Negative Quadrant Glitch Suppression of Ball-screw-driven Stage by Initial Value Compensation with Additional Input

Ball-screw-driven stages are often used as feed systems of machine tools. A problem of tracking control of the stages is rolling friction, which degrades tracking accuracy around the velocity reversal point and causes a large tracking error called a quadrant glitch. Although the model-based feedforward friction compensation effectively reduces the tracking error, the compensation often results in an inverse response, which is the tracking error in the direction opposite to the quadrant glitch. This study aims to provide an analysis and a suppression approach to the inverse response. First, the inverse response is analyzed through the simulations. Next, a suppression approach of the inverse response is proposed. In the proposed approach, a step input is injected and cancel the inverse response. The proposed approach is evaluated through the simulations and experiments.

A mode-switching controller with initial value compensation for hard disk drive servo control

One of the most significant sources of tracking error for an X-Y table is static friction, a nonlinear disturbance. Static friction, or stiction, occurs when an axis makes a zero velocity crossing, i.e. when an axis reverses direction. In traversing a circular profile for example. an X-Y table encounters zero velocity crossings at ninety degree intervals around the circle, leaving relatively large tracking errors referred to as "quadrant glitches". To eliminate errors caused by stiction, repetitive control, a subclass of learning control, is employed. Experiments are conducted on the X-Y bed of a CNC milling machine. The repetitive controller is implemented digitally in the position servo loop. The velocity loop is closed internally by an analog PI servo which is part of the original controller of the CNC milling machine. The repetitive controller undergoes successive passes in tracking a profile containing a zero velocity crossing. The repetitive controller contains an internal periodic signal generator which receives the error over repetitive cycles and generates a compensating input for cancellation of the repetitive disturbance. Experimental data indicates that perfect velocity tracking can be achieved in twelve cycles or less. Of particular interest is the velocity command input generated by repetitive control. The input contains feedforward information for developing perfect trajectoris at low velocities. An analysis for using the feedforward information for zero velocity crossings at different local accelerations is developed.

This paper proposes a design method for short track-seeking control based on one-degree-of-freedom (ODOF) control and initial value compensation (IVC). IVC utilizes non-zero initial values of the feedback controller to improve the step reference response of the ODOF tracking control system. This makes feedforward control unnecessary to shape the transient response of short track-seeking. As a result, the amount of computation during short track-seeking may be kept minimal. The proposed design method minimizes tracking errors in multi-rate control frame work for a step reference input taking into account the inter-sampling behavior. The effectiveness of the proposed method is shown by simulations.

Lateral Control for UAVs using Sliding Mode Technique

The SynchronyTM Respiratory Tracking System (SRTS) component of the CyberKnife® Robotic Radiosurgery System (Accuray, Inc., Sunnyvale CA) enables real-time tracking of moving targets by modeling the correlation between the targets and external surrogate light-emitting diode (LED) markers placed on the patient's chest. Previous studies reported some cases with respiratory phase shifts between lung tumor and chest wall motions. In this study, the impacts of respiratory phase shifts on the motion-tracking accuracy of the SRTS were investigated. A plastic scintillator was used to detect the position of the x-ray beams. The scintillation light was recorded using a camera in a dark room. A moving phantom moved a U-shaped frame on the scintillator with a 4th power of sinusoidal functions. Three metallic markers for motion tracking and four fluorescent tapes were attached to the frame. The fluorescent tapes were used to identify phantom position and respiratory phase for each video frame. The beam positions collected, when considered relative to the phantom motion, represent the degree of tracking error. Beam position was calculated by adding error value to phantom position. Motions with respiratory phase shifts between the target and an extra stage mimicking chest wall motion were also tested for LED markers. Log files of the SRTS were analyzed to evaluate correlation errors. When target and LED marker motions were synchronized with a respiratory cycle of 4s, the maximum tracking errors for 90% and 95% of beam-on time were 1.0mm and 1.2mm, respectively. The frequency of tracking errors increased when LED marker motion phase preceded target motion. Tracking errors that corresponded to 90% beam-on time were within 2.4mm for 5-15% of phase shifts. In contrast, the tracking errors were very large when the LED marker delayed to the target motions; the maximum errors of 90% beam-on time were 3.0, 3.8, and 7.5mm for 5%, 10%, and 15% of phase shifts, respectively. The patterns of the tracking errors derived from the scintillation light were very similar to those of the correlation data of the SRTS derived from the log files, indicating that the tracking errors caused mainly due to the errors in modeling the correlation data. With long respiratory cycle of 6s, the tracking errors were significantly decreased; the maximum tracking errors for 95% beam-on time were 1.6mm and 2.2mm for early and delayed LED motion. We have investigated the motion-tracking accuracy of the CyberKnife SRTS for cases with the respiratory phase shift between the target and the LED marker. The maximum tracking errors for 90% probability were within 2.4mm when the target delays to the LED markers. When LED marker delays, however, very large tracking errors were observed. With a long respiratory cycle, the tracking errors were greatly improved to less than 2.2mm. Coaching slow breathing will be useful for accurate motion tracking radiotherapy.

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields.

This paper presents sliding mode based lateral control for UAVs using piecewise linear sliding surface. A single linear surface is replaced by piecewise linear sliding surface to ensure the performance during small track errors or divergence and a bounded heading error in the case of large track error, thus avoiding the actuator saturations. Stability of the proposed sliding surface is analyzed with piecewise analytical solution of the proposed sliding surface. The proposed scheme uses only the readily available sensor information that is lateral track error and heading error angle and no succeeding mission information are required here. The proposed scheme is implemented on the 6-degrees-of-freedom (6-dof) simulation and different cases of large and small track errors are simulated in the presence/absence of wind to successfully validate the proposed control scheme.

This paper investigates the trajectory tracking control problem of an underactuated marine surface vehicle (MSV) with stochastic disturbances, where the tracking errors are required to preserve within the predefined constraint bounds. Since the vehicle system is exposed to the environmental stochastic disturbances, the difficulty arisen from Hessian terms induced by stochastic differentiation rule is overcome by introducing a quartic Lyapunov function. To tackle the difficulties caused by underactuation and nonzero off-diagonal terms in the system matrix, we develop a transverse function control approach to introduce an additional control input in backstepping procedure. A tan-type barrier Lyapunov function is introduced to guarantee the satisfaction of the prescribed tracking error constraints. Based on backstepping procedure, barrier Lyapunov function, transverse function control approach, and Lyapunov synthesis, feedback control laws are proposed to ensure that the tracking errors converge exponentially to a small neighborhood of zero, and the constraint requirements on the tracking errors are not violated in the course of operation in probability despite the presence of stochastic disturbances. Simulation results illustrate the performance of the proposed tracking control.

In this paper, a non-time based tracking controller of a nonholonomic mobile robot is first analyzed. Non-time based motion controllers have been successfully applied to many areas such as robot motion control, multi-robot coordination, force control, robotic teleoperation and manufacturing automation. However, by the traditional non-time based motion controller many suffer from oscillations in both the linear and angular velocities when there is a large initial tracking error. In this paper, a traditional non-time based tracking controller is optimized using a genetic algorithm, which is used to generate the model parameters that could guarantee the system stability and convergence of tracking error. Simulations using a nonholonomic mobile robot model with a four degree of freedom are conducted to investigate the performance of the proposed controller. The results using the proposed model is compared to those of the conventional model. Generally the proposed model performs better than the conventional model because the genetic algorithm can provide better parameters to minimize tracking error and the oscillation.

SummaryThis paper studies the problem of stabilizing reference trajectories (also called as the trajectory tracking problem) for underactuated marine vehicles under predefined tracking error constraints. The boundary functions of the predefined constraints are asymmetric and time‐varying. The time‐varying boundary functions allow us to quantify prescribed performance of tracking errors on both transient and steady‐state stages. To overcome difficulties raised by underactuation and nonzero off‐diagonal terms in the system matrices, we develop a novel transverse function control approach to introduce an additional control input in backstepping procedure. This approach provides practical stabilization of any smooth reference trajectory, whether this trajectory is feasible or not. By practical stabilization, we mean that the tracking errors of vehicle position and orientation converge to a small neighborhood of zero. With the introduction of an error transformation function, we construct an inverse‐hyperbolic‐tangent‐like barrier Lyapunov function to show practical stability of the closed‐loop systems with prescribed transient and steady‐state performances. To deal with unmodeled dynamic uncertainties and external disturbances, we employ neural network (NN) approximators to estimate uncertain dynamics and present disturbance observers to estimate unknown disturbances. Subsequently, we develop adaptive control, based on NN approximators and disturbance estimates, that guarantees the prescribed performance of tracking errors during the transient stage of on‐line NN weight adaptations and disturbance estimates. Simulation results show the performance of the proposed tracking control.

This article investigates a novel nonaffine control strategy using neural networks for an air-breathing hypersonic vehicle. Actual actuators are regarded as additional state variables and virtual control inputs are derived from low-computational cost neural approximations, while a new altitude control design independent of affine models is addressed for air-breathing hypersonic vehicles. To further reduce the computational load, an advanced regulation algorithm is applied to devise adaptive laws for neural estimations. Moreover, a new prescribed performance mechanism is exploited, which imposes preselected bounds on the transient and steady-state tracking error performance via developing new performance functions, capable of guaranteeing altitude and velocity tracking errors with small overshoots. Unlike some existing neural control methodologies, the proposed prescribed performance-based nonaffine control approach can ensure tracking errors with preselected transient and steady-state performance. Meanwhile, the complex design procedure of backstepping is also avoided. Finally, simulation results are presented to validate the design.

Aiming at the problems of large tracking error and long tracking time in traditional multiperson target dynamic tracking methods, a new method based on wireless body area network for athlete training multiperson target dynamic tracking is proposed. First, the microinertial sensor in the wireless body area network is used to collect the multiperson image data of the athlete training, and the sparse representation is performed after processing, which improves the reliability of the data and reduces the tracking error. Secondly, the multiperson target dynamic tracking method based on the adaptive search box is used, combined with target isolation and occlusion detection, to judge the athlete’s training target. Finally, the nearest neighbor algorithm is used to construct an adaptive search box to achieve dynamic tracking of multiple targets. Experimental results show that this method can accurately measure the similarity of target features, with small tracking error and short tracking time. The minimum tracking error is only 0.11 frame.

Ocean currents and waves can cause the initial position of an underwater vehicle to deviate from the initial state of the desired trajectory. For such cases, this paper investigates the finite‐time tracking control problem for a vehicle in the presence of large initial tracking errors and external disturbances. A continuous finite‐time tracking control scheme is developed for this scenario based on an improved nonsingular terminal sliding mode surface modified by a piecewise function. Compared to the alternative approaches, a method based on error transformation is added to the sliding mode surface, resulting in improved tracking accuracy in the steady state. Then, to reduce the effect of large initial tracking error on the control input signals, an exponential decay function about the initial location of the vehicle is developed to obtain the control law by combining it with a strictly generalized saturation function. In contrast to alternative approaches, a nonlinear structure is used to counteract the approximation error of the neural network used in the modeling of the dynamics. Moreover, it is shown that the tracking error converges to a small neighborhood of zero in a fixed time. Finally, the new control scheme's effectiveness is verified by simulation studies on an underwater vehicle model and the results compared to other existing control designs.

We present an analytical design and experimental verification of trajectory tracking control of a 7-DOF robot manipulator, which achieves convergence of all tracking errors to the origin within a finite terminal time, also referred to as the “settling time.” A key feature of this control strategy is that the settling time is explicitly assigned by the control designer to a value desired, or “prescribed” by the user and that the settling time is independent of the initial conditions and of the reference signal. In order to achieve this beneficial property with the controller, a scaling of the state by a function of time that grows unbounded toward the terminal time is employed. Through Lyapunov analysis, we first demonstrate that the proposed controller achieves regulation of all tracking errors within the prescribed time as well as the uniform boundedness of the joint torques, even in the presence of a matched, nonvanishing disturbance. Then, through both simulation and experiment, we demonstrate that the proposed controller is capable of converging to the desired trajectory within the prescribed time, despite large distance between the initial conditions and the reference trajectory, i.e., in spite of large initial tracking errors, and in spite of a sinusoidal disturbance being applied in each joint.