Reduce Tracking Errors Research Articles

PD with sliding mode control for trajectory tracking of robotic system

Good tracking performance is very important for trajectory tracking control of robotic systems. In this paper, a new model-free control law, called PD with sliding mode control law or PD-SMC in short, is proposed for trajectory tracking control of multi-degree-of-freedom linear translational robotic systems. The new control law takes the advantages of the simplicity and easy design of PD control and the robustness of SMC to model uncertainty and parameter fluctuation, and avoid the requirements for known knowledge of the system dynamics associated with SMC. The proposed control has the features of linear control provided by PD control and nonlinear control contributed by SMC. In the proposed PD-SMC, PD control is used to stabilize the controlled system, while SMC is used to compensate the disturbance and uncertainty and reduce tracking errors dramatically. The stability analysis is conducted for the proposed PD-SMC law, and some guidelines for the selection of control parameters for PD-SMC are provided. Simulation results prove the effectiveness and robustness of the proposed PD-SMC. It is also shown that PD-SMC can achieve very good tracking performances compared to PD control under the uncertainties and varying load conditions.

PROBING THE POTENTIAL FOR FUNCTIONAL RECOVERY IN PATIENTS WITH NEUROMYELITIS OPTICA USING VISUOMOTOR PRACTICE

ObjectiveNeuromyelitis optica (NMO) is characterised by episodes of optic neuritis and transverse myelitis. Despite therapeutic advances, treatments aimed at reducing disease activity have little impact on disabilities and much of...

Tracking error reduction in CNC machining by reshaping the kinematic trajectory

In this paper, a method of reducing the tracking error in CNC machining is proposed. The structured neural network is used to approximate the discontinuous friction in CNC machining, which has jump points and uncertainties. With the estimated nonlinear friction function, the reshaped trajectory can be computed from the desired one by solving a second order ODE such that when the reshaped trajectory is fed into the CNC controller, the output is the desired trajectory and the tracking error is eliminated in certain sense. The proposed reshape method is also shown to be robust with respect to certain parameters of the dynamic system.

Effect of audio instruction on tracking errors using a four‐dimensional image‐guided radiotherapy system

The Vero4DRT (MHI‐TM2000) is capable of performing X‐ray image‐based tracking (X‐ray Tracking) that directly tracks the target or fiducial markers under continuous kV X‐ray imaging. Previously, we have shown that irregular respiratory patterns increased X‐ray Tracking errors. Thus, we assumed that audio instruction, which generally improves the periodicity of respiration, should reduce tracking errors. The purpose of this study was to assess the effect of audio instruction on X‐ray Tracking errors. Anterior‐posterior abdominal skin‐surface displacements obtained from ten lung cancer patients under free breathing and simple audio instruction were used as an alternative to tumor motion in the superior‐inferior direction. First, a sequential predictive model based on the Levinson‐Durbin algorithm was created to estimate the future three‐dimensional (3D) target position under continuous kV X‐ray imaging while moving a steel ball target of 9.5 mm in diameter. After creating the predictive model, the future 3D target position was sequentially calculated from the current and past 3D target positions based on the predictive model every 70 ms under continuous kV X‐ray imaging. Simultaneously, the system controller of the Vero4DRT calculated the corresponding pan and tilt rotational angles of the gimbaled X‐ray head, which then adjusted its orientation to the target. The calculated and current rotational angles of the gimbaled X‐ray head were recorded every 5 ms. The target position measured by the laser displacement gauge was synchronously recorded every 10 msec. Total tracking system errors (ET) were compared between free breathing and audio instruction. Audio instruction significantly improved breathing regularity (p < 0.01). The mean ± standard deviation of the 95th percentile of ET (E95T) was 1.7 ± 0.5 mm (range: 1.1–2.6 mm) under free breathing (E95T,FB) and 1.9 ± 0.5 mm (range: 1.2–2.7 mm) under audio instruction (E95T,AI). E95T,AI was larger than E95T,FB for five patients; no significant difference was found between E95T,FB and ET,AI95(p = 0.21). Correlation analysis revealed that the rapid respiratory velocity significantly increased E95T. Although audio instruction improved breathing regularity, it also increased the respiratory velocity, which did not necessarily reduce tracking errors.PACS number: 87.55.ne, 87.57.N‐, 87.59.C‐,

유압실린더-토글 서보 메카니즘의 모델링 및 운동제어

This paper presents a robust motion tracking control of a cylinder-toggle servomechanism for injection molding machines. Virtual design model has been developed for a five-point type toggle mechanism. A sliding function is defined and combined with PID control to accommodate mismatches between the real plant and the linear model used. From tracking control simulations, it is shown that significant reduction in position tracking error is achieved with clamping force build-up through the use of proposed control scheme.

Predictive-repetitive control with constraints: From design to implementation

This paper addresses the design and implementation of predictive-repetitive control systems, including the case when constraints are imposed. The approach is based on a frequency response decomposition technique that detects the dominant frequency components of the reference signals and embeds them in the predictive-repetitive controller. Analysis and design make use of the frequency response of the closed-loop system and the choice of the structure of the repetitive controller is considered as a balance between the reduction of tracking errors and minimization of the effects on performance of unwanted elements, such as model uncertainty. The implementation of operational constraints on the amplitudes of the control signals, their increments and on the outputs is considered using an on-line solution constructed using quadratic programming and the identification of active constraints. Experimental results from application to a 2 degree-of-freedom robotic system are used to illustrate the design and implementation procedures developed.

Electrohydraulic Control Using Neural MRAC Based on a Modified State Observer

A new model reference adaptive control design method using neural networks that improves both transient and steady-state performance is proposed in this paper. Stable tracking of a desired trajectory can be achieved for nonlinear systems having significant uncertainties. An uncertainty-state observer structure is designed to achieve desired transient performance. The neural network adaptation rule is derived using Lyapunov theory, which guarantees stability of the error dynamics and boundedness of the neural network weights. An extra term is added in the controller expression to introduce a “soft-switching” sliding mode that can be used to reduce tracking error. The proposed design method is applied to control the velocity and position of an electrohydraulic piston comprising industrial components and having a limited bandwidth, and experimental results demonstrate its effectiveness as compared to commonly used controllers.

Control System Development for Pneumatic Artificial Muscle-Driven Active Rotor Systems

An active trailing-edge flap system for helicopter rotor blades using pneumatic artificial muscles was investigated, with the goal of generating flap deflections suitable for both vibration mitigation and primary flight control. Prior work with these actuators showed that large deflections were possible under full-scale conditions. This study developed and demonstrated an inner loop feedback controller that enables the flap to track the complex waveforms required to achieve its goal. Control algorithm development began with proportional feedback, but required the addition of a dead time compensator to reduce tracking error and increase bandwidth. The dead time compensator initially had a fixed value, though this transitioned to a variable to cope with potential changes in the dead time due to changing operating conditions. In this case, the system can continuously adjust the dead time estimate from the position error and predict future tracking error using that estimate, and adjust the control action accordingly. This approach requires no system model and no a priori information about the desired command signal. As such, it is well suited to the active rotor problem. Testing of this controller on single sine waves and complex waveforms composed of a sum of the first four odd harmonics of the rotor frequency showed good tracking for all conditions.

A survey on non-linear gain scheduling design control for continuous and discrete time systems

Recently published researches on trajectory tracking methods are reviewed, and suggestions for further control aims are offered. Both methods for handling reduced tracking error and approaches for treating the case of periodical output trajectories in the case of continuous and discrete non-linear systems are discussed. Computational algorithm aspects are emphasised throughout. The main contribution of this paper is the development of a theoretical framework for different strategies of non-linear gain scheduling control. We demonstrate that one can achieve an accurate trajectory tracking in both cases of discrete and continuous system modelling. The discussion is investigated via the study of a continuous stirred-tank reactor (CSTR) as a second order non-linear model. Finally, interesting conclusions are so derived.

Tracking control of piezoelectric stack actuator using modified Prandtl–Ishlinskii model

This article presents the development of Prandtl–Ishlinskii hysteresis model and tracking control of piezoelectric stack actuator with severe hysteresis. Classical Prandtl–Ishlinskii model is a linearly weighted superposition of many backlash operators with different threshold and weight values, which inherits the symmetric property of the backlash operator at about the center point of the loop formed by the operators. To describe the asymmetric hysteresis of piezoelectric stack actuators, two modified operators were developed, one for ascending branches and another for descending branches. Based on this modified model, a feedforward controller was designed to compensate the hysteresis. Since the modified model describes the inverse of hysteresis, the feedforward controller and the hysteresis of piezoelectric stack actuator canceled each other. To attenuate the creep effect and reduce tracking error, a feedback controller was proposed to work with the feedforward controller. Experimental results show that this control scheme that combines feedforward and feedback controllers greatly improves the tracking of the piezoelectric actuator and the error is less than 0.15 µm.

Quaternion based optical flow estimation for robust object tracking

We propose a quaternion optical flow algorithm for robust object tracking. Unlike previous works of color optical flow methods that treat color as separating channels, the proposed algorithm exploits quaternion representation of color and processes color as a holistic signal. In this way, it enables more accurate flow estimation at the pixel locations of spatial color variations, and reduces tracking errors by leaving more features points at their correct locations on the target. For successful and efficient object tracking, we also proposed a novel type of quaternion color corners that are reliable features during tracking. Together with grayscale corners, they form a good feature point set, especially when used with the proposed quaternion optical flow algorithm. We conduct a quantitative evaluation on publicly available dataset to verify the efficacy of the proposed algorithm. And object tracking experiments demonstrate that robust tracking can be achieved for real-time applications.

Hysteresis compensation and high-performance tracking control of piezoelectric actuators

This paper presents a repetitive control-based independent design method for high-performance tracking control of piezoelectric actuators. This work applies an inverse Prandtl–Ishlinskii hysteresis model as a non-linear feedforward compensator to compensate for the hysteresis effect, and validates its inverse model by open-loop compensation. To achieve precision tracking control, the current study designs and implements the feedback control using a prototype repetitive control algorithm, integrating the inverse linear plant model-based feedforward compensation and hysteresis compensation methods to further reduce tracking error. Experimental results and performance analysis demonstrate that using the control structure further improves tracking performance.

The Impact of Securities Lending on Index Fund Performance

Managers of index funds face competitive pressures to replicate their respective benchmarks as close as possible or risk the loss of investors to competing index funds. As a result, these managers have incentive to take actions to reduce any tracking error. One particular way index funds reduce tracking error is through securities lending activities. The authors examine the impact of securities lending activities on the return performance of index funds and document that reliance on securities lending activity as a means of reducing tracking error has increased significantly over time.

Modified Rake architecture for GNSS receiver in a multipath environment

Multipath is one of the major error sources in satellite-based navigation systems. This paper proposes a new anti-multipath architecture, named Modified Rake (MRake), for Global Navigation Satellite System (GNSS) receiver. The architecture derives from RAKE receiver in communication system. It can track direct-path and multipath components separately from the received signal without consumption of much hardware resources or complex calculation. The MRake architecture uses an Amplitude Estimation Unit (AEU) to estimate the amplitude of each component, and a noncoherent DLL to adaptively control its time-delay. Since the receiver only uses the direct-path signal to calculate the user's position, the tracked multipath components are subtracted from the composite signal to reduce the multipath effect, and thus to reduce tracking error. MRake receiver shows better performance or consumes much fewer resources than traditional anti-multipath techniques.

이중 학습에 의한 선형동기모터의 위치제어

This paper proposes PID and RIC (Robust Internal-loop Compensator) based motion controller using dual learning algorithm for position control of linear synchronous motor respectively. Its gains are auto-tuned by using two learning algorithms, reinforcement learning and neural network. The feedback controller gains are tuned by reinforcement learning, and then the feedforward controller gains are tuned by neural network. Experiments prove the validity of dual learning algorithm. The RIC controller has better performance than does the PID-feedforward controller in reducing tracking error and disturbance rejection. Neural network shows its ability to decrease tracking error and to reject disturbance in the stop range of the target position and home.

Real-time tumor tracking with an artificial neural networks-based method: A feasibility study

The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required.We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose.Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz.The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.

Chattering reduction on the dynamic tracking control of a nonholonomic mobile robot using exponential sliding mode

This paper considers the problem of improving chattering reduction and trajectory tracking along a desired trajectory for a mobile robot. It is proposed that exponential sliding mode control is an effective solution to reduce chattering for the trajectory tracking of a nonholonomic mobile robot. Compared to conventional and second-order sliding modes, the developed sliding mode control reduces chattering and delivers a high dynamic tracking performance in a steady state mode. The developed algorithm instructs the robot to keep moving continuously on the desired trajectory while reducing tracking errors. Experimental results on a wheeled mobile robot are presented to demonstrate the performance of the exponential sliding mode controller algorithm compared to both conventional and second-order sliding mode algorithms.

The Singular Value Filter: A General Filter Design Strategy for PCA-Based Signal Separation in Medical Ultrasound Imaging

A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB with over 40% reduction in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients.

파티클 필터 알고리즘을 이용한 다기능레이더 표적 추적 필터 설계

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), <TEX>${\alpha}-{\beta}$</TEX> filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.

Indirect Adaptive Robust Control of Hydraulic Manipulators With Accurate Parameter Estimates

In a general direct adaptive robust control (DARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in presence of uncertain nonlinearities and parametric uncertainties. Such a direct algorithm suffers from lack of modularity, controller-estimator inseparability, and poor convergence of parameter estimates. In the DARC design the parameters are estimated by gradient law with the sole purpose of reducing tracking error, which is typical of a Lyapunov-type design. However, when the controller-estimator module is expected to assist in secondary purposes such as health monitoring and fault detection, the requirement of having accurate online parameter estimates is as important as the need for the smaller tracking error. In this paper, we consider the trajectory tracking of a robotic manipulator driven by electro-hydraulic actuators. The controller is constructed based on the indirect adaptive robust control (IARC) framework with necessary design modifications required to accommodate uncertain and nonsmooth nonlinearities of the hydraulic system. The online parameter estimates are obtained through a parameter adaptation algorithm that is based on physical plant dynamics rather than the tracking error dynamics. While the new controller preserves the nice properties of the DARC design such as prescribed output tracking transient performance and final tracking accuracy, more accurate parameter estimates are obtained for prognosis and diagnosis purpose. Comparative experimental results are presented to show the effectiveness of the proposed algorithm.