Fabian Andres Lara-Molina

Multi-Objective Design of Parallel Manipulator Using Global Indices

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized simultaneously to improve dexterity over a required workspace, since single optimization of an objective function may not ensure an acceptable design. A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (CENSGA) is used to find the Pareto front.

Multi-objective optimization of Stewart-Gough manipulator using global indices

The paper addresses the optimal design of parallel manipulators based on multi-objective optimization. The objective functions used are: Global Conditioning Index (GCI), Global Payload Index (GPI), and Global Gradient Index (GGI). These indices are evaluated over a required workspace which is contained in the complete workspace of the parallel manipulator. The objective functions are optimized simultaneously to improve dexterity over a required workspace, since single optimization of an objective function may not ensure an acceptable design. A Multi-Objective Evolution Algorithm (MOEA) based on the Control Elitist Non-dominated Sorting Genetic Algorithm (CENSGA) is used to find the Pareto front.

Robust generalized predictive control of the Orthoglide robot

The purpose of this paper is to address the synthesis and experimental application of a generalized predictive control (GPC) technique on an Orthoglide robot.Design/methodology/approach– The control strategy is composed of two control loops. The inner loop aims at linearizing the nonlinear robot dynamics using feedback linearization. The outer loop tracks the desired trajectory based on GPC strategy, which is robustified against measurement noise and neglected dynamics using Youla parameterization.Findings– The experimental results show the benefits of the robustified predictive control strategy on the dynamical performance of the Orthoglide robot in terms of tracking accuracy, disturbance rejection, attenuation of noise acting on the control signal and parameter variation without increasing the computational complexity.Originality/value– The paper shows the implementation of the robustified predictive control strategy in real time with low computational complexity on the Orthoglide robot.

Uncertainty analysis of flexible rotors considering fuzzy parameters and fuzzy-random parameters

THE COMPONENTS OF FLEXIBLE ROTORS ARE SUBJECTED TO UNCERTAINTIES. THE MAIN SOURCES OF UNCERTAINTIES INCLUDE THE VARIATION OF MECHANICAL PROPERTIES. THIS CONTRIBUTION AIMS AT ANALYZING THE DYNAMICS OF FLEXIBLE ROTORS UNDER UNCERTAIN PARAMETERS MODELED AS FUZZY AND FUZZY RANDOM VARIABLES. THE UNCERTAINTY ANALYSIS ENCOMPASSES THE MODELING OF UNCERTAIN PARAMETERS AND THE NUMERICAL SIMULATION OF THE CORRESPONDING FLEXIBLE ROTOR MODEL BY USING AN APPROACH BASED ON FUZZY DYNAMIC ANALYSIS. THE NUMERICAL SIMULATION IS ACCOMPLISHED BY MAPPING THE FUZZY PARAMETERS OF THE DETERMINISTIC FLEXIBLE ROTOR MODEL.N THEREBY, THE FLEXIBLE ROTOR IS MODELED BY USING BOTH THE FUZZY FINITE ELEMENT METHOD AND THE FUZZY STOCHASTIC FINITE ELEMENT METHOD. NUMERICAL SIMULATIONS ILLUSTRATE THE METHODOLOGY CONVEYED IN TERMS OF ORBITS AND FREQUENCY RESPONSE FUNCTIONS SUBJECT TO UNCERTAIN PARAMETERS.

Robust generalized predictive control of Stewart-Gough platform

This paper addresses the position tracking control application of a Stewart-Gough platform using robust generalized predictive control. The robustification of GPC against measurement noise using Youla parameterization is performed. The simulation of the complete model of the Stewart-Gough platform is performed on a circular trajectory. The robustified GPC controller is compared with the classical Computed Torque Control (CTC). The robustified GPC controller shows a better performance reducing the effect of the noise in the control signal of the Stewart-Gough Platform.

APPLICATION OF PREDICTIVE CONTROL TECHNIQUES WITHIN PARALLEL ROBOT

This paper addresses the position tracking control application of a parallel robot using predictive control techniques. A Generalized Predictive Control strategy (GPC), which considers the linear dynamic model, is used to enhance the tracking position accuracy. The robustification of GPC against measurement noise and neglected dynamics using Youla parameterization is performed. A simulation of the orthoglide robot considering uncertainties related to geometrical and dynamic parameters, sensors noise and frictions is performed on two different trajectories. Finally, it is compared the ro-bustified GPC controller with the classical Computed Torque Control (CTC). The robustified GPC controller shows a better performance for high accelerations and it also reduces the effect of the noise in the control signal of the parallel robot.

Robust control in rotating machinery using linear matrix inequalities

Some active vibration control methods are based on mathematical models. In these cases, parameter variations play an important role in the system performance. As it is not possible to know in advance the precise values for all parameters of the mechanical system, a possible alternative is to design robust controllers that take into account the uncertainties. In this context, this work presents a vibration active control technique devoted to rotating machinery by incorporating electromagnetic actuators, which considers uncertainties in the parameters of the system. the gains of the electromagnetic actuator are determined by using linear matrix inequalities, which consist in a powerful tool for the cases in which parameter uncertainties are taken into account. In addition, Kalman estimators are employed to deduce the modal states of the system. The model of the rotating system is obtained by using the finite element method and the potentiality of the methodology for applications in engineering was investigated through experimental tests.

Stochastic Analysis of a 6-DOF Fully Parallel Robot under Uncertain Parameters*

This paper aims at analyzing the effect of uncertain parameters on a 6-DOF fully parallel robot performance by using a stochastic approach. The uncertainties of the parameters are considered as small variations with respect to their nominal values modeled by means of random variables. The dynamics of the robot under uncertain structural and dynamic parameters including a computed torque position controller is analyzed. Additionally, a sensitivity analysis allows to determine the degree of influence of each uncertain parameter on the response of the robot. Numerical simulations illustrate the proposed methodology so that the effect of uncertain parameters on the dynamic performance of the robot is properly described.

Fuzzy Uncertainty Analysis of a Tilting-Pad Journal Bearing

This paper is dedicated to the analysis of uncertainties affecting the load capability of a 4-pad tilting-pad journal bearing, in which the load is applied between two pads (load on pad configuration; LOP). A well-known stochastic method has been extensively used to model uncertain parameters, the so-called Monte Carlo simulation. However, in the present contribution, the inherent uncertainties of the bearings’ parameters (i.e. the pad radius, the oil viscosity, and the radial clearance) are modeled by using a fuzzy logic based analysis. This alternative methodology seems to be more appropriate when the stochastic process that characterizes the uncertainties is unknown. The analysis procedure is confined to the load capability of the bearing, being generated by the envelopes of the pressure fields developed on each pad. The hydrodynamic supporting forces are determined by considering a nonlinear model, which is obtained from the solution of the Reynolds’ equation. The most significant results are associated to the changes in the dynamic behavior of the bearing because of the reaction forces that are modified according the uncertainties introduced in the system. Finally, it is worth mentioning that the uncertainty analysis in this case provides relevant information both for design and maintenance of tilting-pad hydrodynamic bearings.Copyright

Generalized Predictive Control of Parallel Robots

This study addresses the position tracking control application of a parallel robot using predictive control. A Generalized Predictive Control strategy (GPC), which considers the linear dynamic model, is used to enhance the dynamic performance. A realistic simulation of the complete model of the Orthoglide robot is performed on two different trajectories with the purpose of comparing the GPC controller with the classical Computed Torque Control (CTC) in terms of tracking accuracy. The GPC controller shows better performance for high accelerations with uncertain dynamic parameters.