Mario Emanuel Serrano

Trajectory Tracking of Underactuated Surface Vessels: A Linear Algebra Approach

This brief presents the design of a controller that allows an underactuated vessel to track a reference trajectory in the x-y plane. A trajectory tracking controller designed originally for robotic systems is applied for underactuated surface ships. Such a model is represented by numerical methods and, from this approach, the control actions for an optimal operation of the system are obtained. Its main advantage is that the condition for the tracking error tends to zero, and the calculation of control actions are obtained solving a system of linear equations. The proofs of convergence to zero of the tracking error are presented here and complete the previous work of the authors. Simulation results show the good performance of the proposed control system.

Tracking control of concentration profiles in a fed-batch bioreactor using a linear algebra methodology

Based on a linear algebra approach, this paper aims at developing a novel control law able to track reference profiles that were previously-determined in the literature. A main advantage of the proposed strategy is that the control actions are obtained by solving a system of linear equations. The optimal controller parameters are selected through Monte Carlo Randomized Algorithm in order to minimize a proposed cost index. The controller performance is evaluated through several tests, and compared with other controller reported in the literature. Finally, a Monte Carlo Randomized Algorithm is conducted to assess the performance of the proposed controller.

Trajectory-tracking controller design with constraints in the control signals: a case study in mobile robots

Fil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingenieria. Instituto de Ingenieria Quimica; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas; Argentina

Control of a Fed-Batch Fermenter Based on a Linear Algebra Strategy

Based on a linear algebra approach, this paper aims at developing a novel control law able to track reference profiles that were previously-determined to optimize the protein production in a fed-batch fermenter. A main advantage of the proposed strategy is that the control actions are obtained by solving a system of linear equations. The optimal controller parameters are selected through Monte Carlo Experiments in order to minimize a proposed cost index. The controller performance is evaluated through several tests, and compared with other controller reported in the literature.

Energy evaluation of low-level control in UAVs powered by lithium polymer battery

Nowadays, the energetic cost of flying in electric-powered UAVs is one of the key challenges. The continuous evolution of electrical energy storage sources is overcome by the great amount of energy required by the propulsion system. Therefore, the on-board energy is a crucial factor that needs to be further analyzed. In this work, different control strategies applied to a generic UAV propulsion system are considered and a lithium polymer battery dynamic model is included as the propulsion system energy source. Several simulations are carried out for each control strategy, and a quantitative evaluation of the influence of each control law over the actual energy consumed by the propulsion system is reported. This energy, which is delivery by the battery, is next compared against a well-known control-effort-based index. The results and analysis suggest that conclusions regarding energy savings based on control effort signals should be drawn carefully, because they do not directly represent the actual consumed energy.

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

Nonlinear Trajectory Tracking Control for Marine Vessels with Additive Uncertainties

In this paper trajectory tracking problem in marine vessels under model errors isaddressed. In the wake of the results obtained in Serrano et al. (2013), the problem of modelerrors is focused and the zero convergence of tracking errors under polynomial uncertainties isdemonstrated. A simple design method is given, which can be easily implemented. Simulationresults are presented and discussed, showing the good performance of the controller. DOI: http://dx.doi.org/10.5755/j01.itc.47.1.17782

A nonlinear trajectory tracking controller for mobile robots with velocity limitation via parameters regulation

This paper addresses the problem of trajectory tracking control in mobile robots under velocity limitations. Following the results reported in ref. [ 1 ], the problem of trajectory tracking considering control actions constraint is focused and the zero convergence of the tracking errors is demonstrated. In this work, the original methodology is expanded considering a controller that depends not only on the position but also on the velocity. A simple scheme is obtained, which can be easily implemented in others controllers of the literature. Experimental results are presented and discussed, demonstrating the good performance of the controller.

Tracking multivariable optimal profiles of induced foreign protein production by recombinant bacteria in a fed-batch reactor

Bioprocesses are difficult to control, especially if one considers the fed-batch operation with more than one variable of control. Mathematical models include stiff and nonlinear differential equations, which are complex when designing a suitable driver for the system. This paper aims to implement a simple but efficient technique based on a linear algebra approach that allows an adequate tracking of optimal profiles of state variables making easier the design of the control system with very satisfactory results. The biological system presented has two control variables and two manipulated variables. The performance of the proposed controller is tested through simulations.

Trajectory tracking in a nonlinear CSTR. Controller design based on a linear algebra approach

This work presents a novel linear algebra methodology able to adequately design the controller algorithm for an efficient trajectory tracking of a continuous stirred tank reactor (CSTR). The mathematical model of the CSTR is represented by a system of coupled nonlinear differential equations, which is then approximated through a discrete statement of the problem. The discrete model is reorganized as a system of linear equations, which is used to find out a simple solution for the optimal control actions. An advantage of the present approach is that both the condition of negligible tracking error and the calculation of control actions are obtained by solving a system of linear equations. A Monte Carlo method is used for tuning the controller adjustment parameters. The simulation results show a good performance of the proposed control law.