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Real-time on line tuning of fuzzy controller for two-link rigid-flexible robot manipulators

机译:两连杆刚柔机器人机械手的模糊控制器实时在线调整

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摘要

In this paper, an online time self-tuning multi-input-multi-output (MIMO) fuzzy bang-bang controller (FBBC) is proposed for the control of two-link rigid and flexible robot manipulators. Two-link rigid and flexible robot manipulators are highly non-linear plants. The fuzzy control is based on the Takagi-Sugeno-type architecture fuzzy model combined with online self-tuning so that both the desired transient and steady-state responses can be achieved. The proposed FBBC is different from a fuzzy logic controller (FLC) in that it has a bi-level output like a relay but with fuzzy inputs. The online self-tuning is based on the gradient of steepest descent tuning method, which tunes the FBBC's input and output gains. The controller operation is demonstrated and compared with a classic FLC and sliding mode controller (SMC) by simulation to highlight its tracking ability and the manipulator's positioning control with rigid and flexible robot types. Based on the simulation results, the proposed controller with this tuning strategy was found to be superior at different operating conditions.
机译:本文提出了一种在线时间自调整多输入多输出(MIMO)模糊Bang-bang控制器(FBBC)来控制两链刚性和柔性机器人操纵器。两连杆刚性和柔性机器人操纵器是高度非线性的设备。模糊控制是基于Takagi-Sugeno型架构模糊模型与在线自整定相结合的,因此可以实现所需的瞬态响应和稳态响应。所提出的FBBC与模糊逻辑控制器(FLC)的不同之处在于,它具有类似于继电器的双级输出,但具有模糊输入。在线自调整是基于最速下降调整方法的梯度进行的,该方法可调整FBBC的输入和输出增益。通过仿真演示了该控制器的操作,并将其与经典的FLC和滑模控制器(SMC)进行了比较,以突出其跟踪能力以及使用刚性和柔性机器人类型的机械手的定位控制。根据仿真结果,发现具有这种调整策略的控制器在不同的工作条件下均具有优越性。

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