Miguel G. Villarreal-Cervantes

Robust Structure-Control Design Approach for Mechatronic Systems

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.

Differential evolution techniques for the structure-control design of a five-bar parallel robot

The present work deals with the use of a constraint-handling differential evolution algorithm to solve a nonlinear dynamic optimization problem (NLDOP) with 51 decision variables. A novel mechatronic design approach is proposed as an NLDOP, where both the structural parameters of a non-redundant parallel robot and the control parameters are simultaneously designed with respect to a performance criterion. Additionally, the dynamic model of the parallel robot is included in the NLDOP as an equality constraint. The obtained solution will be a set of optimal geometric parameters and optimal PID control gains. The optimal geometric parameters adjust the dynamic and the kinematic parameters, optimizing then, the link shapes of the robot. The proposed mechatronic design approach is applied to design simultaneously both the mechanical structure of a five-bar parallel robot and the PID controller.

Parametric reconfiguration improvement in non-iterative concurrent mechatronic design using an evolutionary-based approach

Parametric reconfiguration plays a key role in non-iterative concurrent design of mechatronic systems. This is because it allows the designer to select, among different competitive solutions, the most suitable without sacrificing sub-optimal characteristics. This paper presents a method based on an evolutionary algorithm to improve the parametric reconfiguration feature in the optimal design of a continuously variable transmission and a five-bar parallel robot. The approach considers a solution-diversity mechanism coupled with a memory of those sub-optimal solutions found during the process. Furthermore, a constraint-handling mechanism is added to bias the search to the feasible region of the search space. Differential Evolution is utilized as the search algorithm. The results obtained in a set of five experiments performed per each mechatronic system show the effectiveness of the proposed approach.

Kinematic Dexterity Maximization of an Omnidirectional Wheeled Mobile Robot: A Comparison of Metaheuristic and SQP Algorithms

Mobile robots with omnidirectional wheels are expected to perform a wide variety of movements in a narrow space. However, kinematic mobility and dexterity have not been clearly identified as an objective to be considered when designing omnidirectional redundant robots. In light of this fact, this article proposes to maximize the dexterity of the mobile robot by properly locating the omnidirectional wheels. In addition, four hybrid differential evolution (DE) algorithm based on the synergetic integration of different kinds of mutation and crossover are presented. A comparison of metaheuristic and gradient-based algorithms for kinematic dexterity maximization is also presented.

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 Sontags formula, which ensures asymptotic stability, smoothness everywhere and continuity at the equilibrium. Experimental results, compared with a computed torque control, validate the theoretical analysis.

Structure-control mechatronic design of the planar 5R 2DoF parallel robot

In this paper, a concurrent design methodology to formulate the mechatronic design problem of the planar five revolute two degrees of freedom (5R 2DoF) parallel robot and its PID controller is presented. This methodology involves a synergetic design of the mechanical structure and the control system. A nolinear dynamic optimization problem is stated for this approach and two optimization techniques, one based on a nonlinear programming technique and the other based on a novel evolutionary approach, are used to solve it. Finally, the optimal mechanical structure and controller parameters show the effectiveness of the proposed approach via simulation and experimental results.

Off-line PID control tuning for a planar parallel robot using DE variants

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.

Motion Control Design for an Omnidirectional Mobile Robot Subject to Velocity Constraints

A solution to achieve global asymptotic tracking with bounded velocities in an omnidirectional mobile robot is proposed in this paper. It is motivated by the need of having a useful in-practice motion control scheme, which takes into account the physical limits of the velocities. To this end, a passive nonlinear controller is designed and combined with a tracking controller in a negative feedback connection structure. By using Lyapunov theory and passivity tools, global asymptotic tracking with desired bounded velocities is proved. Simulations and experimental results are provided to show the effectiveness of the proposal.

Differential Evolution for the Control Gain's Optimal Tuning of a Four-bar Mechanism

In this paper the variation of the velocity error of a four-bar mechanism with spring and damping forces is reduced by solving a dynamic optimization problem using a differential evolution algorithm with a constraint handling mechanism. The optimal design of the velocity control for the mechanism is formulated as a dynamic optimization problem. Moreover, in order to compare the results of the differential evolution algorithm, a simulation experiment of the proposed control strategy was carried out. The simulation results and discussion are presented in order to evaluate the performance of both approaches in the control of the mechanism.

Differential evolution based adaptation for the direct current motor velocity control parameters

Abstract 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.