FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs

FPGA-Based Parallel DNA Algorithm for Optimal Configurations of an Omnidirectional Mobile Service Robot Performing Fire Extinguishment

This paper presents a coarse-grain parallel deoxyribonucleic acid (PDNA) algorithm for optimal configurations of an omnidirectional mobile robot with a five-link robotic arm. This efficient coarse-grain PDNA is proposed to search for the global optimum of the redundant inverse kinematics problem with minimal movement, thereby showing better population diversity and avoiding premature convergence. Moreover, the pipelined hardware implementation, hardware/software co-design, and System-on-a-Programmable-Chip (SoPC) technology on a fieldprogrammable gate array (FPGA) chip are employed to realize the proposed PDNA in order to significantly shorten its processing time. Simulations and experimental results are conducted to illustrate the merit and superiority of the proposed FPGA-based PDNA algorithm in comparison with conventional genetic algorithms (GAs) for omnidirectional mobile robot performing fire extinguishment

SoPC-Based Adaptive Motion Control, Optimal Configurations and Global Path Planning for Three-Wheel Omnidirectional Mobile Service Robot

本論文旨在發展植基於系統可程式晶片(SoPC)的三輪全方位行動服務機器人之適應運動控制、最佳組態和全域路徑規劃。卡氏座標空間和極座標空間的適應運動控制器經由適應倒逆步法被合成出來，並完成軌跡追踨和穩定。更進一步的，一個平行DNA演算法 (PDNA) 和平行精英基因演算法 (PEGA) 被發展來解決全方位機器人的冗餘問題和全域路徑規劃問題。一個coarse-grain平行化模型不僅被用在PDNA演算法求解全方位行動服務機器人的最佳組態問題並執行滅火任務，而且也用在PEGA求解全域路徑規劃問題。被發展出來的二個適應運動控制器、PDNA演算法、PEGA也都有效的利用硬體/軟體 協同設計和SoPC技術植基於FPGA晶片內。可重覆使用的IP (Intellectual Property) 元件庫也快速的在FPGA內發展出來並整合同一晶片內的嵌入式處理器和嵌入式即時作業系統，驅動行動機器人追踨軌跡和解決行動機器人的最佳化問題。經由模擬和實驗結果，這二個植基於SoPC 的卡氏座標和極座標的適應動態控制器比傳統的全方位行動平台控制器性能更好。更進一步的，植基於SoPC所發展出來的PDNA 演算法和PEGA 也被證明能更有效的解決最佳化的問題。本論文所提出來的技術能夠提供在自主性行動機器領域的研究中，值得參考的方法。This dissertation presents SoPC-based embedded adaptive motion controllers in both Cartesian and polar coordinates, optimal configuration, and global path planning for three-wheel omnidirectional mobile service robot. The adaptive Cartesian-space and polar-space motion controllers are synthesized via adaptive backstepping to achieve both trajectory tracking and stabilization. Moreover, a parallel deoxyribonucleic acid (PDNA) algorithm and a parallel elite genetic algorithm (PEGA) are presented to solve the redundant inverse kinematic problem and global path planning problem for the omnidirectional mobile robot. A coarse-grain parallel model is not only used to the PDNA algorithm for optimal configurations of an omnidirectional mobile service robot performing fire extinguishment task, but also applied to the PEGA for global path planning. The proposed two adaptive motion controllers, coarse-grain PDNA algorithm and PEGA have been efficiently implemented into field-programmable gate array (FPGA) chips using the hardware/software co-design technique and SoPC (System-on-a-Programmable-Chip) technique. The reusable IP (Intellectual Property) core library has been rapidly developed in FPGA chips by incorporating with the embedded processor and the real-time operating system (RTOS) in the same chip to drive the mobile robot to follow the desired trajectory and solving the optimal problems for omnidirectional mobile robot. Through simulations and experimental results, the proposed SoPC-based adaptive controllers in both Cartesian and polar space outperform conventional controllers for three-wheel omnidirectional mobile platform. Furthermore, the proposed SoPC-based PDNA algorithm and PEGA are shown more powerful to solve the optimal problems. The proposed techniques may provide useful references for professionals working in the field of autonomous mobile robotics.Contents Acknowledgements i Chinese Abstract ii English Abstract iii Contents iv List of Figures x List of Tables xv Nomenclature xvi List of Acronyms xvii Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Literature Review 5 1.2.1 Related Work for Omnidirectional Mobile Robot 5 1.2.2 Related Work for Optimal Algorithms Solving Redundant Problem and Path Planning Problem of Omnidirectional Mobile Robots ………………6 1.2.3 Related Work for SoPC-Based Embedded System Design 9 1.3 Motivation and Objectives 10 1.4 Contributions of the Dissertation 11 1.5 Organization of the Dissertation 13 Chapter 2 SoPC Implementation of an Embedded Robust Adaptive Controller for Autonomous Omnidirectional Mobile Platform 2.1 Introduction 14 2.2 Brief Description of the Dynamic Model with Slip 15 2.3 Adaptive Robust Controller Design 18 2.3.1 Robust Controller Design 19 2.3.2 Adaptive Robust Controller 21 2.4 Extension to Path Following 23 2.5 FPGA Implementation of the Proposed Adaptive Controller 25 2.5.1 FPGA Implementation of the Proposed Embedded Adaptive Robust Controller 25 2.5.2 The User IP Core Library Design 28 2.5.2.1 Clock Divider Module 28 2.5.2.2 Digital Filter Module 29 2.5.2.3 QEP Circuit Module 29 2.5.3 Embedded Adaptive Controller 30 2.6 Simulations, Experimental Results and Discussion 32 2.6.1 Simulation Results and Discussion 32 2.6.1.1 Adaptive Elliptical Trajectory Tracking 32 2.6.1.2 Adaptive Parabola Path Following 35 2.6.2 Experimental Results and Discussion 36 2.6.2.1 System Architecture of the Experimental Omnidirectional Mobile Service Robot 36 2.6.2.2 Point Stabilization (Regulation) 38 2.6.2.3 Robust Adaptive Elliptic Trajectory Tracking 41 2.6.2.4 Robust Adaptive Triangle and Window Trajectory Tracking 42 2.6.2.5 Fetch-and-Carry Task Execution 42 2.7 Concluding Remarks 46 Chapter 3 SoPC-Implemented Adaptive Polar-Space Controller for Omnidirectional Mobile Platform with Dynamic Effect and Uncertainties 3.1 Introduction 47 3.2 Dynamic Model in Polar Coordinates 48 3.3 Adaptive Dynamic Controller Design 52 3.4 FPGA Implementation 56 3.4.1 Embedded Adaptive Controller 56 3.4.2 Circuits Design 59 3.4.2.1 Clock Generator Module 59 3.4.2.2 QEP Circuit Module 60 3.4.3 Software Implementation of the Control Law 60 3.5 Simulations, Experimental Results and Discussion 62 3.5.1 Simulations Results and Discussion 62 3.5.1.1 Adaptive Limacon of Pascal Trajectory Tracking 62 3.5.1.2 Adaptive Rose Curve Trajectory Tracking 63 3.5.2 Experimental Results and Discussion 63 3.5.2.1 System Architecture of the Experimental Omnidirectional Mobile Service Robot 63 3.5.2.2 Polar-Space Adaptive Archimedes' Spiral and Rose Curve Trajectory Tracking 69 3.5.2.3 Fetch-and-Carry Task Execution via Trajectory Tracking and Image Processing 74 3.6 Concluding Remarks 74 Chapter 4 SoPC-Based Parallel DNA Algorithm for Optimal Configurations of an Omnidirectional Mobile Service Robot Performing Fire Extinguishment 4.1 Introduction 75 4.2 Parallel DNA Algorithm 76 4.2.1 DNA Computing 76 4.2.1.1 Coding Scheme 76 4.2.1.2 Selection 77 4.2.1.3 Crossover 77 4.2.1.4 Mutation (Enzyme and Virus Operation) 77 4.2.1.5 Fitness function 78 4.2.2 Coarse-Grain PDNA Algorithm 78 4.3 FPGA Implementation of the Proposed PDNA Algorithm 79 4.3.1 RNG Module 82 4.3.2 Selection Module 83 4.3.3 Crossover Module 84 4.3.4 Mutation Module (Enzyme and Virus Operation) 84 4.3.5 Fitness Module 85 4.3.6 Migration Module 85 4.4 Application to Redundant Inverse Kinematic Problem with Minimum Movement for Omnidirectional Mobile Service Robot 85 4.4.1 The Redundant Inverse Kinematic Problem 85 4.4.2 FPGA-Based PDNA Algorithm for Point-to-Point Task Planning 90 4.5 Experimental Results and Discussion 92 4.5.1 Fitness Value in PDNA Computing 92 4.5.2 Performance Evaluation of the FPGA-based PDNA 94 4.5.3 Brief Description of the Experimental Omnidirectional Mobile Service Robot Performing Fire Extinguishment 95 4.5.4 Fire Extinguishment Task Execution 96 4.6 Concluding Remarks 98 Chapter 5 SoPC-Based Parallel Elite Genetic Algorithm for Global Path Planning of an Autonomous Omnidirectional Mobile Service Robot 5.1 Introduction 100 5.2 Parallel Elite Genetic Algorithm 101 5.2.1 Elite GA Computing 101 5.2.1.1 Mutation 102 5.2.1.2 Reproduction Strategy with Elite Policy 103 5.2.1.3 Diversity Increasing in Population Pool 103 5.2.2 Coarse-Grain Parallel Elite Genetic Algorithm 104 5.3 FPGA-Implemented PEGA 106 5.4 Global Path Planning for Omnidirectional Mobile Robot 108 5.4.1 Global Path Planning Using PEGA 108 5.4.2 Structure of the Omnidirectional Mobile Service Robot 110 5.4.3 FPGA-Based PEGA for Global Path Planning 111 5.5 Simulations, Experimental Results and Discussion 113 5.5.1 Simulation Result of Fitness Value in Elite GA Computing 113 5.5.2 Simulation Results of Global Path Planning 114 5.5.3 Experimental Results of Global Path Planning 114 5.5.3.1 Experimental Results of the FPGA-Based PEGA Global Path Planning 114 5.5.3.2 Performance Evaluation of the FPGA-based PEGA 116 5.6 Concluding Remarks 119 Chapter 6 Conclusions and Future Work 6.1 Conclusions 120 6.2 Future Work 121 Bibliography 12