High Control Accuracy Research Articles

An Intelligent Nonlinear Control Method for the Multistage Electromechanical Servo System

In order to meet the requirements of servo systems, including sensitive and rapid adjustment, high control and motion accuracy, and strong working adaptability, in special application fields, such as high thrust and long travel, an adaptive inversion control method is proposed for the lateral force and other nonlinear factors of multistage electromechanical servo system (MEMSS). The position tracking controller of permanent magnet synchronous motor (PMSM), based on an improved adaptive inversion method, was designed and its stability was analyzed, and the Luenberger load torque observer model of PMSM was established. The EMESS simulation model of an adaptive inversion controller was built using the Simulink platform, and the motor multibody dynamics model was established in ADAMS software. Through the joint simulation of Simulink and ADAMS software, the results of EMESS under adaptive inversion controller and traditional PID controller were compared, and the feasibility and reliability of the designed adaptive inversion controller were verified. Finally, the designed controller was tested based on the experimental platform. The experimental results show that the adaptive inversion controller designed in this paper has better robustness and stability than the traditional PID controller.

Magnetic Actuator with Programmable Force Distribution and Self‐Sensing for Bidirectional Deformation Control

AbstractThe realization of bidirectional deformation function is an important symbol of most natural creatures and intelligent flexible robots. Currently, most soft actuators are developed on the basis of transition between two different states, which means difficulties of control during the whole movement. In this work, variable distribution positions of hard magnetorheological elastomers (H‐MREs) in the matrix are used to achieve different magnetic force distributions. The programmable force distribution promotes different deformations of the magnetic actuators, which enlarges the deformation range by 51.69% under the same magnetic field. Pre‐magnetizing the H‐MREs makes them have a certain residual magnetization. Due to the residual magnetization property of H‐MREs, the magnetized actuator can not only be attracted but also be repelled by the applied magnetic field. This bidirectional deformation capability gives the actuators a wider deformation range and greater clamping force, such as the smart gripper for pinching up object. In addition, the actuators are integrated with a flexible sensing layer with high resolution and strong stability for self‐sensing. This kind of force distribution programmable technology and self‐sensing performance have the potential to broaden the application of actuators in medical equipment requiring high control accuracy.

High Efficiency Rate Control for Versatile Video Coding Based on Composite Cauchy Distribution

In this work, we propose a novel rate control algorithm for Versatile Video Coding (VVC) standard based on its distinct rate-distortion characteristics. By modelling the transform coefficients with the composite Cauchy distribution, higher accuracy compared with traditional distributions has been achieved. Based on the transform coefficient modelling, the theoretically derived R-Q and D-Q models which have been shown to deliver higher accuracy in characterizing RD characteristics for sequences with different content are incorporated into the rate control process. Furthermore, to establish an adaptive bit allocation scheme, the dependency between different levels of frames is modelled by a dependency factor to describe relationship between the reference and to-be-coded frames. Given the derived R-Q and D-Q relationships, as well as the dependency factor, an adaptive bit allocation scheme is developed for optimal bits allocation. We implement the proposed algorithm on VVC Test Model (VTM) 3.0. Experiments show that due to proper bit allocation, for low delay configuration the proposed algorithm can achieve 1.03% BD-Rate saving compared with the default rate control algorithm and 2.96% BD-Rate saving compared with fixed-QP scheme. Moreover, 1.29% BD-Rate saving and higher control accuracy have also been observed under the random access configuration.

Geostationary Station-Keeping of Electric-Propulsion Satellite Equipped with Robotic Arms

We propose two approaches based on feedforward control and model-predictive control, respectively, to solve the station-keeping problem of an electric-propulsion geostationary Earth orbit (GEO) satellite, whose thrusters are mounted on two robotic arms on its anti-nadir face. This novel configuration enables a wider range of thrust direction, making it possible to regard the thrust direction as control variables. To solve this control problem, we present the quick feedforward controller (QFFC) and the fuel-optimal model predictive controller (FOMPC). The QFFC is developed based on the analysis of GEO dynamics and the thruster configuration. The FOMPC applies an optimization algorithm to solve the nonlinear model predictive control (NLMPC) problem with the initial value given by the QFFC. Numerical simulations suggest that both controllers could achieve stable station-keeping over multiple objective elements with fewer thrusters and fewer maneuvers. The QFFC has higher control accuracy and lower computational requirements than the FOMPC, whereas the FOMPC could significantly save fuel consumption. The robustness assessment and other discussions of the controllers are also presented.

Theoretical and experimental study of a new defrost control strategy based on gray scale graph theory

Frosting of the evaporators is inevitable during system operation. As such, defrosting must be conducted periodically to ensure stable and efficient operation of the system. However, the current control methods often cause ``mal-defrosting'' due to the complexity and randomness of frosting. To improve the defrosting accuracy of the system a new detection method for the amount of frost was proposed based on the image processing gray scale image theory. Further, the theory of combining the frosting characteristic parameter St with gray contrast C of the frosting image to forecast the critical defrosting time t of the system was established. Through experiments, the best defrosting times (tbest1 and tbest2) of the system were determined, compared with t. Additionally, a frosting error factor (FEF) was identified, compared with the St value of times(tbest1,tbest2 and t). The results show that there was a defrost lag in the defrost time determined by the existing defrost control method,the average time error TEF was by 177.25 s and 321 s. Finally, in view of the illumination effect, the influence of illumination intensity on the accuracy of the proposed method was explored. After changing the illumination intensity,new defrost control method still offers high control accuracy, TEF was by 64 s and 324 s. Overall, from the perspective of engineering applications, the proposed method can reduce the influence of illumination intensity on frosting image recognition, allowing the system to accurately judge the defrosting start point, thereby realising accurate defrosting.

MTPA Control for IPMSM Drives Based on Pseudorandom Frequency-Switching Sinusoidal Signal Injection

Among various maximum torque per ampere (MTPA) control schemes for interior permanent magnet synchronous motor (IPMSM) drives, the signal-injection-based methods exhibit relatively high overall performance due to their high control accuracy and satisfactory dynamic performance. However, the high current spectrum peaks induced by the fixed-frequency signal injection may cause electromagnetic interference and even audible noise problems in applications, such as electric vehicles, vessels, and aircraft. To address this problem, an MTPA control method using pseudorandom frequency-switching sinusoidal signal injection is proposed in this paper. The sinusoidal signals with two different frequencies are randomly injected into the d- and q-axis currents and the MTPA points can be tracked according to the resultant system response. In this way, a high-performance MTPA control can be achieved regardless of motor parameter variations. Since the injection frequency of the proposed method varies randomly, the induced harmonic components in phase currents no longer concentrate at certain frequencies, and the current spectrum peaks caused by signal injection can be reduced accordingly. The experimental results demonstrate the validity of the presented method.

Magnetically Actuated Cell-Robot System: Precise Control, Manipulation, and Multimode Conversion.

Border-nearing microrobots with self-propelling and navigating capabilities have promising applications in micromanipulation and bioengineering, because they can stimulate the surrounding fluid flow for object transportation. However, ensuring the biosafety of microrobots is a concurrent challenge in bioengineering applications. Here, macrophage template-based microrobots (cell robots) that can be controlled individually or in chain-like swarms are proposed, which can transport various objects. The cell robots are constructed using the phagocytic ability of macrophages to load nanomagnetic particles while maintaining their viability. The robots exhibit high position control accuracy and generate a flow field that can be used to transport microspheres and sperm when exposed to an external magnetic field near a wall. The cell robots can also form chain-like swarms to transport a large object (more than 100 times the volume). This new insight into the manipulation of macrophage-based cell robots provides a new concept by converting other biological cells into microrobots for micromanipulation in biomedical applications.

Research on Intelligent Vehicle Trajectory Planning and Control Based on an Improved Terminal Sliding Mode

Aiming at precisely tracking an intelligent vehicle on a desired trajectory, this paper proposes an intelligent vehicle trajectory planning and control strategy based on an improved terminal sliding mold. Firstly, the traditional RRT algorithm is improved by using the target bias strategy and the separation axis theorem to improve the algorithm search efficiency. Secondly, an improved terminal sliding mode controller is designed. The controller comprehensively considers the lateral error and heading error of the tracking control, and the stability of the control system is proven by the Lyapunov function. Finally, the performance of the designed controller is verified by the Matlab-Carsim HIL simulation platform. The test results of the Matlab-Carsim HIL simulation platform show that, compared with the general terminal sliding mode controller, the improved terminal sliding mode controller designed in this paper has higher control accuracy and better robustness.

Integrated artificial neural network prediction model of indoor environmental quality in a school building

Recently, artificial intelligence (AI)-based methods of building control or prediction have been confirmed to show high prediction accuracy and energy-efficient control performance of a single building environment. However, it can be difficult to optimize the indoor environments by previous approaches in a macroscopic system such as a building in which various physical factors interact according to operating HVAC (heating, ventilation, and air-conditioning). According to these reasons, an innovative integrated prediction and control solution of the building environment is required. This study aimed at developing an integrated model based on artificial neural network (ANN) in a school building, which predicts predicted mean vote (PMV) and concentrations of carbon dioxide (CO2), particulate matter 10 μm (PM10), and particulate matter 2.5 μm (PM2.5) after one cycle using current input variables. This model can be developed with training that prevents spurious regression while taking the advantage of supervised learning. In addition, it can overcome the limitations of existing machine learning models and, has utility in predicting the integrated environment of buildings. To achieve this goal, first, the structure of the integrated neural network was designed, and data acquisition was conducted according to the simulation of the target building. Second, the number of hidden layers was searched, which is suitable for predicting each target, by developing initial prediction models. Third, an optimal model was developed using multi-objective genetic algorithm (MOGA). Particularly, the proposed method divides the internal structure of a neural network into independent and detailed prediction models, and it can prevent spurious regression by excluding irrelevant input variables in predicting each target variable. As the accuracy analysis of the optimal prediction model, the root mean square errors (RMSEs) between the predicted data and observed data are as follows: (1) 0.2243 for PMV, (2) 0.8816 for CO2, (3) 0.4645 for PM10, (4) and 0.6646 for PM2.5. Therefore, it was concluded that the proposed model has high accuracy and applicability for building integrated control. Also, this model will be applied to the optimal integrated control of indoor environments and HVAC energy.

Design and Dynamic Performance Research of Underwater Inspection Robots

With the construction of a large number of power plants along the river and the coast, more and more power plants have adopted open water intake in the circulating water pool of the power plant and the number of underwater operations is increasing. In the detection of complex underwater environments, underwater robots have been widely used as a kind of underwater intelligent equipment. However, the current underwater robots generally have problems such as weak endurance, low intelligence, poor detection ability, and large environmental disturbance. The underwater robot designed in this paper is able to accomplish various rotational and flat movement works in the water and also can realize the monitoring of the water environment. In this paper, the underwater dynamic performance of the underwater robot is studied, the coordinate system and motion equation are established, the hydrodynamic analysis is carried out, and the dynamic characteristics of the underwater robot are tested. The experimental results show that the underwater robot has achieved good results in underwater motion control and its dynamic performance, showing the advantages of good robustness and accuracy, high control accuracy, and good dynamic characteristics.

Unusual phase transitions in two-dimensional telluride heterostructures

Phase-change heterostructure (PCH) holds great promise for overcoming the low-precision bottleneck that limits multibit storage and parallel computing in conventional phase-change random-access memory. However, the origin of high-accuracy control of electrical resistance achieved in programming PCH memory devices has yet to be established. Via in situ transmission electron microscopy, here we unveil the unusual microscopic processes during the order–disorder phase transitions driven by electrical pulse in a Sb2Te3/TiTe2 PCH architecture. The template-modulated phase transition is confirmed to be two-dimensional (2D) in nature. The structural transformation path and dynamics in the confined Sb2Te3 sublayers are found to be profoundly changed with respect to those in bulk monolithic Sb2Te3, leading to markedly suppressed amorphous relaxation and substantially reduced crystallization stochasticity, both highly desirable for swift and accurate device operations. Our atomic-scale observations provide direct evidence of, and much-needed insight into, the working mechanisms that may enable superior 2D phase-change electronic devices.

A Lower Limb Rehabilitation Assistance Training Robot System Driven by an Innovative Pneumatic Artificial Muscle System.

This study aims to develop the application of pneumatic artificial muscle (PAM) for a 2-degrees of freedom (2-DOF) lower limb rehabilitation assistance training robot system. The proposed lower limb robot system can be divided into two axes, such as hip joint and knee joint. Each joint contains a pneumatic proportional valve to control a single-PAM with a torsion spring to simulate joint extension and flexion bionics characteristics and achieve a human-like 2-DOF lower limb robot system design and experimental prototype system. By analyzing the kinematics, the derived kinematics conforms to the lower limb motion pattern of the moving human body. Single PAM is difficult to achieve high accuracy control due to the different characteristics between extension and contraction. In our previous research, dual PAMs have been developed to drive a rotational joint which can achieve better control accuracy, however, cannot be suitable for multiaxial robotic design. The mechanism will become very complex and result in lower response and control accuracy. Thus, in this article the novel concept using single PAM with torsion spring was proposed to drive a joint to achieve two-axial robotic design. It has the advantage of multiaxial mechanism design, but the difficulty in joint control due to motion nonlinearity between contraction and extension. The torsion spring can improve motion nonlinearity between contraction and extension partly. Thus, the joint controller using adaptive self-organizing fuzzy sliding mode controller (ASOFSMC) was developed to solve this problem and achieve the required control performance for the joint angle positioning and gait planning control. Through the novel combination of single PAM, torsion spring, and the ASOFSMC joint controller with novel mechanism design and controller design, the two-axial robot mechanism designs and achieves the required control accuracy. The experimental results show that ASOFSMC can effectively control a 2-DOF lower limb robot system, and can modify fuzzy rules online, and adapt to rapid changes in the external environment and load to improve system control performance. The results prove that the proposed innovative single-PAM with a torsion spring and the control strategy can achieve the performance of 2-DOF lower limb rehabilitation assistance training robot system.

A novel rate control algorithm for low latency video coding base on mobile edge cloud computing

The current low-latency video coding rate control algorithm has similar video sequence structure, low rate control accuracy, long control time, etc., which cannot meet the expectations of modern workers. Based on this, this paper proposes a mobile edge cloud computing-based Low-latency video coding rate control algorithm, optimize the original control algorithm, improve its rate control accuracy, etc. By analyzing the relationship between mobile edge computing and cloud computing, combined with edge computing in the context of cloud computing, this paper proposes a mobile edge cloud computing method and designs the mobile edge cloud computing architecture. Low latency video sequences are obtained using mobile edge cloud computing, and a mobile edge cloud computing model is established to control the update rate parameters of delayed video files. According to the principle of rate control, based on joint rate–distortion theory and frame coding complexity calculation model, the low latency video coding rate control is realized. The experimental results show that the byte data volume of the low-latency video coding sequence of the proposed method is 5653 dB. The rate control accuracy can be as high as 99.64%, and the rate control time is 1.37 s. The proposed method has a high similarity of video sequence structure and high rate control accuracy and can effectively shorten the rate control time of low latency video coding.

Unified Accurate Attitude Control for Dual-Tiltrotor UAV with Cyclic Pitch Using Actuator Dynamics Compensated LADRC.

This paper proposes a unified attitude controller based on the modified linear active disturbance rejection control (LADRC) for a dual-tiltrotor unmanned aerial vehicle (UAV) with cyclic pitch to achieve accurate attitude control despite its nonlinear and time-varying characteristics during flight mode transitions. The proposed control algorithm has higher robustness against model mismatch compared with the model-based control algorithms. The modified LADRC utilizes the state feedbacks from the onboard sensors like IMU and Pitot tube instead of the mathematical model of the plane. It has less dependency on the accurate dynamics model of the dual-tiltrotor UAV, which can hardly be built. In contrast to the original LADRC, an actuator model is integrated into the modified LADRC to compensate for the non-negligible slow rotor flapping dynamics and servo dynamics. This modification eliminates the oscillation of the original LADRC when applied on the plant with slow-response actuators, such as propeller and rotors of the helicopter. In this way, the stability and performance of the controller are improved. The controller replaces the gain-scheduling or the control logic switching by a unified controller structure, which simplifies the design approach of the controller for different flight modes. The effectiveness of the modified LADRC and the performance of the unified attitude controller are demonstrated in both simulation and flight tests using a dual-tiltrotor UAV. The attitude control error is less than ±4° during the conversion flight. The control rising time in different flight modes is all about 0.5 s, despite the variations in the airspeed and tilt angle. The flight results show that the controller guarantees high control accuracy and uniform control quality in different flight modes.

Trajectory tracking control of powered parafoil system based on sliding mode control in a complex environment

Powered parafoil systems are nonlinear systems that are easily affected by the surrounding complex environment. In this study, an eight-degree-of-freedom (DOF) model of a powered parafoil was established based on the Lagrange equation and dynamic constraints. A control methodology was then designed based on an extended state observer (ESO) and sliding mode control (SMC). By accurately estimating the influences of the complex environment using the ESO, the estimated disturbances could be compensated for in the SMC. Finally, the simulation results showed that this method could effectively overcome the influence of environmental disturbances and achieve accurate trajectory tracking control. Compared with the traditional proportional–integral–derivative (PID) controller and active disturbance rejection control (ADRC), this method has a higher control accuracy and better robustness.

Discrete‐time extended state observer‐based model‐free adaptive sliding mode control with prescribed performance

AbstractTo tackle the trajectory tracking problem and achieve high control accuracy in many actual nonlinear systems with unknown disturbance, a novel discrete‐time extended state observer‐ (DESO) based model‐free adaptive sliding mode control strategy with prescribed tracking performance is studied, which only relies on the input/output data of the system rather than explicit model information. Firstly, a compact‐form dynamic linearization method is used to reconstruct the discrete‐time nonlinear process, where the time‐varying parameter linearly connected with the control input is obtained by an adaptive method and the unknown nonlinear term is estimated by a DESO. Then, by considering the prescribed performance and using an unconstrained vector transformed from the constrained tracking error, one model‐free sliding mode controller is designed. In addition, a rigorous stability analysis is presented to show the boundedness of the sliding mode function and the prescribed transient‐state and steady‐state performance of the output tracking error. Finally, the simulations with comparing results verify the effectiveness and superiority of the developed control scheme.

Design and analysis of a two-degrees-of-freedom monolithic compliant piezoelectric microgripper

This paper presents the design, modeling, and testing of a compliant two-degrees-of-freedom microgripper using a single piezoelectric actuator. It can move along the x- and y-directions by combining double-leaf bridge and parallelogram mechanisms. An analytical model is established using the finite-element method. The displacement amplification ratio, natural frequency, and output coupling ratio are analyzed using ANSYS software. An experimental test system is built to verify the open-loop performances. Experimental results show that the displacement amplification ratios in the x- and y-directions are 30.8 and 8.6. The first resonant frequencies in the x- and y-directions are 123.3 and 546.9 Hz. If an input displacement of 10 μm is applied, the operating ranges in the x- and y-directions are 0–616.6 μm and 0–51.0 μm, and the gripping force range is 0–25.8 mN. Experimental results validate the feasibility of the theoretical model and simulation analysis. Also, closed-loop trajectory control is conducted to reduce the hysteresis nonlinearity and achieve a high control accuracy for the new microgripper.

SECURITY APPLICATION OF NEURAL NETWORKS UNDER THE INSPECTION OF NONLINEAR DYNAMIC SYSTEMS

Based on the improved BP neural network, this paper establishes an adaptive online controlling model and adopts the model to optimize the controlling accuracies in discrete nonlinear dynamic systems and inverted pendulum systems. To avoid the local minimum problem of the BP neural network’s objective function in the training process, this paper proposes a neural network training method based on the quasi-Newton method (BFGS) optimization algorithm. Compared with other control methods, the neural network-based inverted pendulum control method proposed in this paper has higher control accuracy. Through the control simulation of its power system uses a discrete control method and the control of the inverted pendulum model system, this paper verifies the validity and good control has significantly improved the control method.

S-type speed control curve based on the number of pulses

The stepper motor is a kind of motor that converts electrical pulse signals into corresponding angular displacements or linear displacements, with high accuracy and easy control. According to the characteristics of stepper motor controlled by pulses, this paper designs an S-type speed control method based on the number of pulses, then tests and analyzes it in a motion system which controlled by STM32. The results show that: the change of the speed for the motion system is compared. It is stable and basically conforms to the designed quadratic function curve, and there is no out-of-step phenomenon in the test. While retaining the great stability and acceleration performance of S-type acceleration and deceleration, this control method greatly reduces the calculation difficulty. It is more suitable for microcontrollers and embedded control systems.

Integration modeling and control of a 12-degree-of-freedom macro–micro dual parallel manipulator

Parallel manipulator systems with large workspace and high-accuracy positioning are attractive for a variety of accuracy engineering applications. In this article, a new 12-DOF macro–micro dual parallel manipulator is presented, which comprises a 6-PSU macro symmetrical parallel manipulator and a 6-SPS micro orthogonal parallel manipulator. This novel mechanism can not only realize a large range of movement but also accurate positioning. To achieve high-accuracy control, a macro–micro integrated model of the macro–micro dual parallel mechanism is proposed, including a mechanical system model and drive system model. Aiming at the trajectory planning for a multi-degree-of-freedom (DOF) manipulator, a smooth optimization trajectory planner for the 12-DOF macro–micro dual parallel manipulator is presented to realize smooth trajectory generation. A high-accuracy motion controller combining feedback and feedforward is developed based on the drive system model. Finally, the experiment well validates the correctness of the integrated model and motion control algorithm and reveals that the proposed mechanism is expected to be adopted into micro scale manipulations.