Pattern-based autonomous smooth switching control for constrained flexible joint manipulator

The paper proposes a pattern-based control scheme for a class of flexible joint manipulators with unknown dynamics and position constraints. The scheme consists of two phases: (i) online controller adjustment and task pattern identification; (ii) offline pattern recognition and controller calling. In phase (i), the constraint position vectors are transformed into unconstrained variables by using a transformation function. Subsequently, a set of static neural learning controllers are constructed for different reference patterns by employing experience weights obtained from stable adaptive neural control. Moreover, a dynamic estimator is developed to identify different reference patterns. The identified patterns are stored by constant NNs, thereby constructing a trained pattern library by combining with their corresponding static neural learning controllers. In phase (ii), the dynamic residual system is designed to recognize a tested pattern by comparing the size of residual error between the tested pattern and trained patterns. To avoid misjudgment, a novel recognition strategy is proposed by pre-recognition and recognition phases. When the tested pattern is recognized, the relevant experience-based controller strategy is recalled by an autonomous smooth switching technology. The proposed pattern-based control scheme has some advantages including accurate pattern recognition ability, small controller chattering, and highly autonomous control. Simulation studies on a 2-link flexible joint manipulator are implemented to show these advantages of the proposed scheme.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The paper proposes a pattern-based control scheme for flexible joint manipulators with unknown dynamics and position constraints. The scheme consists of two phases: online controller adjustment and task pattern identification, and offline pattern recognition and controller calling. The proposed scheme has advantages including accurate pattern recognition ability, small controller chattering, and highly autonomous control.