Reduced-order asymptotic observer of nonlinear friction for precise motion control

Chapter 18 - A Small Reaction Manipulator for Maneuvering a GPR Sensing Head

Precise tension control of a dancer with a reduced-order observer for roll-to-roll manufacturing systems

Motion control of chemically powered colloidal motors.

The interest in the study of friction in control engineering has been driven by the need for precise motion control in most of industrial applications such as machine tools, robot systems, semiconductor manufacturing systems and Mechatronics systems. Friction has been experimentally shown to be a major factor in performance degradation in various control tasks. Among the prominent effects of friction in motion control are: steady state error to a reference command, slow response, periodic process of sticking and sliding (stickslip) motion, as well as periodic oscillations about a reference point known as hunting when an integral control is employed in the control scheme. Table 1 shows the effects and type of friction as highlighted by Armstrong et. al. (1994). It is observed that, each of task is dominated by at least one friction effect ranging from stiction, or/and kinetic to negative friction (Stribeck). Hence, the need for accurate compensation of friction has become important in high precision motion control. Several techniques to alleviate the effects of friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; Tjahjowidodo, 2004; Canudas, et.al., 1986). One of the successful methods is the well-known model-based friction compensation (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, the effect of the friction is cancelled by applying additional control signal which generates a torque/force. The generated torque/force has the same value (or approximately the same) with the friction torque/force but in opposite direction. This method requires a precise modeling of the characteristics of the friction to provide a good performance. Hence, in the context of model-based friction compensation, identification of the friction is one of the important issues to achieve high performance motion control. However, as discussed in the literatures, several types of friction models have been identified (Armstrong et al., 1994; Canudas et. al., 1995; Makkar et. al., 2005) and classified as static or dynamic friction models. Among the static models are Coulomb friction model, Tustin model, Leuven model, Karnop model, Lorentzian model. Meanwhile Dahl model, Lugre model, Seven parameters model, and the most recent Generalized Maxwell-Slip (GMS) model, are among the dynamic friction models (Tjahjowidodo, 2004). The static friction model is simple and easy in the identification process, however using such model

The flexible manipulator is widely used in many fields due to its advantages of light weight, fast response, and low energy consumption. However, due to the flexible structure of the flexible manipulator, it is easy to generate tip vibration, and difficult to achieve high-speed and high-precision motion control. In this paper, the dynamics model of the flexible manipulator system with controllable damping is established based on the Lagrange method and damping force constraining equation. Using the singular perturbation principle, the dynamics model is decomposed into fast and slow subsystems, where the slow subsystem represents the large-scale rigid motion, and the fast subsystem represents the small-scale vibration. A two-time scale composite control strategy is proposed, which consists of an adaptive robust controller for the slow system and a LQR controller for the fast system, to achieve tip vibration suppression and precise motion control. Through simulation, the effectiveness of the two-time scale control strategy of the flexible manipulator system with controllable damping is proved.

Curative-intent radiotherapy for multiple lung metastases with more than 10 lesions is limited by lung dose constraints and respiratory motion. Carbon ion radiotherapy (CIRT) leverages the Bragg peak for precision and hypofractionation, while high-frequency oscillatory ventilation (HFOV) minimizes motion under anesthesia. This study evaluates the feasibility of combining CIRT and HFOV in treating ≥15 lung metastases. Two patients received single-fraction CIRT (50 Gy(RBE)) targeting all lesions. Case 1 (16 metastases of bilateral lung, hepatocellular carcinoma) and Case 2 (15 metastases of the left lung, colorectal adenocarcinoma) underwent four-dimensional (4D)-CT simulation with HFOV and CIRT in one single dose. Diaphragm motion reduced from 1.15 and 3.09 cm for Cases 1 and 2 (free-breathing) to 0.1 and 0.2 cm, respectively. Lung dose constraint parameters (V30: 3.57% to 12.35%; V20: 6.06% to 15.3%) met safety thresholds. Both patients achieved 22-month survival with continued local control (complete response in Case 1; partial response in Case 2). Case 1 developed grade II pneumonitis (resolved with steroids) and asymptomatic fibrosis; Case 2 had no toxicity. Some new lesions were managed with additional CIRT. Outcomes compare favorably to published photon trials in which up to 10 lung lesions were irradiated, underscoring CIRT's precision and HFOV's motion control. Success relied on three factors: CIRT's dosimetric advantages, HFOV-driven motion reduction, and anesthesia-enabled tolerability of the longer irradiation time span. Despite cost barriers, CIRT seems to extend survival in patients with multiple lung metastases that were refractory to conventional local therapy. This approach demonstrates the feasibility ofirradiating more than 15 lesionsin patients with polymetastatic lung disease, offering durable control with manageable toxicity. Larger studies are needed to validate its role in multimodal therapy.

This article presents a precise motion control strategy for electric push rod (EPR) using active disturbance rejection control (ADRC). A complete mathematical model is developed firstly, which integrating the dynamics of the DC motor and transmission system to characterize dynamic behavior of the EPR. A linear ADRC controller is then designed to address the system’s dynamics and external disturbances, with rigorous stability analysis provided to demonstrate its theoretical robustness. Finally, experimental validation is conducted through comparative simulations and physical tests, respectively. The simulation results validate the effectiveness of ADRC. The physical validation is performed on a dedicated test platform under three critical schemes: (1) sine-wave tracking (for dynamic response and multi-frequency disturbance), (2) complex periodic wave tracking (for robustness performance), and (3) step-wave regulation (for transient performance). Comparative results demonstrate that compared with the classical PID control, using ADRC control reduces the RMSE of the three trajectories by 17.91%, 16.39%, and 30.64%, respectively, while reducing the settling time of step response by 30%. The study demonstrates that ADRC-based control significantly enhances the performance of EPR in precision applications while maintaining system stability under varying operational conditions.

Motion control of mechatronic systems with uncertainties and physical constraints, while ensuring robustness and achieving better performance, such as high tracking accuracy and fast response, has always been a hot topic. However, the most current related works only focus on how to guarantee system stability under constraints, and few consider comprehensive performance. This paper investigates gated recurrent unit (GRU)-based compensation adaptive robust control (ARC) for uncertain linear motor–driven stage system with state and input constraints. To achieve rapid and precise motion control, a dual-loop control structure is employed, where GRU and ARC are the outer loop and the inner loop, respectively. First, the ARC control law is used to deal with the parameters uncertainty and external disturbances in the system, which further improves the tracking accuracy. A GRU neural network is then constructed and capable of implementing precise prediction ahead of the actual system output. Through choosing suitable loss function and training model, it can effectively minimize prediction error under state and input constraints. Comparative experiment results demonstrate the superiority and validity of the proposed scheme on the basis of GRU and ARC.

Expanding an unmanned aerial vehicle (UAV) with an equipped robotic arm increases the ability of the UAV to actively interact with the environment. During the process of active tasks, precise motion control under model uncertainties and external disturbances is a challenging issue. To solve this issue, an active modeling based control scheme is proposed in this paper. In this scheme, the aerial manipulator system (AMS) is modeled with consideration of various uncertainties represented by the model error and the process noise. The model error caused by the gravity and movement of the manipulator is estimated by the unscented kalman filter (UKF). The estimation is further integrated into a nominal controller to achieve high-precision motion control. Detailed experimental results on the self-bulit testbed illustrate the effectiveness and good performance of the proposed method.

Miniaturized helical swimmers we call artificial bacterial flagella (ABFs) are promising for biomedical applications, such as targeted drug delivery and cell manipulation. ABFs possess several advantageous characteristics, such as high swimming velocity and precise motion control, indicating their potential for diverse applications. The velocity and the propulsive force of ABFs can be controlled by the input frequency of the rotating magnetic field that powers them. In this paper the motion control of two types of ABF, i.e. nanobelt based ABFs (NB-ABFs) and nanowire based ABFs (NW-ABFs), is reported. For NB-ABFs the swimming behavior at frequencies near the step-out frequency is investigated. Experimental results indicate that the non-ideal swimming motion of the ABF at frequencies higher than the step-out frequency is promising for the decoupling of specified ABFs from a swarm. The NW-ABF is achieved by magnetic assembly of two bent Ni nanowires. The results show that the motion of the NW-ABF is controllable by a rotating magnetic field.

This paper presents the control platform design for a self-developing mobile robot. In addition to the introduction of the robot's whole framework its control architecture, motion controller design and velocity loop servo controller are also described. Modular, open, distributed control architecture is utilized in the robot as well as advanced intelligent control strategies such as cross-coupling control and fuzzy logic control. Therefore, the robot has quick reactivity, precise motion control and excellent robustness under various dynamic environments.

Robotic force control is a core research direction in robotics technology. For tasks such as polishing and assembly, it requires not only precise motion control but also compliant force control by adjusting output force and torque. Traditional impedance control can ensure accuracy in a single environment, but it is limited by the difficulty in accurately acquiring parameters in complex environments; although variable impedance control can adapt to the environment, it faces challenges in control law design. This paper integrates the extended Twin Delayed Deep Deterministic Policy Gradient (ex-TD3) algorithm with impedance control, proposing an ex-TD3-based intelligent impedance control optimization algorithm. A Python-MATLAB/SimMechanics co-simulation platform is established to verify its effectiveness. The experiment adopts a scenario where the robot moves linearly along the z-axis, with fixed environmental stiffness and the desired tracking force changing in stages over time. The model training is iterated for 300 episodes, after which the reward tends to stabilize and the control strategy converges. Experimental results show that, compared with the traditional adaptive impedance control algorithm, the proposed algorithm in this paper exhibits faster force response, smaller force fluctuations during force step changes, smoother force output throughout the process: the maximum overshoot of force control decreases from 6.21N to 1.33N (a reduction rate of 77.63%), and the maximum position tracking error is reduced by 69.5%. With the above optimized design, this study significantly improves the energy efficiency and long-term operational sustainability of mechatronic systems; its precise control of force overshoot and position tracking error effectively reduces mechanical losses and unnecessary energy consumption during the system’s dynamic interaction with the environment.

Understanding the evolution and tunable control of skyrmion dynamics is crucial for the effective development of next generation Racetrack Memory (RM) devices. In this study, we present the micromagnetic simulation demonstrating precise control of skyrmion motion in a magnetic nanotrack composed of two (three) narrow and a wider nanochannels under the influence of spin transfer torque (STT) and out of plane bias field. Our results reveal that out of plane magnetic field assists in efficient conversion of Domain wall (DW) pair to skyrmion and results in the achievement of uniform velocity for each skyrmion. Furthermore, a thorough analysis of individual and total energy terms during the DW pair to skyrmion conversion provides detailed insight into the role of bias field in overcoming the pinning potential at the junction of the narrow and wide nanochannel. The ability to precisely control the skyrmion motion stabilized from a DW pair under the influence of an external bias field is of significant importance for developing skyrmion based memory devices.

Simple harmonic waves and synthesized simple harmonic waves are widely used in the test of instruments. However, because of the errors caused by clearance of gear and time-delay error of FPGA, it is difficult to control servo electric cylinder in precise simple harmonic motion under high speed, high frequency and large load conditions. To solve the problem, a method of error compensation is proposed in this paper. In the method, a displacement sensor is fitted on the piston rod of the electric cylinder. By using the displacement sensor, the real-time displacement of the piston rod is obtained and fed back to the input of servo motor, then a closed loop control is realized. There is compensation of pulses in the next period of the synthetic waves. This paper uses FPGA as the processing core. The software mainly comprises a waveform generator, an Ethernet module, a memory module, a pulse generator, a pulse selector, a protection module, an error compensation module. A durability of shock absorbers is used as the testing platform. The durability mainly comprises a single electric cylinder, a servo motor for driving the electric cylinder, and the servo motor driver.

In this paper, a model reference adaptive control (MRAC) scheme is applied for the precise and robust motion control of a pneumatic system with load variation. The reference model for MRAC is designed systematically using linear quadratic Gaussian control with loop transfer recovery (LQG/LTR). The sigmoid function of inverse velocity is used to compensate for the nonlinear friction force between the sliding parts. The experimental results show that MRAC effectively overcame the limit of the PID controller when there was unknown disturbance, including abrupt load variation and model uncertainty in the pneumatic control system.