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
This work presents a new approach to tune the parameters of the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Sequential Quadratic Programming (SQP), thus yielding a dual optimization scheme. Simulations and performance indexes for a non minimum linear model result in a better performance, improving robustness and tracking accuracy.

Abstract
This paper proposes the recently introduced Gravitational Search Algorithm (GSA) to tune a Sliding Mode Controller (SMC) applied on the temperature control of a grains drying system. The problem of maintaining the temperature precisely adjusted inside a silo is relevant to avoid thermal damage and spoilage losses, and thus guarantee the right conditions for storage. The objectives of setpoint tracking and disturbance rejection are incorporated into the minimization of the integral of the time-weighted absolute error. Simulation results are presented and compared with PID and with SMC tuned by Particle SwarmOptimization (PSO) and by earlier proposed tuning equations. © Springer International Publishing Switzerland 2017.

Abstract
The gravitational search algorithm (GSA) is reviewed, by presenting a tutorial analysis of its key issues. As any other metaheuristic, GSA requires the selection of some heuristic parameters. One parameter which is crucial in regulating the exploratory capabilities of this algorithm is the gravitational constant. An analysis regarding this parameter selection is presented and a heuristic rule proposed for this purpose. The GSA performance is compared both with a hybridization with particle swarm optimization (PSO) and standard PSO. Preliminary simulation results are presented considering simple continuous functions optimization examples.

Abstract
This work presents an automatic tuning method for the discontinuous component of the Sliding Mode Generalized Predictive Controller (SMGPC) subject to constraints. The strategy employs Particle Swarm Optimization (PSO) to minimize a second aggregated cost function. The continuous component is obtained by the standard procedure, by Quadratic Programming (QP), thus yielding an online dual optimization scheme. Simulations and performance indexes for common process models in industry, such as nonminimum phase and time delayed systems, result in a better performance, improving robustness and tracking accuracy.