voltage. Based on these results it is evident that accurate temperature control is crucial in terms of energy efficiency of the process. Data monitoring revealed a high accuracy of temporal temperature control. Great temperature differences were observed spatially. Thermoneutral voltage from the process was determined to be 1.02 V. For this electrochemical process studied, carbon nano-onions (CNOs) were obtained as a main product. Identification of the product was carried out based on Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM) and X-Ray Diffraction (XRD) analyses. Results also showed that iron, chromium and nickel metals are released during electrolysis. Iron and chromium can only be released from the stainless steel anode, as nickel can come from anode or cathode.

An Optimal Control Strategy for Multi-UAVs Target Tracking and Cooperative Competition

An optimal control strategy of winner-take-all (WTA) model is proposed for target tracking and cooperative competition of multi-UAVs (unmanned aerial vehicles). In this model, firstly, based on the artificial potential field method, the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets. Secondly, according to the finite-time convergence high-order differentiator, a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory. Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking, high precision, strong stability and avoiding chattering. Finally, a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy. Theoretical analysis and numerical simulation results show that the model has the fast convergence, high control accuracy, strong stability and good robustness.

Application of two-phase hybrid stepping motor based on three-phase inverter chopping control strategy in aircraft electric drive servo valve

With the development of multi-/all-electric technology, more and more aircraft platforms use electrically driven servo valves as the driving source to realize real-time adjustments of flow, pressure and temperature in the area network. The new generation of aircraft applies a stepper motor to drive the servo valve as the drive source, and utilizes the holding torque and open-loop control characteristics of the stepper motor when the stepper motor could not meet aircraft’s requirements of the reliability of the servo valve, the controllability of the opening and closing angle, and the environmental resistance. This paper develops a set of stepper motor drive servo valve control system. The system is mainly composed of flight tube bus, electromechanical management computer, remote actuation unit, remote interface unit and motor-driven servo valve. The stepper motor driver is integrated in the remote execution unit and is used to control the two-phase hybrid stepper motor to drive the servo valve. The topology of a three-phase inverter bridge drive is used to achieve the two-phase double four-shot drive, which saves about 25% power drive hardware. By controlling the two-phases motor, the direction and amplitude of the current one can realize micro-step control. The test and simulation result show that the system has higher control accuracy and better acceleration. The deceleration characteristics in two-phase full step and micro step working modes can expand the application of electric servo valve and improve aircraft performance.

Frequency tracking and tuning control of wireless power transfer system

A tuning control method with frequency tracking function is proposed to improve the degradation of efficiency caused by coil detuning in magnetically-coupled resonant wireless power transfer. Firstly, the detuning mechanism is studied by combining the AC impedance characteristics of the series resonant circuit, and the feedback control circuit is constructed by using a modified phase-locked loop, which outputs a variable frequency PWM wave to regulate the operating frequency of the high frequency inverter and maintains the phase difference between the inverter output voltage and the original side current within the error range. The Matlab/Simulink simulation results show that the design can successfully transfer the system to a new resonant state with short regulation period and high control accuracy, which can effectively improve the transmission efficiency and load power of the system.

Research And Analysis of Nonlinear Identification Adaptive Control of Fitness Using MATLAB Numerical Simulation

Aiming at the problems of non-linearity in physical exercise, difficulty in predictive model identification control, and difficulty in exercise in control target. A nonlinear identification adaptive control algorithm based on physical exercise is proposed, and a nonlinear identification adaptive control system based on physical exercise is established and simulated with MATLAB. Simulation results show that the recognition ability of physical exercise is strong, which improves the requirements of physical exercise quality, and has the advantages of simple control algorithm, high control accuracy and strong anti-interference ability.

Active disturbance rejection attitude control of unmanned quadrotor via paired coevolution pigeon-inspired optimization

PurposeThe purpose of this paper is to develop a novel active disturbance rejection attitude controller for quadrotors and propose a controller parameters identification approach to obtain better control results.Design/methodology/approachAiming at the problem that quadrotor is susceptible to disturbance in outdoor flight, the improved active disturbance rejection control (IADRC) is applied to design attitude controller. To overcome the difficulty that adjusting the parameters of IADRC controller manually is complex, paired coevolution pigeon-inspired optimization (PCPIO) algorithm is used to optimize the control parameters.FindingsThe IADRC, where nonlinear state error feedback control law is replaced by non-singular fast terminal sliding mode control law and a third-order tracking differentiator is designed for second derivative of the state, has higher control accuracy and better robustness than ADRC. The improved PIO algorithm based on evolutionary mechanism, named PCPIO, is proposed. The optimal parameters of ADRC controller are found by PCPIO with the optimization criterion of integral of time-weighted absolute value of the error. The effectiveness of the proposed method is verified by a series of simulation experiments.Practical implicationsIADRC can improve the accuracy of attitude control of quadrotor and resist external interference more effectively. The proposed PCPIO algorithm can be easily applied to practice and can help the design of the quadrotor control system.Originality/valueAn improved active disturbance rejection controller is designed for quadrotor attitude control, and a hybrid model of PIO and evolution mechanism is proposed for parameters identification of the controller.