Decoupling motion tracking control for 4WD autonomous vehicles based on the path correction

2021 ◽  
pp. 095440702110159
Author(s):  
Yinghong Yu ◽  
Yinong Li ◽  
Yixiao Liang ◽  
Ling Zheng ◽  
Yue Ren
Keyword(s):  
Autonomous Vehicles ◽  
Motion Tracking ◽  
Tracking Error ◽  
Inverse System ◽  
Tracking Accuracy ◽  
Dynamic Coupling ◽  
Long Distance ◽  
Correction Model ◽  
Decoupling Method ◽  
The Neural Network

Since one control loop input disturbs the control of another loop, the dynamic coupling of the longitudinal and lateral directions adversely affects the motion tracking accuracy of autonomous vehicles. With the ability to minimize the interactions between the longitudinal and lateral dynamics, the inverse system learned by the neural network is an effective way to decouple vehicle dynamics. After tracking the vehicle states projected from the desire motion, the dynamic decoupling and the motion tracking are both realized. However, the accumulation of vehicle state tracking errors causes the stable yaw tracking error and the lateral tracking divergence. To solve the accompanying problem, a path correction model is designed to periodically update the desired vehicle states. Moreover, the applicability of the inverse system decoupling method is improved in this paper, because the method usually adopted in distributed drive electric vehicles is applied to four-wheel driving vehicles representing the traditional driving form. Simulation results indicate that the decoupling motion tracking method with the path correction model is suitable for long-distance and complex conditions and has the highest comprehensive tracking accuracy compared with the integrated MPC (model predictive control) and the pure pursuit in the dynamic coupling conditions.

scholarly journals The use of a cascaded Kinect and electromyography gesture decoding algorithm in an initial robot-aided hand neurorehabilitation

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775196 ◽  
Author(s):  
Ping Wang ◽  
Yabo Wang ◽  
He Huang ◽  
Feng Ru ◽  
Quan Pan
Keyword(s):  
Control Algorithm ◽  
Motion Tracking ◽  
Simulation Experiment ◽  
Tracking Error ◽  
Torque Control ◽  
Tracking Accuracy ◽  
Rehabilitation Robots ◽  
Machine Interface ◽  
Repetitive Activities ◽  
Active Assistance

In order to improve the neurological recovery of hand neurorehabilitation, target-oriented, intensive, repetitive activities of daily living are used, such as training with recognition of hand gestures during robot-aided exercise. In this article, a cascade control algorithm integrating electromyography bio-feedback into hand gesture recognition is proposed. The outer loop is the trajectory motion tracking with Kinect-based gesture decoding classifier, and the inner loop is torque control with electromyography bio-feedback in the real time. This proposed method improves the tracking accuracy. The tracking error is effectively reduced from 70.56 to 28.07 in the simulation experiment. The initial test proves that the proposed method with additional torque control allows active assistance on the human–machine interface of other rehabilitation robots in future.

scholarly journals Real-Time Tumor Motion Tracking in 3D Using Planning 4D CT Images during Image-Guided Radiation Therapy

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 155
Author(s):  
Jang-Hwan Choi ◽  
Sooyeul Lee
Keyword(s):  
Real Time ◽  
Motion Tracking ◽  
Tracking Error ◽  
Structural Similarity ◽  
Tracking Accuracy ◽  
Image Guided Radiation Therapy ◽  
Tumor Tracking ◽  
Respiratory Phase ◽  
4D Computed Tomography ◽  
Using Data

In this paper we propose a novel method for tracking the respiratory phase and 3D tumor position in real time during treatment. The method uses planning four-dimensional (4D) computed tomography (CT) obtained through the respiratory phase, and a kV projection taken during treatment. First, digitally rendered radiographs (DRRs) are generated from the 4DCT, and the structural similarity (SSIM) between the DRRs and the kV projection is computed to determine the current respiratory phase and magnitude. The 3D position of the tumor corresponding to the phase and magnitude is estimated using non-rigid registration by utilizing the tumor path segmented in the 4DCT. This method is evaluated using data from six patients with lung cancer and dynamic diaphragm phantom data. The method performs well irrespective of the gantry angle used, i.e., a respiration phase tracking accuracy of 97.2 ± 2.5%, and tumor tracking error in 3D of 0.9 ± 0.4 mm. The phantom study reveals that the DRRs match the actual projections well. The time taken to track the tumor is 400 ± 53 ms. This study demonstrated the feasibility of a technique used to track the respiratory phase and 3D tumor position in real time using kV fluoroscopy acquired from arbitrary angles around the freely breathing patient.

scholarly journals Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System

2020 ◽  
Vol 10 (21) ◽  
pp. 7394
Author(s):  
Yinghong Yu ◽  
Yinong Li ◽  
Yixiao Liang ◽  
Ling Zheng ◽  
Wei Yang
Keyword(s):  
Linear System ◽  
Trajectory Tracking ◽  
Control Method ◽  
Stability Control ◽  
Inverse System ◽  
Coupling Mechanism ◽  
Tracking Accuracy ◽  
Dynamic Coupling ◽  
Automated Vehicles ◽  
Dynamic Decoupling

A simultaneous trajectory tracking and stability control method is present for the four-wheel independent drive (4WID) automated vehicles to handle dynamic coupling maneuvers. To conquer the disadvantage that attendant disturbances caused by the dynamic coupling of traditional decentralized control methods degenerate the trajectory tracking accuracy, the proposed method takes advantage of the idea of decoupling to optimize the tracking performance. After establishing the dynamic model of the 4WID automated vehicles, the coupling mechanism of the vehicle dynamic control and its negative effect on trajectory tracking were studied at first. The inverse system model was then determined by machine learning and connected in series with the controlled object to form a pseudo linear system to realize dynamic decoupling. Finally, differing from previous tracking methods following the apparent lateral position and longitudinal velocity references, the pseudo linear system tracks the ideal intermediate targets transferred from the target trajectory, that is, the accelerations of vehicle in longitudinal, lateral and yaw directions, to indirectly achieve trajectory tracking and validly restrain the vehicle motion. The effectiveness of the proposed method, i.e., the high tracking accuracy and the stable driving performance, is verified through three coupling driving scenarios in the CarSim-Simulink co-simulations platform.

Integrated optimal design of a high-speed planar parallel manipulator

2018 ◽  
Vol 233 (9) ◽  
pp. 2976-2990 ◽  
Author(s):  
Zhengsheng Chen ◽  
Minxiu Kong
Keyword(s):  
Optimal Design ◽  
Parallel Manipulator ◽  
High Speed ◽  
Design Method ◽  
Tracking Error ◽  
Dimensional Synthesis ◽  
Tracking Accuracy ◽  
Nsga Ii ◽  
Parameters Tuning ◽  
Planar Parallel Manipulator

To obtain excellent comprehensive performances of the planar parallel manipulator for the high-speed application, an integrated optimal design method, which integrated dimensional synthesis, motors/reducers selection, and control parameters tuning, is proposed, and the 3RRR parallel manipulator was taken as the example. The kinematic and dynamic performances of condition number, velocity index, acceleration capability, and low-order frequency are taken into accounts for the dimensional synthesis. Then, to match motors/reducers parameters and keep an economical cost, the constraint equations and the parameters library are built, and the cost is chosen as one of the optimization objectives. Also, to get high tracking accuracy, the dynamic forward plus proportional–derivative control scheme is introduced, and the tracking error is chosen as one of the optimization objectives. Hence, the optimization model including dimensional synthesis, motors/reducers selection and controller parameters tuning is established, which is solved by the genetic algorithm II (NSGA-II). The result shows that comprehensive performances can be effectively promoted through the proposed integrated optimal design, and the prototype was constructed according to the Pareto-optimal front.

Position Domain PD Control for Contour Tracking

2010 ◽  
Author(s):  
P. R. Ouyang ◽  
Truong Dam
Keyword(s):  
Motion Tracking ◽  
Control Method ◽  
Tracking Error ◽  
Original System ◽  
Contour Tracking ◽  
Pd Control ◽  
Tracking Errors ◽  
Motion System ◽  
Common Control ◽  
The Time Domain

For multi-axis motion control applications, contour tracking is one of the most common control problems encountered by industrial manipulators and robots. In this paper, a position domain PD control method is proposed for the purpose of improving the contour tracking performance. To develop the new control method, the multi-axis motion system is viewed as a master-slave motion system where the master motion is sampled equidistantly and used as an independent variable, while the slave motions are described as functions of the master motion according to the contour tracking requirements. The dynamic model of the multi-axis motion system is developed in the position domain based on the master motion by transforming the original system dynamic equations from the time domain to the position domain. In this control methodology, the master motion will yield zero tracking error for the position as it is used as reference, and only the slave motion tracking errors will affect the final contour tracking errors. The proposed position domain PD controller is successfully examined in a Cartesian robotic system for linear motion tracking and circular contour tracking.

A Study of Tracking System Using Multiple Emitter Towers

2013 ◽  
Vol 694-697 ◽  
pp. 927-935 ◽  
Author(s):  
Yi Sun ◽  
Tao Ma ◽  
Chia Yung Han ◽  
Joseph Ross ◽  
William Wee
Keyword(s):  
Coordinate Transformation ◽  
Motion Tracking ◽  
Tracking System ◽  
Transformation Method ◽  
Tracking Accuracy ◽  
Working Space ◽  
Frontal Part ◽  
Motion Tracking System ◽  
Coordinate Transformation Method

This paper presents a simple and accurate coordinate transformation method for extending the tracking space of the Intersense IS-900 spatial and motion tracking system using multiple pre-configured emitter towers to form the emitter constellation, but without resorting to the use of a surveyor machine. The proposed approach uses the differences of positional coordinate readings from each emitter tower among a set of commonly viewed spatial points to calculate the parameters needed to define the coordinate transformation. By applying this method, the tracking accuracy using the entire emitter constellation can be achieved by less than 0.5 inches error in most of the working space, and as low as 0.2 inches error in the frontal part of the working space.

Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains

2020 ◽  
Author(s):  
James Dallas ◽  
Yifan Weng ◽  
Tulga Ersal
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Autonomous Vehicles ◽  
Performance Metrics ◽  
Soft Soil ◽  
Computation Time ◽  
Operating Conditions ◽  
The Neural Network ◽  
Vehicle Response ◽  
The Impact

Abstract In this work, a novel combined trajectory planner and tracking controller is developed for autonomous vehicles operating on off-road deformable terrains. Common approaches to trajectory planning and tracking often rely on model-dependent schemes, which utilize a simplified model to predict the impact of control inputs to future vehicle response. However, in an off-road context and especially on deformable terrains, accurately modeling the vehicle response for predictive purposes can be challenging due to the complexity of the tire-terrain interaction and limitations of state-of-the-art terramechanics models in terms of operating conditions, computation time, and continuous differentiability. To address this challenge and improve vehicle safety and performance through more accurate prediction of the plant response, in this paper, a nonlinear model predictive control framework is presented that accounts for terrain deformability explicitly using a neural network terramechanics model for deformable terrains. The utility of the proposed scheme is demonstrated on high fidelity simulations for a notional lightweight military vehicle on soft soil. It is shown that the neural network based controller can outperform a baseline Pacejka model based scheme by improving on performance metrics associated with the cost function. In more severe maneuvers, the neural network based controller can achieve sufficient fidelity as compared to the plant to complete maneuvers that lead to failure for the Pacejka based controller. Finally, it is demonstrated that the proposed framework is conducive to real-time implementability.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

scholarly journals Accelerating 3-D GPU-based Motion Tracking for Ultrasound Strain Elastography Using Sum-Tables: Analysis and Initial Results

2019 ◽  
Vol 9 (10) ◽  
pp. 1991 ◽  
Author(s):  
Bo Peng ◽  
Shasha Luo ◽  
Zhengqiu Xu ◽  
Jingfeng Jiang
Keyword(s):  
Correlation Coefficient ◽  
Computational Efficiency ◽  
Graphics Processing Units ◽  
Motion Tracking ◽  
Performance Comparison ◽  
Tracking Accuracy ◽  
Computing Environment ◽  
Phantom Experiment ◽  
Strain Elastography ◽  
Computationally Intensive

Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally intensive, a lot of efforts are under way to accelerate 3-D motion tracking. In the literature, the concept of Sum-Table has been used in a serial computing environment to reduce the burden of computing signal correlation, which is the single most computationally intensive component in 3-D motion tracking. In this study, parallel programming using graphics processing units (GPU) is used in conjunction with the concept of Sum-Table to improve the computational efficiency of 3-D motion tracking. To our knowledge, sum-tables have not been used in a GPU environment for 3-D motion tracking. Our main objective here is to investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) method for the above-mentioned GPU-accelerated 3-D USE. More specifically, two different implementations of ST-NCC methods proposed by Lewis et al. and Luo-Konofagou are compared against each other. During the performance comparison, the conventional method for calculating the normalized correlation coefficient (NCC) was used as the baseline. All three methods were implemented using compute unified device architecture (CUDA; Version 9.0, Nvidia Inc., CA, USA) and tested on a professional GeForce GTX TITAN X card (Nvidia Inc., CA, USA). Using 3-D ultrasound data acquired during a tissue-mimicking phantom experiment, both displacement tracking accuracy and computational efficiency were evaluated for the above-mentioned three different methods. Based on data investigated, we found that under the GPU platform, Lou-Konofaguo method can still improve the computational efficiency (17–46%), as compared to the classic NCC method implemented into the same GPU platform. However, the Lewis method does not improve the computational efficiency in some configuration or improves the computational efficiency at a lower rate (7–23%) under the GPU parallel computing environment. Comparable displacement tracking accuracy was obtained by both methods.

Particle swarm optimization-tuned adaptive fuzzy output feedback motion tracking control of piezo-positioning mechanism with unknown hysteresis

2019 ◽  
Vol 41 (10) ◽  
pp. 2897-2908 ◽  
Author(s):  
Mohsen Hasanpour Naseriyeh ◽  
Adeleh Arabzadeh Jafari ◽  
Mehrnoosh Zaeifi ◽  
Seyed Mohammad Ali Mohammadi
Keyword(s):  
Particle Swarm Optimization ◽  
Output Feedback ◽  
Motion Tracking ◽  
Tracking Error ◽  
Particle Swarm ◽  
Small Neighborhood ◽  
Output Feedback Control ◽  
Fuzzy Logic Systems ◽  
Swarm Optimization ◽  
Adaptive Fuzzy

This paper considers the problem of observer-based adaptive fuzzy output feedback control for a piezo-positioning mechanism with unknown hysteresis. In this paper, fuzzy logic systems (FLSs) are used to estimate the unknown nonlinear functions, and also Nussbaum function is utilized to overcome the unknown direction hysteresis. Based on the Lyapunov method, the control scheme is constructed by using the backstepping and adaptive technique. In order to better control performance in reducing tracking error, the particle swarm optimization (PSO) algorithm is utilized for tuning the controller parameters. Proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded (SGUUB) and the tracking error can converge to a small neighborhood of the origin. Finally, the simulation results are provided to demonstrate the effectiveness and robustness of the proposed approach.

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