GP_MRAC_Code

Code for testing a Model Reference Adaptive Control (MRAC) scheme to control the pitch of a plane under wing-rock dynamics, accounting for modeling errors by using Gaussian Process (GP) regression. Implementation based on [1].

Disclaimer

This is an implementation of the method presented in [1] I presented for a Parameter Inference and State Estimation graduate class. I don't claim ownership over the methodology presented in [1].

Files

• GP_KL_MRAC_simple_test.m : Code for testing a Model Reference Adaptive Control (MRAC) scheme to control the pitch of a plane under wing-rock dynamics, accounting for modeling errors by using Gaussian Process regression (hence, the algorithm is called GP-MRAC). In this case, the data in the training set will be discarded based on Kullback–Leibler (KL) divergence.
• GP_OL_MRAC_simple_test.m : Code for testing a Model Reference Adaptive Control (MRAC) scheme to control the pitch of a plane under wing-rock dynamics, accounting for modeling errors by using Gaussian Process regression (hence, the algorithm is called GP-MRAC). In this case, the oldest data point in the training set will be discarded.
• RBFN_MRAC_simple_test.m : Code for testing a Model Reference Adaptive Control (MRAC) scheme to control the pitch of a plane under wing-rock dynamics, accounting for modeling errors by using a Gaussian Radial Basis Function Network (RBFN).
• MRAC_Ideal_Tuner.m : Code for tuning Model Reference Adaptive Controller (MRAC) Gains (K), without taking into account modelling errors (ideal case). Thus, Wing-Rock Dynamics are represented by a double integrator plant
• Reference_Model_Tuner.m : Code for tuning reference model response for Model Reference Adaptive Control (MRAC) reference tracking.
• Bayesian Nonparametric MRAC Using GPR_Final_Report.pdf :
• Bayesian Nonparametric MRAC Using GPR_Presentation.ppt : Power Point presentation I used to present the results from using GP-MRAC to control the pitch of a plane under wing-rock dynamic.
• Project_Proposal.pdf : Document I used to propose the GP-MRAC implementation as a final project for the Parameter Inference and State Estimation course to the instructor.

Citing work

• [1] G. Chowdhary, H. A. Kingravi, J. P. How, and P. A. Vela,. "Bayesian nonparametric adaptive control using gaussian processes," IEEE Transactions on Neural Networks and Learning Systems, vol. 26, pp. 537-550, March 2015.