Oscar A. Ortiz

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.

Dynamic simulation of a pilot rotary kiln for charcoal activation

Abstract This paper presents a dynamic simulation system (DSS) and a simulation study of a pilot scale rotary kiln for activated carbon manufacture. DSS has been developed by using the Simulink–Matlab framework, based on a dynamic mathematical model. The model consists of a set of nonlinear partial differential equations, and represents the mass and energy balances in the kiln. The space dimension is approximated by a backward finite difference scheme and the set of ordinary differential equation obtained is solved with a stiff solver. The changes in temperature and mass flow rate with time and space are obtained for solid, gas and wall. Several disturbances in the operating variables are tested with the DSS, and the dynamic response is analyzed. Furthermore, the performance in the start up and shut down operation mode are analyzed. The good performance exhibited by the DSS makes it suitable for controllers design and synthesis purposes.

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.

Adaptive neural model predictive control for the grape juice concentration process

The four-stage evaporator is the core of the process in the manufacture of concentrated grape juice. The dynamic features of this process are very complex due to inputs and outputs constraints, time delays, loop interactions and the persistent unmeasured disturbances that affect it. Therefore, this kind of process requires a robust control in order to assure a stable operation taking into account the changes in the organoleptic properties of the raw material and, to guarantee the quality of the concentrated product. This work proposes an adaptive neural model to control of a four-stage evaporator in a grape juice concentration plant. In order to obtain a more accurate process description the neural model is trained with data from simulation of a phenomenological model and afterwards, is validated with actual plant data. This strategy allows to carry out the training without to introduce disturbance in the real plant. Neural networks of different size are trained and the performance of one of the neural models is compared with the first principles model. In a last step, the performance of a model predictive control based on the neural model is evaluated for disturbance rejection and compared with a MPC controller based on the phenomenological model and with a PI controller. The achieved results allow us to conclude that the developed neural model predictive control is adequate to control effectively the four-stage evaporator.

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

Nonlinear control of the dissolved oxygen concentration integrated with a biomass estimator for production of Bacillus thuringiensis δ-endotoxins

Abstract Bacillus thuringiensis is a microorganism that allows the biosynthesis of δ-endotoxins with toxic properties against some insect larvae, being often used for the production of biological insecticides. A key issue for the bioprocess design consists in adequately tracking a pre-specified optimal profile of the dissolved oxygen concentration. To this effect, this paper aims at developing a novel control law based on a nonlinear dynamic inversion method. The closed-loop strategy includes an observer based on a Bayesian Regression with Gaussian Process, which is used for on-line estimating the biomass present in the bioreactor. Unlike other approaches, the proposed controller leads to an improved response time with effective disturbance rejection properties, while simultaneously prevents undesired oscillations of the dissolved oxygen concentration. Simulation results based on available experimental data were used to show the effectiveness of the proposal.

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.

Advanced Temperature Tracking Control for High Quality Wines Using a Phenomenological Model

Abstract The fermentation in winemaking has high complexity due to interactions between cell biokinetics and bioreactor hydrodynamics. Hence, the new technological options to obtain high quality wines with outstanding organoleptic characteristics require more strict process monitoring and control based on rigorous models. This work presents an advanced temperature control system based on an improved non-isothermal phenomenological model that allows tracking complex temperature profiles to achieve optimal quality of wine. The controller has been performed in discrete-time, so that the current disturbance effect at the output is computed as the difference between the current measured value of the output and the predicted one. The obtained results are satisfactory for experimental data from literature.

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.

Trajectory tracking of boiler-turbine

This paper presents the design of a controller for trajectory tracking in a boiler-turbine system. It is a MIMO system with state variables strongly coupled. The proposed controller is simple and based on concepts easy to understand. Its design consists in representing the model while using numerical methods, and then the control actions are computed through the linear algebra techniques to accomplish the target tracking. The advantage of this method is that, the condition for the tracking error tends to zero and the calculation of control actions, are obtained by solving a system of linear equations. Besides, the method has the ability to follow trajectories for two outputs simultaneously.