The structure-control design approach of mechatronic systems requires a different design formulation where the mechanical structure and control system are simultaneously designed. Optimization ...problems are commonly stated to confront the structure-control design formulation. Nevertheless, these problems are often very complex with a highly nonlinear dependence between the design variables and performance functions. This fact has made the use of evolutionary algorithms, a feasible alternative to solve the highly nonlinear optimization problem; the method to find the best solution is an open issue in the structure-control design approach. Hence, this paper presents a mechanism to exhaustively exploit the solutions in the differential evolution (DE) algorithm in order to find more non-dominated solutions with uniformly distributed Pareto front and better trade-offs in the structure-control design framework. The proposed approach adopts an external population to retain the non-dominated solutions found during the evolutionary process and includes a mechanism to mutate the individuals in their corresponding external population region. As a study case, the structure-control design of a serial-parallel manipulator with its control system is stated as a dynamic optimization problem and is solved by using the proposed approach. A comparative analysis shows that the multi-objective exhaustive exploitation differential evolution obtained a superior performance in the structure-control design framework than a DE algorithm which did not consider the proposal. Hence, the resulting designs provide better trade-offs between the structure-control performance functions.
The dc motor is one of the most fundamental electromechanical devices of mechatronic systems, which plays an important role in maintaining the accuracy in the execution of tasks. One of the main ...issues in the accuracy and robustness of dc motor control system is how to optimally tune its parameters. In this paper, a multi-objective online tuning optimization approach is proposed to adaptively tune up the velocity control parameters of the permanent magnet dc motor. This approach simultaneously considers the modeled error and the corresponding sensitivity to choose the best compromise solution in the Pareto dominance-based selection process of solutions to deal the changing optimum solutions in the dynamic environment of the tuning approach based on online optimization method and moreover, the modified differential evolution with induced initial population based on non-dominated solution through a memory is proposed to guide the search into the feasible region, and to promote the exploitation of solutions found in the previous time interval. Simulation results verify that proposed modifications provide higher robustness and better quality in the velocity regulation control of the dc motor under parametric uncertainties, and also under discontinuous dynamic load, than multi-objective differential evolution, particle swarm optimization, and non-dominated sorting genetic algorithm-II.
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.
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.
•The PID control tuning method for a parallel robot based on a dynamic optimization is stated.•The constraint and method (C&M) included into DE variants efficiently handle unstable individuals.•The ...convergence time of the DE variants is decreased by including the C&M.•Laboratory testing validates the proposed PID control tuning.
Optimization methods have shown to be a very important approach for control engineers. They emulate the decision-making ability of a human expert to tune the control gains for a process or system with the formulation and solution of a mathematical optimization problem. In such formulation, evolutionary algorithms (EAs) have been widely used to obtain the control gains. Nevertheless a bad selection of the control gains through the optimization process can result in instability of the closed-loop control system such that the convergence and diversity in the EAs can be compromised. In this paper the PID control tuning for a planar parallel robot with a five-bar mechanism to follow a highly nonlinear trajectory is stated as an off-line nonlinear dynamic optimization problem (OLNLDOP). In order to promote individuals with a stable behavior in the closed-loop control system, a dynamic constraint and a method to handle such constraint is proposed into the OLNLDOP and into eight different variants of the differential evolution algorithm, respectively. Comparative analysis shows that the proposal finds suitable solutions for the OLNLDOP with a better convergence time. Laboratory testing with the optimum PID control gains on a real prototype validates the tuning optimization method.
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.
•A Periodic Event-Triggered Control (PETC) schema is addressed for the formation problem of a multi-vehicle network.•In the multi-vehicle network each vehicle is represented by a (3,0) mobile robot.•The PETC design is based on the existence of a CLF and the Sontag’s formula.•The proposed framework provides the selection of an explicit sampling period.•Real-time tests validate the proposed control strategy reducing 99.78% the number of times of control updates.
The growth in usage of efficient mobile robots in engineering has motivated the search for new alternatives to improve the control tuning task. In this article, Cartesian space ...proportional-derivative control tuning for omnidirectional mobile robots is established under an offline dynamic optimization approach wherein the minimization of the tracking error and energy consumption are considered simultaneously, providing efficient performance in real tests. A statistical study of the performance of twelve different meta-heuristic algorithms and one gradient technique indicates that using the fittest solution in the meta-heuristic optimization process through generations allows finding more suitable controller parameters. Also, real tests with each of the best control gains obtained using algorithms are realized as a laboratory prototype. Analysis of laboratory tests indicate that, statistically,
of comparisons with the best control gains exhibit different performance functions in spite of having only slightly different control gains.
In this paper, a robust formulation for the structure-control design of mechatronic systems is developed. The proposed robust approach aims at minimization of the sensitivity of the nominal design ...objectives with respect to uncertain parameters. The robust integrated design problem is established as a nonlinear multiobjective dynamic optimization one, which in order to consider synergetic interactions uses mechanical and control nominal design objectives. A planar parallel robot and its controller are simultaneously designed with the proposed approach when the nominal design objectives are the tracking error and the manipulability measure. The payload at the end-effector is considered as the uncertain parameter. Experimental results show that a robustly designed parallel robot presents lower sensitivity of the nominal design objectives under the effects of changes at the payload than a nonrobustly designed one.
The adaptive design of the control system for a direct current motor is solved by proposing differential evolution based control adaptation (DEBAC). From the comparison of two differential evolution ...variants with two constraint-handling techniques, a competitive algorithm based on arithmetic crossover and a set of feasibility rules is obtained. In addition, a comparison of such competitive differential evolution variant against a traditional control technique considering stabilization and tracking is provided. Based on the empirical results, the proposed approach outperforms the traditional method by using three well-known performance indices for closed-loop control, confirming that DEBAC is a valid alternative to control the direct current motor under parametric uncertainties.
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.
•Nonlinear event-triggered feedback (ETC) for mobile robot stabilization is proposed.•The asymptotic stability of the event-triggered closed-loop system is ensured.•ETC reduces control function calls without deteriorating the closed-loop performance.•The ETC is experimentally applied to the stabilization of a (3,0) robot mobile.
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