Abstract
The brain emotional learning (BEL) control paradigm has been gathering increased interest by the control systems design community. However, the lack of a consistent mathematical formulation and computer based tools are factors that have prevented its more widespread use. In this article both features are tackled by providing a coherent mathematical framework for both the continuous and discrete-time formulations and by presenting a SIMULINK (R) computational tool that can be easily used for fast prototyping BEL based control systems.

Abstract
Water level regulation of irrigation canals represents a major challenge for control systems design. Those systems exhibit large dynamic variations in their operating conditions. To overcome this fact, robust controllers should be applied. The sliding mode control paradigm reveals this ability which make it a suitable candidate to be incorporated in the irrigation canal control loop. Moreover, its flexibility can be further potentiated by extending the ordinary formulation by adding fractional-order integro-differential operations. In this work, fractional-order sliding mode control is applied to the above mentioned problem. This application represents a novelty and, according to the obtained simulation results, leads to an accurate and proper performance when compared to its integer-order counterpart and to a fractional proportional-integrative controller, recently proposed for this problem.

Abstract
Renewable sources of energy play a decisive role in the current energetic paradigm to mitigate climate changes associated with greenhouse gases emissions and problems of energy security. Biomass energy and in particular forest wood biomass supply chains have the potential to enhance these changes due to its several benefits such as ability to produce both bioenergy and bioproducts, generate energy on-demand, among others. However, this energy source has some drawbacks mainly associated with the involved costs. In this work, the use of a Model Predictive Control approach is proposed to plan, monitor and control the wood-biomass supply chain for energy production at a tactical level. With this methodology the biomass supply chain becomes more efficient ensuring the service quality in a more competitive way. In order to test and validate the proposed approach different simulation scenarios were considered that proved the efficiency of the proposed tool regarding the decisions definition and control.

Abstract

Abstract
Half-cycle Posicast Control is currently used in a vast range of applications. Although the proved benefits of this technique, one of its major disadvantages concerns model uncertainties. This has motivated the development and integration of robust methods to overcome this issue. In this paper, a practical experiment for auto-tuning of a two degrees of freedom control configuration using a Half-Cycle Posicast pre-filter (or input-shaping), and a PID controller under parametric variations is presented. The proposed method requires using an oscillatory system model in an auto-tuning control structure. The error derivative among the model and system output is used to trigger both the identification and retuning procedure. The proposed method is flexible for choosing identification plus optimization methods. Practical results obtained for electronic filter plants suggest improved performance for the considered cases. © 2018

Abstract
This paper evaluates a robust Model Predictive Controller (MPC) based on Sliding Modes (SMPC) for the downstream level control in irrigation canal pools. Its features are compared with the conventional Generalized Predictive Controller (GPC), regarding set point tracking (water level) and output disturbances (offtake discharges). Simulation results suggest feasibility of applying SMPC for gate manipulation, with suitable command signals and robustness.