There is an increasing interest in using fractional calculus applied to control theory generalizingclassicalcontrol strategies as the PID controller and developing new ones with the intention of taking advantage of characteristics supplied by this mathematical tool for the controller definition. In this work, the fractional generalization of the successful and spread control strategy known as model predictive control is applied to drive autonomously a gasoline-propelled vehicle at low speeds. The vehicle is a Citroën C3 Pluriel that was modified to act over the throttle and brake pedals. Its highly nonlinear dynamics are an excellent test bed for applying beneficial characteristics of fractional predictive formulation to compensate unmodeled dynamics and external disturbances.

This paper deals with the formal formulation of Fractional-Order Generalized Predictive Control, a generalization of GPC that makes use of fractional-order operators in its cost function. A thorough mathematical description of FGPC is given and the main similarities and differences between GPC and FGPC are highlighted.

Learning to program can be very difficult for the students involved. Students must master language syntax, programming theory and problem solving techniques in a short period of time. A non-traditional approach might help students to overcome these difficulties. Several studies have proposed the use of the physical computing paradigm. This paradigm takes the computational concepts "out of the screen" and into the real world so that the student can interact with them. Following this paradigm we designed different learning modules - to be used in lectures and laboratory sessions- to teach C/C++ and MATLAB. Lecturers explain a computational concept and, afterwards, reinforce it using the physical computing modules. For example, conditional structures are illustrated using a photocell and several LEDs, arrays are explained using musical melodies, etc. The effectiveness of the physical computing modules was assessed by means of learning outcomes and students perceptions. Surveys conducted at the beginning and end of the course were analyzed using the Technological Acceptance Model (TAM). Results indicate that the students were highly motivated and found the modules very enjoyable. As a consequence we observed a significant increase in the retention rate of this course.

This work describes the fractional generalization of GPC, known as Fractional-Order Generalized Predictive Control (FGPC). Based on a real-order fractional cost function, FGPC combines valuable characteristics of fractional calculus and predictive control. The mathematical fundamentals of FGPC as well as a possible tuning algorithm are reviewed. We also present application examples where FGPC has proved to be a versatile and valuable control strategy to control real plants.