High-Precision Dynamic Tracking Control Method Based on Parallel GRU–Transformer Prediction and Nonlinear PD Feedforward Compensation Fusion

This work presents the design of two control schemes for a Delta/Par4-like parallel robot: augmented PD (APD) controller and augmented nonlinear PD (ANPD) controller. The stability of parallel robot based on nonlinear PD controller has been analyzed and proved based on Lyapunov method. A comparison study between APD and ANPD controllers has been made in terms of performance and accuracy improvement of trajectory tracking. Also, another comparison study has been presented between augmented nonlinear PD (ANPD) controller and nonaugmented nonlinear PD (NANPD) controller in order to show the enhancement of introducing the augmented structure on dynamic performance and trajectory tracking accuracy. The effectiveness of augmented PD controllers (APD and ANPD) and nonaugmented nonlinear PD (NANPD) controller for the considered parallel robot are verified via simulation within the MATLAB environment.

A hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed, In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.

In order to further improve on the static and dynamic performance of the permanent magnet linear synchronous motor speed regulating system, the traditional PID controller is combined with the expert system technology to achieve optimum control of the control systems. In light of defects of the traditional PID controller, the expert system is introduced into the control system of motor on the basis of the incremental PID algorithm, and it is applied to the permanent magnet linear synchronous motor to adjust the motors speed. The simulation results prove the new PID controller combined with the expert system technology has small overshoot, rapid response and good robust stability.

Purpose This study aims to propose a novel identification method to accurately estimate linear and nonlinear dynamics in permanent magnet synchronous linear motor (PMSLM) based on the time-domain analysis of relay feedback. Design/methodology/approach A mathematical model of the PMSLM-based servo-mechanical system was first established, incorporating the aforementioned nonlinearities. The model’s velocity response was derived by analyzing its behavior as a first-order system under arbitrary input. To induce oscillatory dynamics, an ideal relay with artificially introduced dead-time components was then integrated into the servo-mechanism. Depending on the oscillations and the time-domain analysis, nonlinear formulas were deduced according to the velocity response of the servo-mechanism. Afterwards, the unknown model parameters can be solved on account of the cost function which utilizes the discrepancy between nominal position characteristics and temporary position characteristics, both of which are extracted from the oscillations. The proposed recognition method was validated through a two-stage process: (1) numerical simulation and calculation, followed by (2) real-time experimental verification on a direct-drive servo platform. Subsequently, leveraging the identification results, a novel control strategy was developed and its tracking performance was benchmarked against conventional control schemes. Findings Simulation results demonstrate that the proposed method achieves estimation accuracy within 8%. Building on this, a novel control strategy is developed by incorporating both friction pulsation and force pulsation identification results into the feedforward compensator. Comparative experiments reveal that this strategy significantly enhances tracking and positioning performance over traditional control schemes. In a word, this new identification method can be used in different process control and servo control systems. Moreover, parameter auto-tuning, feed forward compensation or disturbance observer can be investigated based on the obtained information to improve the system stability and control accuracy. Originality/value It is of great significance for the performance improvement of rail transit motor control equipment, such as electro-mechanical braking systems. By enhancing the efficiency of motor control, the performance of the product will be more outstanding.

In robot navigation experiment, a localization and motion control system is required to secure the agricultural robot motion in the environment. However, the high cost of localization system and complex structure of motion controller has limited the low cost agricultural mobile robot development. In this paper, a low-cost localization system and simple motion control system is presented. The localization system has been implemented using a dead reckoning method by accessing an incremental encoder’s reading. A simple cascaded motion control system based on proportional feedback kinematics controller and PI based controller was used to control the mobile robot motion. The performances of different turning methods: U turn and a π turn, were compared for lane changing, based on completion time, controller’s error and distance travelled. Simulation test of robot motion was conducted using a Simulink3d animation in MATLAB software. An experimental test in a real greenhouse environment was conducted to verify the simulation performance in motion control and localization system. The experimental and simulation results have shown that a U turn has the best turning performance with 69.1 % better efficiency in experimental mode and it is recommended to be applied in agricultural field.

Motion control is a fundamental technique used in automated mechanical systems. Classically, velocity, force, and impedance are controlled in motion control systems, but simultaneous control is difficult. This article proposes periodic/aperiodic (P/A) motion control based on periodicity and aperiodicity of motion. The P/A motion control separately applies different control methods to P/A motions using P/A velocity and P/A force, which are extracted using a periodic/aperiodic separation filter (PASF) from velocity and force. Accordingly, six types of P/A motion controls are constructed in this article, which correspond to different combinations of the P/A velocity, P/A force, and P/A impedance controls. To construct the P/A motion control systems, acceleration control based on a disturbance observer is used. The ACS, which rejects disturbances, enables the P/A motion control design to ignore disturbances. The experiments were conducted to validate the six P/A motion controls, which simultaneously realized two P/A motion control objectives.

Considering internal parameters variation and external disturbance will greatly affect the performance of permanent magnet linear synchronous motor (PMLSM) control system, the active disturbance rejection control (ADRC) strategy is studied in this paper to enhance the anti-disturbance ability and dynamic performance of the system. Compared with the traditional PI control, ADRC has smaller overshoot, better adaptability, greater robustness to parameters variation and stronger anti-interference ability. The corresponding Simulink model is set up and its Simulink results are analyzed to prove the above advantages.

An intelligent backstepping sliding-mode control system using radial basis function network (RBFN) for a two-axis motion control system using permanent magnet linear synchronous motors (PMLSMs) is proposed. First, single-axis motion dynamics with the introduction of a lumped uncertainty, including cross-coupled interference between the two-axis mechanism, is derived. Then, to improve the control performance in reference contour tracking, a backstepping sliding-mode approach is proposed to compensate for uncertainties occurring in the motion control system. The bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode control system and is difficult to obtain in advance in practical applications. Therefore, an RBFN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping sliding-mode control system. An adaptive learning algorithm, which can learn the parameters of the RBFN online, is derived using Lyapunov stability theorem. The proposed control algorithms are implemented in a TMS320C32 DSP-based control computer, and the motions in the x-axis and y-axis are controlled separately. The simulated and experimental results of circle and four leaves reference contours show that the motion tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference and frictional forces can also be obtained using the proposed controller.

Predictive current control (PCC) has excellent transient performance, which is a research hotspot in permanent magnet linear synchronous motor (PMLSM) control system. However, model mismatch and parameter variation will result in the current static error and even the system instability. Generally, the parameters will change as the motor runs. Therefore, to solve the problem of parameter dependence in PCC and achieve high performance control of the PMLSM control system, a model reference adaptive system (MRAS) which can identify the inductance and permanent magnet (PM) flux linkage simultaneously is proposed and applied. The simulation and experimental results verify the correctness and effectiveness of the researched scheme.

In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> approach for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X-Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC), and a robust controller. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function (UNDF) that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference, and frictional force. Furthermore, a robust controller is designed to deal with the approximation error, optimal parameter vectors, and higher order terms in Taylor series. Besides, the mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> controller is designed such that the quadratic cost function is minimized and the worst case effect of the UNDF on the tracking error must be attenuated below a desired attenuation level. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> /H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> tracking performance so that the stability of the IATCS can be guaranteed. The experimental results confirm that the proposed IATCS grants robust performance and precise dynamic response to the reference contours regardless of external disturbances and parameter uncertainties.

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

Motion control of an autonomous or self-driving car depends intensely on data associated with its lateral and longitudinal motion sensors. These sensor data is used to formulate necessary and appropriate control signal for smooth and stable maneuver, through steering and acceleration subsystems. The work here focus on implementation of such motion control systems on a physical vehicle that is modified to achieve desired autonomous functionality on a predefined path. In addition, a sensor diagnostic system for monitoring the sensor health condition is tested to identify the faulty values in the system.

This paper proposes a segment-powered control system of a linear motor. It is based on long-mover segmented mode and hybrid switching for the segment-powered control requirements of a high-thrust permanent magnet linear synchronous motor. Firstly, the segment-powered principle of the long-mover segmented mode is analyzed. Then the design of the hybrid switching control system is completed. Finally, the experimental verification proves that the control system has good control stability and control accuracy, which can meet the segment-powered control requirements of a high-thrust linear motor.

A robust Sugeno type adaptive fuzzy neural network (RSAFNN) backstepping control for a two-axis motion control system is proposed in this paper. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The single-axis motion dynamics with the introduction of a lumped uncertainty, which includes parameter variations, external disturbances, cross coupled interference between the two PMLSMs and fiction force, is derived first. Then, a backstepping control approach is proposed to compensate the lumped uncertainty occurred in the two-axis motion control system. Moreover, to improve the control performance in the tracking of the reference contours, a RSAFNN backstepping control is proposed where a Sugeno type adaptive fuzzy neural networks (SAFNN) is employed to estimate the lumped uncertainty directly. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer.

Motion control system has complex features, especially the motion planning problems of the system. Motion control system inevitably causes mechanical vibration when it moves to inflection point in high-speed interpolation process. In this paper, different mathematical models of the inflection point are set up. Outer arc method and inner arc method are put forward to optimize the interpolation algorithm at inflection point. And the advantages and disadvantages of the two methods are analyzed. Combining motion control with intelligent control, the selection of the key length at the inflection point based on fuzzy control theory. Finally, to verify the effectiveness of the methods, an open motion control platform was built, the interpolation algorithm is encapsulated in function block, which accords with the motion control standard proposed by PLCopen.