3. Objective

The basic inner loop controller of any helicopter deals with maintaining a specified height above ground, i.e. altitude, and maintaining a particular pose, or attitude. The attitude in turn allows the helicopter to translate in the x-y plane assuming altitude is held constant.

time being we will leave the 'reference model, 'adjustment mechanism' and the 'controller' as

Model Reference Adaptive Control of Quadrotor UAVs: A Neural Network Perspective

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Neural networks are one such machine learning algorithm. This sub-section will briefly cover the two broad categories of machine learning algorithms. Bear in mind that this chapter will

In supervised learning, the data is tagged with the correct values for prediction/classification associated to it. The algorithm learns by minimizing the error between its results and the correct results. This is the most common form of machine learning and the easiest, however

Neural networks, support vector machines (SVMs), linear/logistic regression and decision trees are a few examples of supervised learning algorithms and the applications could be

In unsupervised learning, the task is to find patterns or meaning in untagged data such as classifying similar data together without actually knowing what those classes may represent (clustering) or we take data in some low level/uncompressed representation and learn a high level/compressed representation with minimum information loss or we have a lot of data which mostly subscribes to a particular pattern and we would like to detect the outliers

K-means clustering, autoencoders (NN based) and principal component analysis are a few

In the twentieth century scientists were able to definitively identify the primary unit of the brain – the neuron. One theory of the time was that information is not pre-loaded in the brain of a newly born child, only the basic structure and connections between the neurons in its brain exist, the brain learns to function by strengthening/weakening various neural pathways.

labeled data is not easy to come by as its curation and tagging is usually expensive.

classification of images, weather prediction, sentiment detection, face recognition, etc.

elaborate on neural networks used in a supervised learning setting.


http://dx.doi.org/10.5772/intechopen.71487

141

black boxes, they will be revisited in Section 6.

5.1. Introduction to machine learning

5. Neural networks

5.1.1. Supervised learning

5.1.2. Unsupervised learning

(anomaly detection).

1

algorithms used for unsupervised learning tasks.

5.2. History and intuition of neural networks

Machine Learning, Tom Mitchell, McGraw Hill, 1997.

A formal definition of ML is:

The standard approach is decentralized or cascaded PID controllers for the various control variables (in this case: roll f, pitch θ, yaw ψ, altitude z), these controllers will have to be tuned for each particular quadrotor UAV. In general, any non-adaptive controller will need to be tuned to a particular quadrotor.

In this chapter we employ neural networks to design an adaptive controller that is system unspecific, i.e. it should work for any quadrotor system. It learns the system parameters online, i.e. in-flight. The challenge is to keep the system stable during online learning.
