IEEE Access (Jan 2022)
A Provably Constrained Neural Control Architecture With Prescribed Performance for Fault-Tolerant Redundant Manipulators
Abstract
In this paper, a novel neural control architecture is proposed and investigated for resolving redundancy in trajectory tracking applications for manipulators with joint velocity constraints. First, a nonlinear invertible map is invoked to transform the constrained system state into a set of unconstrained variables, which allows the proposed framework to realize solutions that rigorously adhere to the specified bound constraints. Next, a quadratic program (QP) architecture is synthesized by incorporating suitably prescribed performance constraints to ensure that the resulting system error achieves exponential convergence to the ground truth while also ensuring that the system states evolve along trajectories with good transient and steady-state behavior. Thus, in contrast with previous approaches that do not rigorously guarantee the satisfaction of the bound constraints in the transient phase and/or the steady-state, the proposed scheme ensures that these constraints are rigorously satisfied while achieving prescribed performance both during the transient phase and in the steady-state. The novelty of the proposed scheme lies in the fusion of prescribed performance constraints with the state and input constraints within the QP framework, which offers the important advantage of higher computational efficiency compared to leading alternative designs. A detailed theoretical analysis is undertaken to prove the global stability and convergence of the proposed scheme. Simulation and experimental results with the KUKA LBR IIWA 14 R820 manipulator are used to verify the efficacy of the proposed scheme in accomplishing trajectory tracking for the fault-free and fault-tolerant cases with multiple joint failures. Finally, detailed performance comparison studies with leading alternative designs are further used to illustrate the advantages of the proposed scheme.
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