Abstract
This study presents deterministic learning from adaptive neural control of unknown electrically-driven mechanical systems. An adaptive neural network system and a high-gain observer are employed to derive the controller. The stable adaptive tuning laws of network weights are derived in the sense of the Lyapunov stability theory. It is rigorously shown that the convergence of partial network weights to their optimal values and locally accurate NN approximation of the unknown closed-loop system dynamics can be achieved in a stable control process because partial Persistent Excitation (PE) condition of some internal signals in the closed-loop system is satisfied. The learned knowledge stored as a set of constant neural weights can be used to improve the control performance and can also be reused in the same or similar control task. Numerical simulation is presented to show the effectiveness of the proposed control scheme.
Highlights
The motion tracking control of uncertain mechanical systems described by a set of second-order differential equations has attracted the interest of researchers over the years
Many works addressing the tracking problem of mechanical systems with actuator dynamics have been described in Dawson et al (1998), Su and Stepanenko (1996, 1998), Chang (2002) and Driessen (2006)
In Wai and Chen (2004, 2006), robust neural fuzzy network control was derived for robot manipulators including actuator dynamics, favorable tracking performance was obtained for complex robot systems
Summary
The motion tracking control of uncertain mechanical systems described by a set of second-order differential equations has attracted the interest of researchers over the years. Many works addressing the tracking problem of mechanical systems with actuator dynamics have been described in Dawson et al (1998), Su and Stepanenko (1996, 1998), Chang (2002) and Driessen (2006) These works were based on the integrator backstepping technique. Based on the universal approximation ability of Neural Networks (NNs) and fuzzy neural networks, adaptive neural/fuzzy neural control schemes have been developed to treat the tracking control of uncertain electro-mechanical systems (Kwan et al, 1998; Huang et al, 2003, 2008; Kuc et al, 2003; Wai and Chen, 2004, 2006; Wai and Yang, 2008). In Wai and Yang (2008), an adaptive FNN controller with only joint position information was designed to cope with the problem caused by the assumption of all system state variables
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