Robot Fault Estimation Based on Iterative Learning and Fuzzy Models
A robot fault estimation strategy based on iterative learning and fuzzy models is proposed to address the problem of actuator faults in flexible robots.Firstly,in order to handle the nonlinearity of the robot system,the robot system is transformed into a Takagi Sugeno(T-S)fuzzy system.Secondly,based on the T-S fuzzy system and the repetitive motion characteristics of the robot,a fuzzy observer and a fuzzy iterative learning fault estimation algorithm are designed to achieve accurate estimation of the robot state and actuator faults simultaneously.Once again,propose solvable conditions to ensure the robust monotonic convergence of the fuzzy iterative learning fault estimation algorithm,so that the iterative estimation algorithm has good transient behavior.Finally,a single link flexible joint robot is used to verify the effectiveness of the proposed method.
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