Advanced Control of Wheeled Inverted Pendulum Systems is an orderly presentation of recent ideas for overcoming the complications inherent in the control of wheeled inverted pendulum (WIP) systems, in the presence of uncertain dynamics, nonholonomic kinematic constraints as well as underactuated configurations. The text leads the reader in a theoretical exploration of problems in kinematics, dynamics modeling, advanced control design techniques and trajectory generation for WIPs. An important concern is how to deal with various uncertainties associated with the nominal model, WIPs being characterized by unstable balance and unmodelled dynamics and being subject to time-varying external disturbances for which accurate models are hard to come by. The book is self-contained, supplying the reader with everything from mathematical preliminaries and the basic Lagrange-Euler-based derivation of dynamics equations to various advanced motion control and force control approaches as well as trajectory generation method. Although primarily intended for researchers in robotic control, Advanced Control of Wheeled Inverted Pendulum Systems will also be useful reading for graduate students studying nonlinear systems more generally.
In a well ordered presentation of recently identified techniques for overcoming the complications inherent in wheeled inverted pendulum (WIP) systems, this volume is a searching exploration of problems in kinematics, dynamics modeling, and other topics.
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1 | (12) |
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1.1 An Overview of WIP Robots |
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2 | (4) |
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1.2 Difficulties of Controlling WIP Systems |
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6 | (4) |
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10 | (3) |
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2 Mathematical Preliminaries |
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13 | (24) |
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13 | (1) |
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13 | (4) |
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17 | (3) |
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20 | (2) |
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22 | (2) |
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2.6 Input-to-State Stability |
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24 | (1) |
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2.7 Lyapunov's Direct Method |
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25 | (1) |
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26 | (2) |
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2.9 Controllability and Observability of Nonlinear Systems |
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28 | (3) |
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28 | (1) |
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29 | (2) |
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2.9.3 Brockett's Theorem on Feedback Stabilization |
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31 | (1) |
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31 | (5) |
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2.11 Notes and References |
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36 | (1) |
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3 Modeling of WIP Systems |
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37 | (18) |
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37 | (1) |
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3.2 Kinematics of the WIP Systems |
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38 | (3) |
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3.3 Dynamics of WIP Systems |
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41 | (10) |
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3.3.1 Lagrange-Euler Equations |
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42 | (3) |
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45 | (1) |
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46 | (1) |
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3.3.4 Lagrangian Equations |
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46 | (2) |
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3.3.5 Properties of Mechanical Dynamics |
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48 | (1) |
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3.3.6 Dynamics of Wheeled Inverted Pendulum |
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49 | (2) |
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3.4 Newton-Euler Approach |
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51 | (3) |
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54 | (1) |
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55 | (22) |
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55 | (1) |
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4.2 Linearization of the WIP Dynamics |
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56 | (2) |
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58 | (1) |
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4.4 LQR based Optimal Control Design |
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59 | (1) |
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60 | (3) |
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4.5.1 Riccati-Based H∞ Control |
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61 | (1) |
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4.5.2 LMI-Based H∞ Control |
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62 | (1) |
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63 | (2) |
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65 | (6) |
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65 | (1) |
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66 | (2) |
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4.7.3 H∞-Like Riccati Control |
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68 | (2) |
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4.7.4 LMI-Based H∞ Control |
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70 | (1) |
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4.7.5 Backstepping Control |
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71 | (1) |
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71 | (6) |
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77 | (22) |
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77 | (1) |
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78 | (1) |
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78 | (2) |
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5.4 Nonlinear Feedback Linearization |
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80 | (1) |
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5.5 Model Based Control Design |
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80 | (6) |
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5.6 Model-Based Disturbance Rejection Control |
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86 | (6) |
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92 | (5) |
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97 | (2) |
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99 | (28) |
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99 | (1) |
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99 | (10) |
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6.2.1 Adaptive Robust Control Design |
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100 | (7) |
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6.2.2 Zero-Dynamics Stability Analysis |
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107 | (1) |
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108 | (1) |
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6.3 Hybrid Force and Motion Control |
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109 | (18) |
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6.3.1 Preliminaries and Dynamics Transformation |
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113 | (3) |
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6.3.2 Motion Control of z2 and z3-Subsystems |
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116 | (4) |
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6.3.3 Stability Analysis of the z1-Subsystem |
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120 | (2) |
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122 | (1) |
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122 | (2) |
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124 | (3) |
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127 | (48) |
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127 | (1) |
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128 | (16) |
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129 | (2) |
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7.2.2 Reduced Dynamics and Physical Properties |
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131 | (1) |
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7.2.3 LS-SVM Based Model Learning |
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132 | (2) |
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7.2.4 LS-SVM Based Control Design |
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134 | (7) |
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141 | (3) |
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144 | (13) |
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145 | (1) |
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7.3.2 Functional Universal Approximation Using FLSs |
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145 | (2) |
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7.3.3 Adaptive Fuzzy Control |
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147 | (8) |
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155 | (2) |
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7.4 Neural Network Output Feedback Control |
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157 | (4) |
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159 | (1) |
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7.4.2 Neural Networks and Parametrization |
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160 | (1) |
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161 | (14) |
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7.5.1 Output Feedback Control |
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164 | (1) |
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165 | (5) |
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170 | (1) |
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171 | (4) |
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8 Optimized Model Reference Adaptive Control |
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175 | (18) |
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175 | (1) |
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176 | (2) |
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8.2.1 Finite Time Linear Quadratic Regulator |
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176 | (1) |
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177 | (1) |
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8.3 Dynamics of Wheeled Inverted Pendulums |
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178 | (1) |
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8.4 Control of ξ1 and ξ3-Subsystems |
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179 | (7) |
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179 | (1) |
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8.4.2 Optimal Reference Model |
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180 | (2) |
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8.4.3 Model Matching Error |
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182 | (1) |
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8.4.4 Adaptive Control Design |
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183 | (1) |
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8.4.5 Controller Structure |
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183 | (1) |
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8.4.6 Control Performance Analysis |
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184 | (2) |
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8.5 Reference Trajectory Generator for ξ2 Subsystem |
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186 | (1) |
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186 | (5) |
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191 | (2) |
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9 Neural Network Based Model Reference Control |
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193 | (18) |
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193 | (1) |
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193 | (2) |
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9.2.1 Radial Basis Function Neural Network |
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193 | (1) |
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9.2.2 Block Matrix Operation |
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194 | (1) |
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9.3 Dynamics of Wheeled Inverted Pendulums |
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195 | (1) |
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9.4 Control of Angular Motion Subsystems |
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196 | (8) |
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9.4.1 Optimized Reference Model |
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196 | (2) |
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9.4.2 Adaptive NN Model Reference Control |
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198 | (6) |
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9.5 Adaptive Generator of Implicit Control Trajectory |
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204 | (3) |
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207 | (1) |
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208 | (3) |
| References |
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211 | (6) |
| Index |
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217 | |
Zhijun Li is an Associate Professor with the Department of Automation, Shanghai Jiaotong University. He received the Dr Eng Degree in mechatronics, Shanghai Jiao Tong University, PR China, in 2002. From 2003 to 2005, he was a postdoctoral fellow in the Department of Mechanical Engineering and Intelligent systems of The University of Electro-Communications, Tokyo, Japan. From 2005 to 2006, he was a research fellow in the Department of Electrical and Computer Engineering, National University of Singapore, and Nanyang Technological University, Singapore. Currently, he is an associate professor in the Department of Automation, Shanghai Jiao Tong University, PR China. Dr. Li is a senior member of the IEEE and his current research interests are adaptive/robust control, mobile manipulators, nonholonomic systems, etc. Chenguang Yang obtained the Bachelor's degree in Measurement and Control Instrument and Technology at the Northwestern Polytechnical University in 2001 and the PhD degree in Control Theory at the National University of Singapore in 2010. From 2005 to 2009, he was working on adaptive/intelligent controllers for highly uncertain systems. In 2008, he worked for the TechX Challenge Urban Mobile Robot Competition organized by DSTA, Singapore, which is equivalent of the DARPA Grand Challenge in US. Together with teammates, he has developed a fully autonomous robot capable of climbing stairs, recognizing and operating an elevator, and navigating and searching for given targets in an unknown environment. From 2009 until 2010 he was with the Human Robotics Group in Imperial College London working on modeling of human neural-muscular control and development of robot control of human motor behavior. In connection with this he has developed a human-mimetic force- and impedance-adaptation algorithm for robots performing both stable and unstable tasks. Dr. Yangs current research interests are in the fields of intelligent robotic control, nonlinearcontrol and humanrobot interaction. He is the author of more than twenty-five peer-reviewed journal and conference papers. He received Honorable Mention in the Mathematical Contest in Modeling (the US competition supported by supported by NSF and NSA), 2004. Since 2006, he has been serving as reviewer of a number of top journals such as the IEEE Transactions on Automatic Control, Automatica and the IEEE Transactions on Neural Networks. He is a member of the Technical Program Committee of the 2011 Chinese Control and Decision Conference (2011 CCDC) and of the IEEE. Liping Fan received his B.S.in the Dept of Automation, Xiameng University in 1996, and from 2002, he is the founder of Shanghai Shengdisi Automation Equipment Co., LTD, and worked as CEO of the company until now, currently, he is the part-time graduate student of Dept of Auttomation, Shanghai Jiao Tong University. His research insterests are mechatronics system development, and control system design.
