Demostration of example 6.2: Constrained Receding horizon Control
Example retired from the book: Receding horizon Control - Model Predictive Control for State Models
published on 2007-03-28
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants.
The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France).
The version 2.0 (February 2005) handles sparse matrices and contains an example
some dummy reverse of colors and forms in a live video capture made in processing (based on java) from processing.org (free) it s funny and opens up your horizon
The idea of the book was born during the time when the second generation cellular system was looming on the horizon.At that time ,the world was divided into three distinct camps as far as looking for a standard: Europe North America and Japan.
Cooperation is not a natural characteristic attributed to humans. The typical human horizon is focused
on short-term gains, which might be due to our instinct-driven subconscious occupying a grander
importance than we dare to admit [1]. Cooperating with other individuals or entities, however, usually
means that short-term losses may translate into long-term gains – something history has proved to
hold true but humans for some reason rarely ever understand.
