Selflearning algorithm

Generally, we define a suitable error function using neural networks for reference, so we can adjust the control parameters by BP algorithm on-line. As is known, an AUV has its own motion will, which is very important for self-learning and will be discussed in detail in the next section, so there is also an expected motion state. Namely, there is an expected control output for S surface controller. Therefore, the error function is given by

where ud is the expected control output, and u is the last time output which can be obtained by eqution (34) .

We can use gradient descent optimization method, i.e. use the gradient of Ep to adjust fe1 and k2.

dki where n is the learning ratio (0 < n < 1 ).

Therefore, k1 and k2 can be optimized by the following eqution.

kt (t+1)=kt (t)+a^ = kt (t)+n(u - u) • -—-—j- • e (38)

We can get the expected speed by expected state programming. The expected control output can be obtained by the following principles.

If the speed v is less than or equal to vd, then u is less than ud, and u needs to be magnified. In the contrast, u needs to be reduced. The expected control output is given by ud = u + c ■ (vd - v) (39)

where c is a proper positive constant. Therefore, S surface controller has the ability of self-learning.

Was this article helpful?

0 0
Learn Photoshop Now

Learn Photoshop Now

This first volume will guide you through the basics of Photoshop. Well start at the beginning and slowly be working our way through to the more advanced stuff but dont worry its all aimed at the total newbie.

Get My Free Ebook

Post a comment