To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptiv...To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.展开更多
A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, th...A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.展开更多
Improving rollover and stability of the vehicles is the indispensable part of automotive research to prevent vehicle rollover and crashes.The main objective of this work is to develop active control mechanism based on...Improving rollover and stability of the vehicles is the indispensable part of automotive research to prevent vehicle rollover and crashes.The main objective of this work is to develop active control mechanism based on fuzzy logic controller(FLC) and linear quadratic regulator(LQR) for improving vehicle path following,roll and handling performances simultaneously.3-DOF vehicle model including yaw rate,lateral velocity(lateral dynamic) and roll angle(roll dynamic) were developed.The controller produces optimal moment to increase stability and roll margin of vehicle by receiving the steering angle as an input and vehicle variables as a feedback signal.The effectiveness of proposed controller and vehicle model were evaluated during fishhook and single lane-change maneuvers.Simulation results demonstrate that in both cases(FLC and LQR controllers) by reducing roll angle,lateral acceleration and side slip angles remain under 0.6g and 4° during maneuver,which ensures vehicle stability and handling properties.Finally,the sensitivity and robustness analysis of developed controller for varying longitudinal speeds were investigated.展开更多
基金Project(90820302) supported by the National Natural Science Foundation of ChinaProject(20110491272) supported by China Postdoctoral Science Foundation of China+2 种基金Project(2012QNZT060) supported by the Fundamental Research Fund for the Central Universities of ChinaProject(11B070) supported by the Science Research Foundation of Education Bureau of Hunan Province,ChinaProject(2010-2012) supported by the Postdoctoral Science Foundation of Central South University,China
文摘To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.
基金Project(51005253) supported by the National Natural Science Foundation of ChinaProject(2007AA04Z344) supported by the National High Technology Research and Development Program of China
文摘A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.
文摘Improving rollover and stability of the vehicles is the indispensable part of automotive research to prevent vehicle rollover and crashes.The main objective of this work is to develop active control mechanism based on fuzzy logic controller(FLC) and linear quadratic regulator(LQR) for improving vehicle path following,roll and handling performances simultaneously.3-DOF vehicle model including yaw rate,lateral velocity(lateral dynamic) and roll angle(roll dynamic) were developed.The controller produces optimal moment to increase stability and roll margin of vehicle by receiving the steering angle as an input and vehicle variables as a feedback signal.The effectiveness of proposed controller and vehicle model were evaluated during fishhook and single lane-change maneuvers.Simulation results demonstrate that in both cases(FLC and LQR controllers) by reducing roll angle,lateral acceleration and side slip angles remain under 0.6g and 4° during maneuver,which ensures vehicle stability and handling properties.Finally,the sensitivity and robustness analysis of developed controller for varying longitudinal speeds were investigated.
