Adaptive Controller Tuning Method Based on Online Multiobjective Optimization: A Case Study of the Four-Bar Mechanism.

The efficient speed regulation of four-bar mechanisms is required for many industrial processes. These mechanisms are hard to control due to the highly nonlinear behavior and the presence of uncertainties or disturbances. In this paper, different Pareto-front approximation search approaches in the adaptive controller tuning based on online multiobjective metaheuristic optimization are studied through their application in the four-bar mechanism speed regulation problem. Dominance-based, decomposition-based, metric-driven, and hybrid search approaches included in the algorithms, such as nondominated sorting genetic algorithm II, multiobjective evolutionary algorithm based on decomposition and differential evolution, S-metric selection evolutionary multiobjective algorithm, and nondominated sorting genetic algorithm III, respectively, are considered in this paper. Also, a proposed metric-driven algorithm based on the differential evolution and the hypervolume indicator (HV-MODE) is incorporated into the analysis. The comparative descriptive and nonparametric statistical evidence presented in this paper shows the effectiveness of the adaptive controller tuning based on online multiobjective metaheuristic optimization and reveals the advantages of the metric-driven search approach.

Evolutionary Optimization of Ensemble Learning to Determine Sentiment Polarity in an Unbalanced Multiclass Corpus.

Sentiment polarity classification in social media is a very important task, as it enables gathering trends on particular subjects given a set of opinions. Currently, a great advance has been made by using deep learning techniques, such as word embeddings, recurrent neural networks, and encoders, such as BERT. Unfortunately, these techniques require large amounts of data, which, in some cases, is not available. In order to model this situation, challenges, such as the Spanish TASS organized by the Spanish Society for Natural Language Processing (SEPLN), have been proposed, which pose particular difficulties: First, an unwieldy balance in the training and the test set, being this latter more than eight times the size of the training set. Another difficulty is the marked unbalance in the distribution of classes, which is also different between both sets. Finally, there are four different labels, which create the need to adapt current classifications methods for multiclass handling. Traditional machine learning methods, such as Naïve Bayes, Logistic Regression, and Support Vector Machines, achieve modest performance in these conditions, but used as an ensemble it is possible to attain competitive execution. Several strategies to build classifier ensembles have been proposed; this paper proposes estimating an optimal weighting scheme using a Differential Evolution algorithm focused on dealing with particular issues that multiclass classification and unbalanced corpora pose. The ensemble with the proposed optimized weighting scheme is able to improve the classification results on the full test set of the TASS challenge (General corpus), achieving state of the art performance when compared with other works on this task, which make no use of NLP techniques.

Periodic Event-Triggered Control strategy for a (3,0) mobile robot network.

This work deals with the development of a nonlinear Periodic Event-Triggered Control strategy employed to the consensus of a multi-vehicle autonomous system based on (3,0) mobile robots. First, the existence of the Control Lyapunov Function (CLF) applicable to the consensus problem is proven. This is subsequently used to develop event and feedback functions. The Periodic Event-Triggered Control ensures trajectories boundedness and convergence to consensus while a specific sampling period is provided. Also, the formation problem is addressed as an extension of the presented work. Experimental results show the performance of the proposed control strategy which reduces 99.78% the number of control updates compared to a continuous control law, resulting in energy saving for the information transfer from central control to the mobile robots.

Stabilization of a (3,0) mobile robot by means of an event-triggered control.

Event-triggered control (ETC) is a sampling strategy that updates the control value only when some events related to the state of the system occurs. It therefore relaxes the periodicity of control updates without deteriorating the closed-loop performance. This paper develops a nonlinear ETC for the stabilization of a (3,0) mobile robot. The construction of an event function and a feedback function is carried out based on the existence of a stabilizing control law and a Control Lyapunov Function (CLF). The event function is dependent on the time derivative of the CLF and the feedback function results from the extension of Sontag's formula, which ensures asymptotic stability, smoothness everywhere and continuity at the equilibrium. Experimental results, compared with a computed torque control, validate the theoretical analysis.