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Read Online or Download Neural Network-Based State Estimation of Nonlinear Systems: Application to Fault Detection and Isolation PDF
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Extra resources for Neural Network-Based State Estimation of Nonlinear Systems: Application to Fault Detection and Isolation
Sample text
This is due to the fact that we cannot expect the network to follow an unbounded signal. However, the flexible-link manipulator is an unstable or marginally stable open-loop system. In fact, the linearized system has one or two poles at the origin depending on the damping coefficients. It is proposed that a joint PD controller be added to the system. The joint PD control can stabilize the closed-loop system so that boundedness of the signal x(t) can be assured. Consider the control law uc = −Kp θ − Kv θ˙ .
2 A Two-link Flexible-Joint Manipulator Simulation results for a two–link planar manipulator are presented in this section. The dynamics of a two-link manipulator are far more complicated than those of a single-link manipulator. 2 1000 1005 1010 t(s) Fig. 05sin4t reference trajectory for NLPNN: (a) motor position during the learning, (b) link position during the learning, (c) motor position at the end of learning phase, (d) link position at the end of learning phase, (e) motor velocity, (f) link velocity.
Corresponding to the Hurwitz matrix A, M(s) := (sI − A)−1 is also shown which is an n × n matrix whose elements are stable transfer functions. 4). As far as the structure of the neural network is concerned, by comparing the neuro-observer schematic in Fig. 1 with the parallel identifier scheme in Fig. 1, it can be seen that even though both receive the same inputs, the neuro-identifier is trained based on the stat estimation error. Whereas, in neuro-observer since all states are not available output error is used to train the network.
