- 作者: Yauheni B. Veryha
- 中文摘要: Error detection, diagnosis and accommodation play key roles in the operation of autonomous robotic systems. System faults, which typically result in changes of critical system parameters or system dynamics, may lead to degradation in performance. This fact is especially important for time optimal robot control when the system parameters reach their critical values and even small changes can lead to accuracy degradation. This paper investigates the problem of error diagnosis in robotic manipulators under computed torque control using neural network and fuzzy logic elements. A learning architecture with neural networks serving as on-line approximators with fuzzy logic elements in the control unit is used for the diagnosis of robotic system errors and error accommodation. Approximation using neural networks provides a model of the error characteristics that can be used for the detection and elimination of errors in robot functioning. Simulation results illustrate the ability of the neural network based error diagnosis method with the fuzzy elements described in this paper to detect and accommodate errors in a two-link robotic manipulator under time optimal control.
- 英文摘要: --
- 中文關鍵字: time optimal control, adaptive robotic system, autonomous robotic system, error detection, neural network, fuzzy logic element
- 英文關鍵字: --