Time-Varying Quadratic-Programming-Based Error Redefinition Neural Network Control and Its Application to Mobile Redundant Manipulators
Lunan Zheng, Zhijun Zhang
Abstract
By incorporating the redefined error monitor function into the network design, an error redefinition neural network (ERNN) is proposed to control mobile redundant manipulators to execute the tracking task in this article. The global asymptotic stability and the strong antidisturbance capability of the ERNN are proved theoretically. Furthermore, the ERNN can overcome the overshoot and constant disturbance. Meanwhile, the ERNN is input-to-state stable, while the bounded time-varying disturbance is considered as the control input.
Topics & Concepts
Control theory (sociology)Overshoot (microwave communication)Exponential stabilityComputer scienceBounded functionArtificial neural networkTracking errorQuadratic programmingConstant (computer programming)State (computer science)Recurrent neural networkStability (learning theory)Control (management)MathematicsMathematical optimizationAlgorithmArtificial intelligenceNonlinear systemProgramming languageTelecommunicationsQuantum mechanicsMathematical analysisPhysicsMachine learningAdaptive Control of Nonlinear SystemsRobotic Mechanisms and DynamicsRobotic Path Planning Algorithms