Reduce Tracking Errors Research Articles

Curvature-Based Ground Vehicle Control of Trailer Path Following Considering Sideslip and Limited Steering Actuation

In this paper, a curvature-based control method capable of both forward and backward path following is developed for off-axle hitching trailers. Compared with earlier methods, this controller handles varying path curvature better while also being easy to implement and tune. The controller also addresses two major issues that deteriorate tracking accuracy: limited steering actuation and sideslip. An active speed limiter brakes the vehicle when the steering angle error is too high and therefore allows the same set of control gains to work across a relatively large range of reference speeds without significantly increasing lateral error or losing stability, which further simplifies controller tuning. An extended Kalman filter (EKF) sideslip estimator is designed to allow sideslip compensation, which considers the varying noise magnitude in GPS measurements caused by varying vehicle speeds so that the braking maneuver will not significantly deteriorate the estimations. To our knowledge, this paper presents the first publication of these two algorithms with trailer control. This method is compared with results from two earlier hitch-angle-based control methods in simulation and experiment. The active speed limiter and the EKF are also implemented on these control methods for fair comparison, during which it is found that the two algorithms can directly work with other control methods and benefit their performance. The results show the curvature-based controller provides better maneuverability and reduced tracking error.

Adaptive Fractional Sliding Mode Control of Active Power Filter Based on Dual RBF Neural Networks

This paper presents an adaptive fractional sliding mode control scheme based on dual radial basis function (RBF) neural networks (NNs) to enhance the performance of a three-phase shunt active power filter (APF), where a conventional integer-order sliding surface is changed into a fractional-order one to speed up the system response and optimize the control performance. Furthermore, the control scheme adopts a class of dual RBF NNs, in which the network weights can be updated online to approximate the nonlinear system functions and the upper bound of estimated disturbances, respectively, improving the system stability and robustness. Meanwhile, the adaptive control laws obtained by Lyapunov analysis can guarantee the system a stable operation. Finally, by comparing with the integer-order control strategy, the simulation results verify that this proposed controller has a better performance in the suppression of the harmonic, elimination of system uncertainties, and the reduction of current tracking error.

Observer-based linear parameter varying H∞ tracking control for hypersonic vehicles

This article aims to develop observer-based linear parameter varying output feedback H∞ tracking controller for hypersonic vehicles. Due to the complexity of an original nonlinear model of the hypersonic vehicle dynamics, a slow–fast loop linear parameter varying polytopic model is introduced for system stability analysis and controller design. Then, a state observer is developed by linear parameter varying technique in order to estimate the unmeasured attitude angular for slow loop system. Also, based on the designed linear parameter varying state observer, a kind of attitude tracking controller is presented to reduce tracking errors for all bounded reference attitude angular inputs. The closed-loop linear parameter varying system is proved to be quadratically stable by Lypapunov function technique. Finally, simulation results show that the developed linear parameter varying H∞ controller has good tracking capability for reference commands.

A new approach to the pre-compensation of contour errorsfor three-axis machine tools using an adaptive cross-coupled controller

Pre-compensation of contour errors is an effective method for predicting and compensating contour errors to position command; however, some challenges remain, such as modeling and precise control strategies specifically for contour errors. This paper presents an improved method for pre-compensation of contour error through a novel adaptive cross-coupled prediction compensation controller (ACPCC), in order to enhance robustness and the capacity to resist disturbance. Its central structure is constituted by a fuzzy PID controller, in which the bottom width of membership functions is optimized by improved particle swarm optimization. Estimation of tracking error and contour error is based on the servo system parameters and error-modeling, respectively, and contour error is regulated by the ACPCC and decoupled to each axis to modify reference commands ahead of sending them to the machine tool. Simulations and experiments are performed on a three-axis machine tool to validate effectiveness. The results demonstrate that the proposed method can significantly reduce tracking error and contour error compared with other control schemes.

Career Risk

ABSTRACTThe author defines asset manager career risk as the risk that asset owners terminate an existing manager due to an extended period of underperformance relative to a benchmark or peer group even though the manager has skill (defined here as positive information ratio). The author shows that myopic loss aversion gives rise to career risk even for skilled asset managers and that the current industry practice of quarterly or annual performance evaluations puts even the most skilled asset managers at risk of undue termination. The author also investigates how a reduction of tracking error leads to a reduction of career risk even though this comes at the expense of lower long-term performance. Finally, the author computes the minimum evaluation period needed to reduce career risk for asset managers of different skill levels.

A Bayesian approach to fiber orientation estimation guided by volumetric tract segmentation

Diffusion magnetic resonance imaging (dMRI) provides information about the microstructure of white matter in the human brain. From dMRI, streamlining tractography is often used to reconstruct computational representations of white matter tracts from which differences in structural connectivity can be explored. In the fiber tracking process, anatomical information can help reduce tracking errors caused by crossing fibers and image noise. In this paper, we propose a Bayesian method for estimating fiber orientations (FOs) guided by anatomical tract information using diffusion tensor imaging (DTI), which is a standard clinical and research dMRI protocol. The proposed method is named Fiber Orientation Reconstruction guided by Tract Segmentation (FORTS). A first step segments and labels the white matter tracts volumetrically, including explicit representations of crossing regions. A second step estimates the FOs using the diffusion information and the anatomical knowledge from segmented white matter tracts. A single FO is estimated in the noncrossing regions while two FOs are estimated in the crossing regions. A third step carries out streamlining tractography that uses information from both the segmented tracts and the estimated FOs. Experiments performed on a digital crossing phantom, a physical phantom, and brain DTI of 18 healthy subjects show that FORTS is able to use the anatomical information to produce FOs with better accuracy and to reduce anatomically incorrect streamlines. In particular, on the brain DTI data, we studied the connectivity of anatomically defined tracts to cortical areas, which is not straightforwardly achievable using only volumetric tract segmentation. These connectivity results demonstrate the potential application of FORTS to scientific studies.

Odd-harmonic repetitive control for high-speed raster scanning of piezo-actuated nanopositioning stages with hysteresis nonlinearity

In raster scanning applications of atomic force microscopies, precisely tracking periodic triangular trajectories is the major objective of nanopositioning stages. Considering the fact of periodic operations, the repetitive control technique becomes promising and has been recently developed to reduce tracking errors. In our new experiments, it is found that, with triangular reference input, the hysteresis nonlinearity mainly affects the system at the odd harmonics of the input signal. In this sense, an odd-harmonic repetitive control (ORC) strategy is proposed to handle the hysteresis nonlinearity, with the hysteresis treated as the odd-harmonic periodic disturbance. Therefore, it avoids the modeling and inverting of the complex hysteresis nonlinearity. Another benefit of the developed ORC strategy is that it can also account for the tracking errors caused by the linear dynamics effect. To verify the effectiveness of the ORC strategy, real-time experiments are performed on a custom-built piezo-actuated nanopositioning stage. Experimental results show that the developed ORC strategy achieves precise tracking of 1562.5-Hz triangular trajectory with the hysteresis nonlinearity mitigated to a negligible level, which demonstrates the feasibility and effectiveness of the developed ORC strategy on hysteresis compensation during high-speed raster scanning.

A Robust LQG Servo Control Strategy of Shunt-Active Power Filter for Power Quality Enhancement

This paper proposes a linear quadratic Gaussian (LQG) servo controller for the current control of shunt-active power filter (SAPF) operating under balanced and unbalanced supply voltages. This LQG controller is comprised of a LQ regulator and a Kalman filter (KF) that minimizes the error between the output currents and their variations. A feedback compensator is used in LQG servo controller that benefits an SAPF system by increasing tracking error reduction, gain stability, reducing amplitude distortion and sensitivity to external disturbances. A KF-based new reference current generation scheme is developed here to resolve the difficulty of tuning gains of a proportional integral controller and for avoiding the use of voltage sensors making it cost effective. Consequently, this reference scheme has self-capability of dc-link voltage regulation by adaptively estimating the peak value of source reference current with changing load conditions. The control algorithm is embedded in SAPF using a MATLAB/Simulink software environment. The effectiveness of the proposed LQG ServoKF algorithm is evaluated through comparison with an existing LQR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KF</sub> algorithm and then validated with experimental studies pursued using a dSPACE1104 computing platform. From the obtained experimental and simulation results, it is observed that the proposed control strategy exhibits superior performance in terms of robustness improvement and current harmonics mitigation under steady-state and dynamic load conditions, thus making it more effective for practical applications.

Evaluation of Growth of Mutual Funds and Exchange Traded Funds in India

Investors are always baffled about the risk-return characteristics of their investments. There is often the challenge of the alternative between active&amp;amp;passive investments. In case of active mutual funds there are numerous categories of active funds each tracking a different benchmark. It often leads to confusion about how the performance can be compared between one fund to another. The growth of ETFs' has been phenomenal in the recent years due to various advantages of an exchange traded fund compared to the mutual fund as lower cost of management, lesser dependence on fund manager, ease of transaction to name a few. In this context the research analysedthe passive ETF's&amp;amp;prominent Mutual funds both active and passive to justify superior returns at lower risk. The research was based on secondary data, for a period of 5 years i.e. from 2010 to 2015.The various tools used were Sharpe Ratio, Jenson's Alpha, Treynor's Ratio and Tracking error. The study recommends fund houses to implement proactive strategies to reduce tracking error and make ETF's a better alternative for investment.

V2X-Based Decentralized Cooperative Adaptive Cruise Control in the Vicinity of Intersections

Cooperative driving with V2X communication is widely researched due to its considerable potential to improve the safety and efficiency of road transportation systems. In this paper, a decentralized cooperative adaptive cruise control algorithm using V2X for vehicles in the vicinity of intersections (CACC-VI) is proposed. This algorithm is designed to improve the throughput of intersection by reorganizing the vehicle platoons around it, in consideration of safety, fuel consumption, speed limit, heterogeneous features of vehicles, and passenger comfort. Within a platoon, vehicles try to find the optimal control input by a distributed particle swarm optimization (PSO) algorithm, in order to reduce tracking errors, while respecting different constraints. A concept of opportunity space is proposed to facilitate platoon reorganization, in which a subplatoon or an individual vehicle can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. The main idea is to make full use of the road capacity and to distribute it to most vehicles that are capable to find an accelerating trajectory to get through the intersection within a limited period. The originality of our algorithm is the introduction of a novel application of V2X communication to make the traffic more intelligent, in terms of safety, time saving, and environment friendly.

Evaluation of Growth of Mutual Funds and Exchange Traded Funds in India

Investors are always baffled about the risk-return characteristics of their investments. There is often the challenge of the alternative between active&passive investments. In case of active mutual funds there are numerous categories of active funds each tracking a different benchmark. It often leads to confusion about how the performance can be compared between one fund to another. The growth of ETFs' has been phenomenal in the recent years due to various advantages of an exchange traded fund compared to the mutual fund as lower cost of management, lesser dependence on fund manager, ease of transaction to name a few. In this context the research analysedthe passive ETF's&prominent Mutual funds both active and passive to justify superior returns at lower risk. The research was based on secondary data, for a period of 5 years i.e. from 2010 to 2015.The various tools used were Sharpe Ratio, Jenson's Alpha, Treynor's Ratio and Tracking error. The study recommends fund houses to implement proactive strategies to reduce tracking error and make ETF's a better alternative for investment.

Improving the Intersection’s Throughput using V2X Communication and Cooperative Adaptive Cruise Control

In this paper, a decentralized cooperative adaptive cruise control algorithm for vehicles in the vicinity of intersections (CACC-VI) is proposed. This algorithm is designed to make use of the road capacity to let more vehicles get through the intersection within a limited traffic signal period in consideration of safety, fuel consumption, speed limit and heterogeneous features of vehicles, and passenger comfort. Within a platoon, vehicles try to find the optimal control input by a distributed PSO algorithm in order to reduce tracking errors while respecting different constraints. A concept of opportunity space is proposed to facilitate platoon reorganization in which a sub-platoon or an individual vehicle can choose to accelerate to join in the preceding platoon or to decelerate to depart from the current one. It is found that the throughput of the intersection during the green phase can be improved, and an optimal control input can be found for each vehicle within the defined constraints.

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

This article offers new insights on the learning control approach developed by [Hu et al. IEEE/ASME Trans. Mechatronics, 19(1): 191–200, 2014]. Theoretical insights are further proposed to unveil why the contraction-type iterative learning control (ILC) schemes are suitable and effective in compensating for hysteresis, widely existing in biorobotic locomotion. Under such circumstances, iteration-based second-order dynamics is adopted to describe the biorobotic systems acted upon by one unknown Preisach hysteresis term. The memory clearing operator is mathematically proven to enable feasibility of contraction-type ILC methods, regardless of whether the initial state is accurately set or not. The simulation examples confirm that the developed iteration-based controller combined with a preceded operator effectively reduce tracking errors caused by the hysteresis nonlinearity. Furthermore, the new insights on theoretical feasibility are definitively corroborated in accordance with the previously published experimental results.

Adaptive PSO for optimal LQR tracking control of 2 DoF laboratory helicopter

This paper deals with the attitude tracking control problem for a 2 DoF laboratory helicopter using optimal linear quadratic regulator (LQR). As the performance of the LQR controller greatly depends on the weighting matrices (Q and R), it is important to select them optimally. However, normally the weighting matrices are selected based on trial and error approach, which not only makes the controller design tedious but also time consuming. Hence, to address the weighting matrices selection problem of LQR, in this paper we propose an adaptive particle swarm optimization (APSO) method to obtain the elements of Q and R matrices. Moreover, to enhance the convergence speed and precision of the conventional PSO, an adaptive inertia weight factor (AIWF) is introduced in the velocity update equation of PSO. One of the key features of the AIWF is that unlike the standard PSO in which the inertia weight is kept constant throughout the optimization process, the weights are varied adaptively according to the success rate of the particles towards the optimum value. The proposed APSO based LQR control strategy is applied for pitch and yaw axes control of 2 Degrees of Freedom (DoF) laboratory helicopter workstation, which is a highly nonlinear and unstable system. Experimental results substantiate that the weights optimized using APSO, compared to PSO, result in not only reduced tracking error but also improved tracking response with reduced oscillations.

Correcting encoder interpolation error on the Green Bank Telescope using an iterative model based identification algorithm

Various forms of measurement errors limit telescope tracking performance in practice. A new method for identifying the correcting coefficients for encoder interpolation error is developed. The algorithm corrects the encoder measurement by identifying a harmonic model of the system and using that model to compute the necessary correction parameters. The approach improves upon others by explicitly modeling the unknown dynamics of the structure and controller and by not requiring a separate system identification to be performed. Experience gained from pin-pointing the source of encoder error on the Green Bank Radio Telescope (GBT) is presented. Several tell-tale indicators of encoder error are discussed. Experimental data from the telescope, tested with two different encoders, are presented. Demonstration of the identification methodology on the GBT as well as details of its implementation are discussed. A root mean square tracking error reduction from 0.68 arc seconds to 0.21 arc sec was achieved by changing encoders and was further reduced to 0.10 arc sec with the calibration algorithm. In particular, the ubiquity of this error source is shown and how, by careful correction, it is possible to go beyond the advertised accuracy of an encoder.

OQTAL: Optimal quaternion tracking using attitude error linearization

The use of quaternions or quaternion error attitude control strategies for unmanned aerial vehicles (UAVs) is commonplace. Quaternion tracking error control is usually presented in rather theoretical works, where the control algorithm is almost exclusively chosen to be a suboptimal one. However, the application of optimal control techniques is usually associated to simplified attitude models frequently aimed at solving real-life problems. The work presented in this paper aims to formally merge the development of a complete theoretical quaternion error model with an optimal control strategy. Moreover, the application of optimal control algorithms to a fully defined quaternion error state-space model and the validation of the same in a real-time experimental setup is the focus of this research. The result is a novel controller named Optimal Quaternion Tracking of Attitude Error Linearization (OQTAL). The paper provides a comprehensive proof of stability, full simulation validation for a planetary landing gravity turn trajectory, and evidence of repeatable experimental work for a real-time quadrotor UAV on a motion capture testbed. OQTAL is compared with proven optimal forms of (PID) proportional-integral-derivative control and linear quadratic regulator control and is shown to have a 10%–20% reduction in error for the experimental setup trajectory tracking trials and an even larger tracking error reduction in the gravity turn simulation trials. Furthermore, for close tracking conditions, OQTAL behaves almost like a linear and time-invariant system, therefore requiring limited computation time for performing the trajectory tracking.

Does Sadness Influence Investor Behavior? Evidence from Bereaved Fund Managers

This study examines whether bereavement, a common life experience, affect the performance and behaviors of mutual fund managers. We find that mutual fund return declines by around 3 percentage points around the parental deaths of fund managers. This underperformance persists for about one year, suggesting a longer-term negative impact of bereavement on fund managers’ cognitive ability. Additionally, the underperformance cannot be explained by alternative explanations such as physical distractions around parental death. Furthermore, bereaved managers’ investing behaviors also change around the parental deaths. Bereaved managers become 1) less likely to take risk, as they reduce tracking errors, shift holdings to larger stocks, and trade less actively; 2) more impatient, as they incur higher transaction costs and close their positions more quickly to realize gains; and 3) more sensitive to losses, as they are more likely to eliminate stocks following large negative returns. Our results identify bereavement as an underexplored life experience that can significantly influence investors’ performance and behaviors.

Iterative learning control bandwidth tuning using the particle swarm optimization technique for high precision motion

This paper utilizes the improved particle swarm optimization (IPSO) technique for adjusting the gains of PID controller, iterative learning control (ILC) and the bandwidth of Butterworth filter of the ILC. The conventional ILC learning process has the potential to excite rich frequency contents and to learn the error signals. However the learnable and unlearnable error signals should be separated for b ettering control process along with the repetitions. Since producing a high frequency error condition should be avoided before the phase margin cause any trouble. Learnable error signals through a bandwidth tuning mechanism should be adaptively injected into learning control laws and thus reduce the tracking error effectively at every repetition. The filter bandwidth should be changed at every repetition for the shape of compensated errors at frequency response thinking. Thus adaptive bandwidth in the ILC controller with the aid of IPSO tuning is proposed here. Simulation results show the new controller can cancel the errors efficiently as the process is repeated. The correlation coefficient that validates the learnable compensated error signal for the trajectory is adaptively decomposed from previous error history via the bandwidth tuning mechanism in the next repetition. The learnable error signals of the intrinsic mode functions through the empirical mode decomposition correlate efficiently with reduced tracking error with further repetitions. Simulation results validate the effectiveness of the IPSO-ILC for precision motion control of a robot arm link performing high speed maneuvering or computer numerical control commanding axis positioning.

A Modified Moving Average for Financial Trading Algorithms

The volatility of security prices often limits the usefulness of straightforward moving averages. Price moving averages rarely reflect reliable estimates of price, and only indicate the proper trend during prolonged bull or bear markets. In this paper, we describe a modified moving average that applied to stocks and bonds is shown to reduce tracking error by 30 to 60 percent and capture significant inflection points with good accuracy.

Friction compensation using Coulomb friction model with zero velocity crossing estimator for a force controlled model in the loop suspension test rig

This paper presents a method of friction compensation for a linear electric motor in a model in the loop suspension test rig. The suspension consists of a numerically modeled spring and damper, with inputs of suspension motion. The linear motor is force controlled using a force sensor to track the output of the numerical model. The method uses a Coulomb friction model and applies a feedforward step signal when velocity zero crossing occurs. Velocity zero crossing estimation is achieved using an algorithm based on measured feedback velocity and force. Experimental results indicate reduction of force tracking error caused by Coulomb friction leading to improved test rig accuracy.