scholarly journals A Model-based Parameter Optimization Control Strategy for Trajectory Tracking with Torque and Velocity Constraints

2021 ◽  
Author(s):  
Min Luo ◽  
Xiaorong Hou ◽  
Xiaoxue Li ◽  
Jinbo Lu ◽  
Jing Yang
Keyword(s):  
Parameter Optimization ◽  
Trajectory Tracking ◽  
Learning Strategy ◽  
Optimization Methods ◽  
Optimization Method ◽  
Settling Time ◽  
Trajectory Tracking Control ◽  
Time Index ◽  
Model Based ◽  
Time Optimization

Abstract The wheeled robots trajectory tracking control methods rarely constrain the torque and speed at the same time. In actual application, the torque and speed of the robot cannot exceed the saturation limit of the actuator. This paper develops a model-based trajectory tracking parameter optimization controller with both velocity and torque constraints, using a gradient descent parameter iterative learning strategy to minimize the settling time index of the system. Trajectory tracking time optimization methods usually require a given analytical expression of the system time, while this time optimization method only requires that the settling time is solvable. The MATLAB simulation experiments show that the proposed parameter optimization controller for trajectory tracking can perform velocity and torque constraints while having a relatively good overall rapidity time index. If the resolution of the robot sensor can meet the design requirements, the optimization method can strictly control the system torque maximum to a reasonably small expected value. When the resolution of the robot sensor is limited, this optimization method can restrict the system torque maximum within a reasonable saturation constraint range.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

A Novel Model-Based Hearing Compensation Design Using a Gradient-Free Optimization Method

2005 ◽  
Vol 17 (12) ◽  
pp. 2648-2671 ◽  
Author(s):  
Zhe Chen ◽  
Suzanna Becker ◽  
Jeff Bondy ◽  
Ian C. Bruce ◽  
Simon Haykin
Keyword(s):  
Neural Coding ◽  
Optimization Procedure ◽  
Optimization Methods ◽  
Optimization Method ◽  
Physiological Data ◽  
Compensation Strategy ◽  
Unknown Parameters ◽  
Model Based ◽  
Hearing Compensation ◽  
Novel Model

We propose a novel model-based hearing compensation strategy and gradient-free optimization procedure for a learning-based hearing aid design. Motivated by physiological data and normal and impaired auditory nerve models, a hearing compensation strategy is cast as a neural coding problem, and a Neurocompensator is designed to compensate for the hearing loss and enhance the speech. With the goal of learning the Neurocompensator parameters, we use a gradient-free optimization procedure, an improved version of the ALOPEX that we have developed (Haykin, Chen, & Becker, 2004), to learn the unknown parameters of the Neurocompensator. We present our methodology, learning procedure, and experimental results in detail; discussion is also given regarding the unsupervised learning and optimization methods.

A New Hybrid Method for Parameter Optimization of SVR

2018 ◽  
Vol 22 (2) ◽  
pp. 271-279 ◽  
Author(s):  
Jiang Xie ◽  
Taifeng Sun ◽  
Jieyu Zhang ◽  
Wu Zhang ◽  
◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Hybrid Method ◽  
Scatter Search ◽  
Back Propagation ◽  
Optimization Methods ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Local Optimum ◽  
Improvement Method

The performance of Support Vector Regression (SVR) depends heavily on its parameters, but some optimization methods based on Grid Search (GS) or evolutionary algorithms still have several issues that must be addressed. This paper proposes a new hybrid method (PSO-SS) that combines Particle Swarm Optimization (PSO) and Scatter Search (SS) to optimize the parameters of the SVR. In PSO-SS, to improve the search capability of PSO and reduce the likelihood of the PSO becoming trapped in the local optimum, the initial PSO population is generated by the diversification generation method and the improvement method of SS, and the velocity updating formula of PSO is improved by adding diversity information. On the StatLib and UCI datasets, our experiments show that the PSO-SS method is an effective parameter optimization method compared with other methods. In addition, an SVR model with its parameters optimized by PSO-SS (PSO-SS-SVR) is used to predict the grain size of aluminum alloys. The experimental results show that the PSO-SS-SVR method outperforms Back Propagation Neural Network (BPNN), PSO-SVR and the empirical model.

Model-Based Control of Synchronizer Shifting Process for Trajectory Tracking Control

2020 ◽  
Vol 21 (4) ◽  
pp. 943-952
Author(s):  
Yan Zhang ◽  
Han Zhao ◽  
Mingming Qiu ◽  
Feifei Qin ◽  
Bingzhan Zhang
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Model Based Control ◽  
Model Based

scholarly journals Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-22 ◽  
Author(s):  
Yingjie Song ◽  
Daqing Wu ◽  
Ali Wagdy Mohamed ◽  
Xiangbing Zhou ◽  
Bin Zhang ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Time Complexity ◽  
Method Comparison ◽  
Optimization Methods ◽  
Optimization Method ◽  
Pv Systems ◽  
The Past ◽  
Comparison Results ◽  
Mutation Strategy ◽  
Poor Stability

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.

scholarly journals Optimal Tuning of the SMC Parameters for a Two-Link Manipulator Co-Simulation Control

2021 ◽  
Author(s):  
Sinan Ilgen ◽  
Akif Durdu ◽  
Erdi Gulbahce ◽  
Abdullah Cakan
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Pid Controller ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Optimization Methods ◽  
Effective Control ◽  
Control Performance ◽  
Trajectory Tracking Control ◽  
Simplex Search

This paper presents the trajectory tracking control of a two-link planar robot manipulator using MSC Adams and MATLAB co-simulation which enables the innovative virtual prototyping of the systems without any mathematical expressions. Firstly, the tracking control performance of the planar manipulator is investigated using the Sliding Mode Control (SMC) controller and the Proportional Integral Derivative (PID) controller in terms of the performance analysis. As a result, the SMC demonstrates effective control performances compared to the PID controller according to the required trajectory, settling time, and end position of the system. Then, the SMC controller parameters are determined using the different optimization methods offered as open source by MATLAB/Response Optimization Toolbox and compared to each other. In the virtual co-simulation, the trajectory tracking control performance is observed to be improved by optimizing the parameters of the SMC controller using Simplex Search (SS) method. All control results are examined and presented with graphics and international error standards.

Sign in / Sign up

Export Citation Format

Share Document