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Neural Approximation-based Model Predictive Tracking Control of Nonholonomic Wheel-legged Robots
Jiehao Li,Junzheng Wang,Shoukun Wang,Wen Qi,Longbin Zhang,Yingbai Hu,Hang Su 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.1
This paper proposes a neural approximation based model predictive control approach for tracking controlof a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertaintyand unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertainenvironment, effective approaches for reliable tracking control should be investigated with the consideration of thedisturbances, including internal-robot friction and external physical interactions in the robot’s dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller(NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybridcontrol strategy. The results indicate that the proposed methodology can achieve promising tracking performancein terms of accuracy and stability.
A Prioritized Network Coding Scheme based E-WP Algorithm for Packet Recovery in DVB-IPDC System
Lian Wang,Jiehao Wang 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.3
DVB-H is developed to broadcast digital videos to mobile handsets, but data loss is a concern due to the wireless broadcasting nature. In this paper, take WiMAX as an IP-based wireless network to recover the loss packets in DVB-H, and take E-WP based on network coding as the encoding packet selection algorithm. Furthermore, a prioritized scheme EWP-PNC based on E-WP packet selection algorithm is proposed. In this scheme, take a base station in WiMAX as encoding node to encode any two packets that meet the encoding and decoding necessary and sufficient condition into an encoding recovery packet according to the current lost packet distribution, and calculate the benefit of each encoding packet to decide the final priority of all encoding packets. The goals of this scheme are to improve the lost packet recovery ratio and reduce the discarded packet ratio. According to the simulation results, the validity of this scheme is proved.
Wei Shen,Jiehao Wang 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.7
Control issue is the key for applying hydraulic hybrid system, especially for common pressure rail (CPR)system which has the huge potential to enhance efficiency. In the paper, the mathematical model of hydrauliccylinder speed control system using new hydraulic transformer is established. Then an adaptive fuzzy sliding modecontroller based on Pi-sigma fuzzy neutral network is designed to solve the problem of parameter uncertainty andnonlinearity without establishing the precise model. Furthermore, compared to PID and conventional adaptivefuzzy system, the controller proposed can achieve good control performance and strong robustness in the presenceof time-varying uncertainty.
PDBNet: Parallel Dual Branch Network for Real-time Semantic Segmentation
Yingpeng Dai,Junzheng Wang,Jiehao Li,Jing Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.8
To make a trade-off between accuracy and inference speed in real-time applications on the unmanned mobile platform, a novel neural network, named Parallel Dual Branch Network (PDBNet), is proposed. Firstly, a multi-scale module, namely Parallel Dual Branch (PDB), is designed to extract complete information. PDB module consists of two parallel branches to remove detailed low-level information and high-level semantic information while maintaining few parameters. Then, based on the PDB module, PDBNet, a small-scale and shallow structure, is designed for semantic segmentation. A multi-scale module tends to extract abundant information and segment the object out from the image well. The small-scale and shallow structure tends to accelerate the inference speed. So PDBNet architecture is designed to be effective both in terms of accuracy and inference speed. PDBNet adopts three downsamplings to obtain feature maps with high spatial resolution and uses PDB modules with different dilation rates to extract multi-scale features and enlarge the receptive field in the last several layers. Finally, experiments on Camvid dataset and Cityscapes dataset, we respectively get 67.7% and 69.5% Mean Intersection over Union (MIoU) with only 1.82 million parameters and quicker speed on a single GTX 1070Ti card.
Voltage Prediction in Transient Connection for Power Battery Modules: Experimental Results
You Xu,Qiang Wu,Limin Yu,Jiehao Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.9
This paper mainly focuses on the safe maintenance of power systems and the use of secondary batteries for electric vehicles from the experimental scenarios. In engineering, the power battery module of series connection or parallel connection is conducive to the fast combination and unloading of high-voltage energy systems in the electric vehicles. However, the parallel connection is affected by inconsistency and generally uses the powerful resistance to solve the current shock. In this paper, an improved RC network lithium-iron-phosphate battery model based on parallel hysteretic voltage is proposed to achieve a safe parallel sequence and solve the high current impact in this process. Furthermore, the voltage oscillation standard deviations at different voltage levels and voltage differences are carried out, which obtains the hysteretic curve map of oscillating voltage distribution. At last, the voltage oscillation model is established by discussing the oscillating parallel characteristics, and the control strategy of the pre-charging circuit can be applied to the voltage optimization. Comparative experimental results using 32650 lithium-ion phosphate battery can effectively achieve satisfactory predicting performance with a reasonable voltage range.
Songyi Zhou,Yizhao Pan,Yan Zhang,Lijun Gu,Leikai Ma,Qingqing Xu,Weijian Wang,Jiehao Sun 대한통증학회 2023 The Korean Journal of Pain Vol.36 No.3
Background: Spinal N-methyl-D-aspartate (NMDA) receptor activation is attributed to remifentanil-induced hyperalgesia (RIH). However, the specific mechanism and subsequent treatment is still unknown. Previous studies have shown that the dynamin-related protein 1 (DRP1)-mitochondria-reactive oxygen species (ROS) pathway plays an important role in neuropathic pain. This study examined whether antisense oligodeoxynucleotides against DRP1 (AS-DRP1) could reverse RIH. Methods: The authors first measured changes in paw withdrawal mechanical threshold (PWMT) and paw withdrawal thermal latency (PWTL) at 24 hours before remifentanil infusion and 4, 8, 24, and 48 hours after infusion. The expression levels of DRP1 and NR2B were measured after behavioral testing using Western blotting. In addition, DRP1 expression was knocked down by intrathecal administration of AS-DRP1 to investigate the effects of DRP1 on RIH. The behavioral testing, the expression levels of spinal DRP1 and NR2B, and dorsal mitochondrial superoxide were measured. Changes in mitochondrial morphology were assessed using electron microscopy. Results: After remifentanil exposure, upregulation of spinal DRP1 and NR2B was observed along with a reduction in PWMT and PWTL. In addition, AS-DRP1 improved RIH-induced PWTL and PWMT (P < 0.001 and P < 0.001) and reduced remifentanil-mediated enhancement of spinal DRP1 and NR2B expression (P = 0.020 and P = 0.022). More importantly, AS-DRP1 reversed RIH-induced mitochondrial fission (P = 0.020) and mitochondrial superoxide upregulation (P = 0.031). Conclusions: These results indicate that AS-DRP1 could modulate NMDA receptor expression to prevent RIH through the DRP1-mitochondria-ROS pathway.