Dynamic Tracking Performance Research Articles

Dynamic Power Tracking Performance and Small Signal Stability Analysis of Integrated Wind-to-Hydrogen System

Dynamic Power Tracking Performance and Small Signal Stability Analysis of Integrated Wind-to-Hydrogen System

A Novel Nonsingular Fast Terminal Sliding Mode Control with Sliding Mode Disturbance Observer for Permanent Magnet Synchronous Motor Servo Control

This article proposes a novel nonsingular fast terminal sliding mode control (N-NFTSMC) with a sliding mode disturbance observer (SDOB) for permanent magnet synchronous motor (PMSM) servo control. Firstly, to reduce the chattering issue, a new sliding mode reaching law (NSRL) is proposed for the N-NFTSMC. Secondly, to further improve the dynamic tracking accuracy, we introduce a sliding disturbance observer to estimate unknown disturbances for feedforward compensation. Comparative simulations via Matlab/Simulink 2018 are conducted using the traditional NFTSMC and N-NFTSMC; the step simulation results show that the chattering phenomenon was suppressed well via the N-NFTSMC scheme. The sine wave tracking simulation proves that the N-NFTSMC has better dynamic tracking performance when compared with traditional NFTSMC. Finally, we carry out experiments to validate that the N-NFTSMC adequately suppresses the chattering issue and possesses better anti-disturbance performance.

Radial Basis Function Neural Network and Feedforward Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor

A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.

An artificial potential field approach for event-triggered cooperative control of multiple high-speed trains with free initial states

ABSTRACT In this paper, a multi-algorithm-fusion-based cooperative tracking control strategy is proposed for high-speed trains to achieve the control objectives of reference speed trajectory tracking and adjacent safety distance maintenance simultaneously. First, the multiple high-speed train (MHST) system is formulated as a multi-agent system (MAS) with a virtual leader. Second, a coordination controller that considers the dynamic tracking performance and input saturation is designed by combining the MAS leader-follower consensus algorithm and the improved artificial potential field (APF) method. In addition, within the train-to-train (T2T) communication network, an event-triggered mechanism is introduced in the continuous state transmission process to address the communication channel capacity protection limitation issue. Third, the closed-loop stability of the MHST system is ensured by the comprehensive analysis of a novel log-type Lyapunov function. Finally, the effectiveness of the proposed event-triggered cooperative tracking control strategy is verified through a numerical example of the MHST system on a typical line.

Design and analysis of tracking differentiator based on SO(3)

Motivated by the issue of insufficient dynamic performance and tracking accuracy in SO(3)-based attitude tracking differentiators during large-angle maneuvers and complex trajectory tracking, a novel design approach for a three-degree-of-freedom attitude tracking differentiator within the SO(3) framework is proposed by incorporating second-order system theory and Lie group theory and improving the classical tracking differentiator. The kinematics model and error dynamics model of a rigid body on SO(3) are derived, and a reasonable virtual control input on SO(3) is constructed subsequently in order to achieve better dynamic response and tracking performance. Simulation and experimental results validate that the designed tracking differentiator could realize rapid and smooth convergence during large-angle maneuvers, and the initial large tracking error rapidly drops to near zero in a short period of time; additionally, it can also track expected time-varying curves well in complex trajectory tracking, with initial errors rapidly decreasing and maintaining at normal levels, demonstrating excellent tracking and control capabilities. There are strong application prospects for this new approach in addition to its theoretical significance.

NP-MBO: A newton predictor-based momentum observer for interaction force estimation of legged robots

Swift perception of interaction forces is a crucial skill required for legged robots to ensure safe human–robot interaction and dynamic contact management. Proprioceptive-based interactive force is widely applied due to its outstanding cross-platform versatility. In this paper, we present a novel interactive force observer, which possesses superior dynamic tracking performance. We propose a dynamic cutoff frequency configuration method to replace the conventional fixed cutoff frequency setting in the traditional momentum-based observer (MBO). This method achieves a balance between rapid tracking and noise suppression. Moreover, to mitigate the phase lag introduced by the low-pass filtering, we cascaded a Newton Predictor (NP) after MBO, which features simple computation and adaptability. The precision analysis of this method has been presented. We conducted extensive experiments on the point-foot biped robot BRAVER to validate the performance of the proposed algorithm in both simulation and physical prototype.

Research on linear/nonlinear active disturbance rejection switching control of PMSM speed servo system

An improved linear/nonlinear active disturbance rejection switching control strategy is proposed to enhance the dynamic response and disturbance suppression capabilities of the permanent magnet synchronous motor (PMSM) speed servo system. Firstly, facilitated by a tracking differentiator, rapid setpoint tracking is achieved without speed overshoot, effectively addressing the inherent conflict between fast system response and overshooting. Secondly, weight factors are introduced into the control law, and a novel switching criterion based on the step function is designed, enabling the system to smoothly transition between operating modes in the presence of disturbances with varying amplitudes. This facilitates accurate estimation and swift compensation for disturbances, thereby enhancing control precision and robustness. Additionally, a frequency domain analysis of the extended state observer is conducted, leading to the formulation of a parameter tuning scheme for the controller. Lastly, simulation tests validate the feasibility and effectiveness of this strategy as a PMSM speed controller. The system exhibits superior dynamic tracking and disturbance suppression performance over a wide speed range.

Displacement Self-Sensing Control of Permanent Magnet Assisted Bearingless Synchronous Reluctance Motor Based on Least Square Support Vector Machine Optimized by Improved NSGA-II

In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless synchronous reluctance motor (PMa-BSynRM) control system, a novel displacement self-sensing control method using a least square support vector machine (LSSVM) left inverse system is proposed. Firstly, the working principle of the PMa-BSynRM is introduced and the mathematical model of the PMa-BSynRM is derived. Secondly, the observation principle of the left-inverse system of the PMa-BSynRM is explained and the left-invertibility of the displacement subsystem is proved. Thirdly, the improved NSGA-II algorithm is utilized to optimize the the regularization parameter and the bandwidth of LSSVM, and the displacement self-sensing control system is constructed. The simulations of speed variation and anti-interference are performed, which proves the dynamic tracking performance of the displacement. Finally, the static suspension, speed variation and anti-interference experiments are carried out. The feasibility and reliability of the proposed displacement self-sensing method are verified.

Integrated Application and Optimized Design Strategies of Automatic and Servo Control Systems for Industrial Robots

Abstract At present, industrial robots have been widely used in industrial fields. This topic focuses on optimizing the system of servo control technology for the automatic control of industrial robots. Taking the optimization of robot automatic grasping technology with visual servo as an example, image-based and position-based visual servo control schemes are designed, respectively, and adaptive gain and first-order non-singular linear equations are used for visual servo performance enhancement. Open-loop control and closed-loop control are combined to create the visual servo-based automatic robot grasping docking method. The dynamic tracking effect of visual servo technology and the grasping performance of automatic grasping technology are evaluated through test experiments. The results can be obtained that the image-based and position-based visual servo control schemes can realize better dynamic tracking of objects, and the tracking accuracy is kept within 1.1mm and 8mm in the uniform and variable speed experiments, respectively. The robot’s automatic target grasping error gradually decreases to about 0 with the increase in the number of steps, and the system exhibits high grasping accuracy (over 96.67%) and speed (within 0.80s) at an operating speed lower than 20 m/s. Dynamic tracking performance and target grasping accuracy are certain features of the automatic robot grasping method based on the visual servo in this paper.

An Autonomous Tracking and Landing Method for Unmanned Aerial Vehicles Based on Visual Navigation

In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV’s environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the autonomous tracking and landing of UAVs. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAVs was established. Tracking performance was verified by tracking three trajectories of an unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

A high-precision online compensation method for random errors of optical gyroscope

The traditional Allan variance cannot reflect the time-varying characteristics of random error signals, and the dynamic Allan variance cannot simultaneously guarantee both the signal dynamic tracking performance and confidence of variance estimation due to adopting the sliding window with fixed window length to intercept and segment random error signals. To address these issues, a dynamic Allan variance analysis method with adaptive window length is proposed. Based on the idea of piecewise stability of time-varying random error signals, a sliding window is introduced to intercept and segment the signal to be analyzed along the time axis, and the Allan variance analysis results of all signal segments are synthesized to track and describe the time-varying characteristics of the signal. Then according to the changes in the statistical characteristics of the random error signal, an adaptive factor is constructed to adaptively adjust the length of the sliding window, thereby guaranteeing both the signal dynamic tracking performance and confidence of variance estimation at the same time. On this basis, this method is applied to the online compensation of optical gyroscope random errors, and then a high-precision online compensation method for random errors of optical gyroscope is proposed. The experimental results show that the proposed method can achieve high-precision online compensation for the time-varying random error signals of optical gyroscope measured in complex environment such as uncertainty and noise interference.

Planning and tracking control of full drive-by-wire electric vehicles in unstructured scenario

Full drive-by-wire electric vehicles (FDWEV) equipped with X-by-wire technology can enable independent driving, braking, and steering of each wheel, making them an ideal platform for developing autonomous driving technology. However, designing a robust control algorithm that comprehensively integrates vehicle path planning in a complex and unstructured scenario is a challenging task for FDWEV. To address this issue, this paper (1) proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints; (2) designs curvature calculation (CC-based) and model predictive control (MPC-based) tracking methods with the lateral dynamics model to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on the fuzzy control theory; (3) conducts CarSim-AMESim-Simulink co-simulation with the existence of obstacles. The simulation results show that the proposed two control approaches are practical for classical driving scenarios. Especially the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions.

Fault estimation and self-healing control of discrete-time T-S fuzzy model with sensor and actuator faults based on dual observers

This paper proposes a sensor and actuator fault estimation and self-healing control scheme to address issues related to deteriorating effluent quality, process failures, and safety hazards. Firstly, a discrete T-S fuzzy model of the wastewater treatment system is developed by employing the weighted recursive least squares method, which involves identifying the system based on the Benchmark Simulation Model No. 1. Secondly, in order to overcome the conservativeness problem when estimating sensor and actuator faults with a single observer, a fault estimation method employing dual observers is proposed utilizing the discrete-time T-S fuzzy model established. Thirdly, a fuzzy weighted self-healing controller is designed based on integral sliding mode and state feedback. This controller aims to overcome the aforementioned issues. Finally, Some simulations are provided to ensure the effectiveness of the obtained results. The simulation results show that compared with other methods, the fault estimation method using dual observers achieves shorter error estimation correction time and faster response to sudden fault estimation. Compared with output feedback controller, dynamic output feedback based self-healing controller exhibits superior control performance and better dynamic tracking performance.

Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

This paper presents an intelligent modified predictive control approach with squeezed search space, for tracking control of piezo-actuated nano stage. The model obtained from the gray box neural network is first dynamically linearized to avoid calculation of inverse hysteresis model. The optimum control values of the previous control cycle are used to construct a squeezed search space, which reduces the computation burden and improves the tracking control performance. The effectiveness of the proposed scheme is verified theoretically by deriving a convergence analysis and by experimental results. The results show that the proposed approach significantly improves the dynamic tracking performance for high-frequency reference signals than existing results in the literature.

Research on laser target dynamic tracking system with rotating polarization grating.

A micromechanical target tracking system based on polarization grating (PG) is designed to meet the conformal design of laser communication systems and to realize the lightweight and miniaturization of space laser communication networking. The rotating dual PGs are applied to the dynamic tracking of laser targets for the first time, the relationship between the target position and the dual polarization gratings (PGs) angles is defined, and the PG beam deflection multi-coordinate construction and decoupling are carried out. A dual PGs mathematical model was established, and a controller based on the dual PGs system loop was designed. After calibration and dynamic verification of the dual PGs, the unmanned aerial vehicle (UAV) tracking experiment is conducted for the first time, and the dual axis closed-loop tracking error of the dynamic target is within 300µrad (RMSE). The feasibility of dual PGs tracking formula, the feasibility of laser target fixed-point closed-loop control, and the dynamic closed-loop tracking performance are verified. In engineering applications, the dual PGs tracking system has guiding significance for realizing the lightweight and miniaturization of system integration, as well as the possibility to replace the traditional tracking control system.

Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise

Economic model predictive control and tracking model predictive control are two popular advanced process control strategies used in various of fields. Nevertheless, for a given process, which controller should be chosen to achieve better performance is uncertain when noise exists. To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them and guide the controller selection in this work. First, their controller gains around the optimal steady state are evaluated using the sensitivities of corresponding constrained dynamic programming problems. Second, the controller gains are substituted into the control loop to derive the propagation of process and measurement noise. Subsequently, the Taylor expansion is introduced to simplify the calculation of variance and mean of each variable. Finally, the tracking and economic performance surfaces are plotted and the performance indices are precisely calculated through integrating the objective functions and the probability density functions. Moreover, boundary moving (i.e., back off) and target moving can be pre-configured to guarantee the stability of controlled processes based on the proposed approach. Extensive simulations under different cases prove that the proposed approach can provide useful guidance on performance assessment and controller design.

Research on MPPT control strategy based on the Perturbation observation method

About traditional disturbance observation of larger changes in the external environment, the need for successive change step length, thus slow convergence speed of the shortcomings, based on traditional disturbance observation method, will be the main factors influencing the photovoltaic battery power curve (temperature, light intensity) is introduced into the control strategy of, when external conditions change dramatically, the method can directly reach the optimal duty ratio near, in this way, the slow convergence speed of the traditional perturbation observation method is effectively improved, and the dynamic tracking performance is optimized. In Matlab/Simulink, the improved control strategy model is built to verify that the improved control strategy can significantly improve the convergence speed and efficiency of the photovoltaic power generation system under the working condition of large changes in temperature and external illumination intensity.

Improved ROGI-FLL-Based Sensorless Model Predictive Current Control With MRAS Parameter Identification for SPMSM Drives

This paper presents a sensorless finite control set model predictive current control (FCS-MPCC) for surface-mounted permanent magnet synchronous motor drives based on an improved reduced-order generalized integrator-frequency-locked loop (IROGI-FLL). To simplify the structure of the sensorless FCS-MPCC and avoid control performance degradation caused by the rotor position estimation error, an instantaneous power theory-based given current calculation method is designed. Accordingly, the whole algorithm can be realized without using rotor position. IROGI-FLL is adopted for filtering the estimated back electromotive force and estimating motor speed. To enhance its dynamic speed tracking performance, a speed tracking deviation compensation term is designed for FLL. With the feedforward compensation term, speed static tracking deviation in acceleration and deceleration states can be eliminated. Moreover, to avoid the adverse effect of parameter mismatch on control performance, an incremental adjustable model-based model reference adaptive system (MRAS) is introduced to identify the resistance and inductance parameters. The incremental adjustable model can avoid identification error caused by the rank deficiency issue. Finally, the effectiveness of the proposed method is investigated by experiments.

Stability Analysis and Controller Synthesis

As the application market of voltage source inverters is consistently expanding, new control technologies are also being explored. This article presents a novel PIR controller using the conventional controller as a point of comparison and validated by tests conducted in MATLAB, the results of which led to the conclusion that the PIR controller has superior performance when compared with the traditional PI controller, R controller, and PR controller. These controllers are tested on a voltage source inverter system. In contrast to the conventional controller, the PIR controller demonstrates extra control flexibility through the Bode diagram and offers enhanced steady-state tracking capability. The analysis and comparison show that the PI controller does not have good dynamic tracking performance and cannot suppress the unbalance of secondary AC pulsation. The new control approach suggested in this paper may be implemented in the system to improve the system’s stability and dependability, increase the smoothness of the overall output, and assure the quality of the output.

Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information

With the progress and development of AI technology and industrial automation technology, AI robot dogs are widely used in engineering practice to replace human beings in high-precision and tedious industrial operations. Bionic robots easily produce control errors due to the influence of spatial disturbance factors in the process of pose determination. It is necessary to calibrate robots accurately to improve the positioning control accuracy of bionic robots. Therefore, a robust control algorithm for bionic robots based on binocular vision navigation is proposed. An optical CCD binocular vision dynamic tracking system is used to measure the end position and pose parameters of a bionic robot, and the kinematics model of the controlled object is established. Taking the degree of freedom parameter of the robot’s rotating joint as the control constraint parameter, a hierarchical subdimensional space motion planning model of the robot is established. The binocular vision tracking method is used to realize the adaptive correction of the position and posture of the bionic robot and achieve robust control. The simulation results show that the fitting error of the robot’s end position and pose parameters is low, and the dynamic tracking performance is good when the method is used for the position positioning of control of the bionic robot.