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132 Adaptive Identification and Control of Uncertain Systems with Non-smooth Dynamics

8.4.2 Experimental Results
In the experiments, the reference trajectory is given by y d = 0.6sin(2πt/5),
and comparative tracking control performances of four different controllers
are shown in Fig. 8.2.Fig. 8.2A depicts the tracking performance of the
different control schemes, while Fig. 8.2Band Fig. 8.2Cprovide thecorre-
sponding tracking errors and control signals, respectively. From Fig. 8.2,we
can see that the proposed ARFTNC method can provide better tracking
performance than the other three controllers with respect to the conver-
gence speed and steady-state error. In particular, the proposed ARFTNC
can obtain faster convergence speed than NNTSMC because of the lin-
ear term k 1s in the sliding manifold, and ARFTNC can achieve smaller
tracking error than NNLSMC due to the terminal sliding mode term
r
k 2 |s| sgn(s).

8.5 CONCLUSION
In this chapter, we present an adaptive robust finite-time neural control
scheme for uncertain PMSM servo systems with non-linear dead-zone.
The inverse compensation approach is avoided by representing the dead-
zone as a linear time-varying system. Based on a fast terminal sliding mode
principle, an adaptive control is designed by using a neural network to
handle uncertainties. In the proposed approach, the singularity problem is
eliminated by modifying the TSMC manifold and the NN approximation
error is compensated by employing a robust term. The boundedness of all
signals and the finite-time stability of the closed-loop system are guaranteed
based on the Lyapunov synthesis. Experimental results show the improved
tracking performance of the proposed method in comparison with several
other controllers.

REFERENCES

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