2022 Eighth Indian Control Conference (ICC)最新文献_第2页

On Controller Design for Unknown Nonlinear Systems with Prescribed Performance and Input Constraints 具有预定性能和输入约束的未知非线性系统的控制器设计

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093463

P. Mishra, Pushpak Jagtap

{"title":"On Controller Design for Unknown Nonlinear Systems with Prescribed Performance and Input Constraints","authors":"P. Mishra, Pushpak Jagtap","doi":"10.1109/ICC56513.2022.10093463","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093463","url":null,"abstract":"This paper considers the tracking control problem for an unknown control-affine nonlinear system with time-varying bounded disturbance subjected to a prescribed performance and input constraints. For such a problem, we cannot arbitrarily specify any restriction on the input. For the same, we have given a feasibility condition for prescribing input constraints. In addition, we cannot prescribe arbitrary performance constraints when input constraints are present. There is always a trade-off when performance and input constraints are specified simultaneously. To deal with such arbitrary prescription issues, we also devised a feasibility condition for prescribing performance constraints. Furthermore, considering the feasibility condition, we have proposed a low-complexity approximation-free controller, which guarantees that the tracking performance will never violate its prescribed performance and input constraints. Simulation results, including case studies, show the effectiveness of the proposed controller and support the feasibility condition by examining the failure of the tracking performance under unfeasible conditions.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133332932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Trajectory tracking of a Quadcopter using Supertwisting Sliding Mode Control 基于超扭转滑模控制的四轴飞行器轨迹跟踪

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093308

N. Ghosh, P. Roy

引用次数: 0

FPGA Implementation of Low Complexity Nonlinear Model Predictive Control Using Deep Learning Approach 基于深度学习的低复杂度非线性模型预测控制的FPGA实现

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093340

N. Mohanty, Chaitanya Jugade, Vaishali Patne, Deepak D. Ingole, D. Sonawane

{"title":"FPGA Implementation of Low Complexity Nonlinear Model Predictive Control Using Deep Learning Approach","authors":"N. Mohanty, Chaitanya Jugade, Vaishali Patne, Deepak D. Ingole, D. Sonawane","doi":"10.1109/ICC56513.2022.10093340","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093340","url":null,"abstract":"The main bottleneck in the embedded real-time implementation of nonlinear model predictive control (NMPC) is to solve a complex online optimization problem within a sample time on resource-limited hardware. This computational complexity limits its applicability in real-time control applications running on embedded hardware. Motivated by the recent developments in machine learning methods, our idea is to approximate standard NMPC control law with deep neural networks (DNNs) for a nonlinear system. The developed DNN-based NMPC is implemented on a field-programmable gate array (FPGA) using low-level C/C++ code, where the activation function is evaluated at each sample time. The performance of the proposed DNN-NMPC is demonstrated with a flying robot control application. The proposed DNN-NMPC is compared with the standard NMPC implemented on the same FPGA board (Xilinx's ZYNQ-7000 SoC ZC706). The hardware-in-the-loop (HIL) co-simulation results of both controllers are presented with a detailed analysis of computational complexity, memory utilization, clock speed, and power utilization. Results show that the FPGA-based DNN-NMPC is fast, resource-efficient, and delivers comparable closed-loop performance compared to standard NMPC. The proposed framework allows one to use an NMPC for systems with high computational complexity, significant resource demand and on low-cost embedded hardware like microcontrollers, digital signal processors (DSPs), programmable logic controllers (PLCs), etc.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121589026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hierarchical Framework for Optimal and Scalable Process Scheduling in Plant Operations 工厂作业中最优和可扩展过程调度的分层框架

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093603

Ajit Umesh Deshpande, Mayank Baranwal

引用次数: 0

Cluster Consensus of Multi-agent Systems with Second Order Dynamics Over Matrix-weighted Graphs 矩阵加权图上二阶动态多智能体系统的聚类一致性

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093252

G. R., V. Resmi, Rakesh R. Warier

引用次数: 0

Parameterized Adaptive Controller Design using Reinforcement Learning and Deep Neural Networks 基于强化学习和深度神经网络的参数化自适应控制器设计

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093404

Kranthi Kumar P, K. Detroja

{"title":"Parameterized Adaptive Controller Design using Reinforcement Learning and Deep Neural Networks","authors":"Kranthi Kumar P, K. Detroja","doi":"10.1109/ICC56513.2022.10093404","DOIUrl":"https://doi.org/10.1109/ICC56513.2022.10093404","url":null,"abstract":"This manuscript aims to build a parameterized adaptive controller using Reinforcement Learning (RL) and Deep Neural Networks (DNN). The main objective is to adapt parameters of any given controller structure using RL formulation and achieve better performance. In recent years, reinforcement learning has gained much attention, and its advantages make it ideal for adaptive tuning of parameterized controllers. With the advancement of computational power, it has become easier to approximate a complex policy function using a deep neural network to achieve better accuracy and performance. Conventional Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm may not be able to provide best possible training for a given number of episodes. To improve performance of the TD3 algorithm, dynamic action space is proposed along with modified reward function, designed to aid faster convergence. The proposed algorithm provides improved performance by dynamically modifying the action space in the existing TD3 algorithm. The effectiveness of the proposed RL-based parameterized controller is demonstrated through a standard first order system by designing an adaptive PI controller. A case study involving a 3 DOF (Degree of Freedom) gyroscope system, which is an unstable plant, is also presented. For the 3 DOF gyroscope system an adaptive Lead controller is designed, where the proposed algorithm provides faster convergence and better performance compared to the original TD3 algorithm.","PeriodicalId":101654,"journal":{"name":"2022 Eighth Indian Control Conference (ICC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129679919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A State-Space Perspective on the Expedited Gradient Methods: Nadam, RAdam, and Rescaled Gradient Flow 加速梯度方法的状态空间视角:那达慕、RAdam和重尺度梯度流

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093397

Kushal Chakrabarti, N. Chopra

引用次数: 0

Robust Linear Controller Design for Tilt Quadrotor based on Euler Angles 基于欧拉角的倾斜四旋翼鲁棒线性控制器设计

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093597

Sathyanarayanan Seshasayanan, S. R. Sahoo

引用次数: 0

Asymptotic Observers for Heterogeneous Reaction Systems using the Concepts of Reaction and Mass-transfer Extents 用反应和传质范围概念的非均相反应系统的渐近观测器

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093438

Nirav P. Bhatt

引用次数: 0

Exponential Reaching Law based Robust Trajectory Tracking for Unmanned Aerial Vehicles 基于指数逼近律的无人机鲁棒轨迹跟踪

2022 Eighth Indian Control Conference (ICC) Pub Date : 2022-12-14 DOI: 10.1109/ICC56513.2022.10093309

Saurabh Kumar, S. R. Kumar

引用次数: 0