This paper presents the design and analysis of an intelligent control system that inherits the robust properties of sliding-mode control (SMC) for an n-link robot manipulator, including actuator dynamics in order to achieve a high-precision position tracking with a firm robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is briefy introduced. Then, a conventional SMC scheme is developed for the joint position tracking of robot manipulators. Moreover, a fuzzy-neural-network inherited SMC (FNNISMC) scheme is proposed to relax the requirement of detailed system information and deal with chattering control efforts in the SMC system. In the FNNISMC strategy, the FNN framework is designed to mimic the SMC law, and adaptive tuning algorithms for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator actuated by DC servo motors are provided to justify the claims of the proposed FNNISMC system, and the superiority of the proposed FNNISMC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.

Fuzzy-Neural-Network Inherited Sliding-Mode Control for Robot Manipulator Including Actuator Dynamics

Robust fuzzy tracking control for robotic manipulators

Hybrid feedback feedforward

This study presents the design and analysis of an intelligent control system that inherits the systematic and recursive design methodology for an n-link robot manipulator, including actuator dynamics, in order to achieve a high-precision position tracking with a firm stability and robustness. First, the coupled higher order dynamic model of an n-link robot manipulator is introduced briefly. Then, a conventional backstepping control (BSC) scheme is developed for the joint position tracking of the robot manipulator. Moreover, a fuzzy-neural-network-inherited BSC (FNNIBSC) scheme is proposed to relax the requirement of detailed system information to improve the robustness of BSC and to deal with the serious chattering that is caused by the discontinuous function. In the FNNIBSC strategy, the FNN framework is designed to mimic the BSC law, and adaptive tuning algorithms for network parameters are derived in the sense of the projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Numerical simulations and experimental results of a two-link robot manipulator that are actuated by dc servomotors are provided to justify the claims of the proposed FNNIBSC system, and the superiority of the proposed FNNIBSC scheme is also evaluated by quantitative comparison with previous intelligent control schemes.

Design of Fuzzy-Neural-Network-Inherited Backstepping Control for Robot Manipulator Including Actuator Dynamics

An adaptive control of MEMS gyroscope using global fast terminal sliding mode control (GTSMC) and fuzzy-neural-network (FNN) is presented for micro-electro-mechanical systems (MEMS) vibratory gyroscopes in this paper. This approach gives a new global fast terminal sliding surface, which will guarantee that the designed control system can reach the sliding surface and converge to equilibrium point in a shorter finite time from any initial state. In addition, the proposed adaptive global fast terminal sliding mode controller can real-time estimate the angular velocity and the damping and stiffness coefficients. Moreover, the main feature of this scheme is that an adaptive fuzzy-neural-network is employed to learn the upper bound of model uncertainties and external disturbances, so the prior knowledge of the upper bound of the system uncertainties is not required. All adaptive laws in the control system are derived in the same Lyapunov framework, which can guarantee the globally asymptotical stability of the closed-loop system. Numerical simulations for a MEMS gyroscope are investigated to demonstrate the validity of the proposed control approaches.

Adaptive control of MEMS gyroscope using global fast terminal sliding mode control and fuzzy-neural-network

This article presents a control system structure, as well as a control algorithm, that combines neural networks with fuzzy logic for dynamical compensation of both structured and unstructured uncertainties. A new fuzzy reasoning method is derived in the neural mechanism and implemented with a cerebellar model articulation controller (CMAC), which outperforms conventional fuzzy controllers by reducing computational complexity and providing a learning ability that conventional fuzzy systems do not have. The overall control system is proven to be stable. The simulation results confirm that the system can track the desired position for both set-point and dynamic tracking in the presence of uncertainties such as changing payload, various frictions, and unknown disturbances.

L. Peng

Papers

Neural-fuzzy control system for robotic manipulators