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Adaptive neural network control of robot based on a unified objective bound
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ADAPTIVE NEURAL NETWORK CONTROL OF ROBOT BASED ON A UNIFIED
OBJECTIVE BOUND
ABSTRACT:
In the conventional adaptive neural network control of robotic manipulator, the desired position
of robot end effector is specified as a point or trajectory. In addition, it is usually difficult to
guarantee the transient performance of adaptive neural network control system due to the
initialization error of the weight of neural network. In this paper, a new control formulation is
proposed for the adaptive neural network control of robotic manipulator, which unifies existing
neural network control tasks such as setpoint control, trajectory tracking control, and trajectory
tracking control with prescribed performance bound. The proposed method also includes a new
adaptive neural network control scheme where the objective for the robot end effector can be
specified as a dynamic region, instead of the desired position or trajectory. The stability of the
closed-loop system is analyzed using Lyapunov-like analysis. Experimental results are presented
to illustrate the performance of the proposed approach and the energy-saving property of the
proposed neural network controller with dynamic region.
