<p>En este trabajo se propone una estrategia de control en lazo cerrado para el seguimiento de perfiles óptimos previamente definidos para un bioproceso fed-batch. La mayor ventaja de este enfoque es que las acciones de control se calculan resolviendo un sistema de ecuaciones lineales, sin tener que linealizar el modelo matemático, lo que permite trabajar en cualquier rango. Además, se plantean tres técnicas para la sintonización de los parámetros del controlador diseñado. Primero se propone un método de Monte Carlo, el cual es un método probabilístico. En segundo lugar, se presenta una metodología basada en Algoritmos Genéticos, una técnica evolutiva de optimización. La tercera alternativa es el desarrollo de un Algoritmo Híbrido, diseñado a partir de la combinación de los dos métodos anteriores. En todos los casos, el objetivo es encontrar los parámetros del controlador que minimicen el error total de seguimiento de trayectorias. El desempeño del controlador se evalúa a través de simulaciones en condiciones normales de operación y frente a incertidumbre paramétrica, empleando los parámetros del controlador obtenidos.</p>
This work presents a novel control
technique that combines linear
algebra-based controller (LABC) methodology with sliding surface concepts.
An LABC is developed from a first-order plus dead time model of the
process, which is improved to work under uncertainties by the use
of sliding surface concepts. Two different strategies are proposed
to reject constant and variable uncertainties. The result is two linear
controllers, which are tuned using four parameters at most. Results
of the control of two level tanks connected in series, a mixing tank
with variable dead time, and a laboratory batch reactor using this
novel technique are presented, including experimental and simulated
results. The efficiency of the proposed controller is tested under
nominal operating conditions and under parametric uncertainty and
persistent process disturbances. Proof of convergence to zero of tracking
errors is analyzed and included in this article.
In this work, a controller design technique called linear algebra based controller (LABC) is presented. The controller is obtained following a systematic procedure that is summarized in this work. In addition, the influence of additive uncertainty on the tracking error is analyzed, and a solution using integrators is proposed. A mobile robot is used as a benchmark to test the performance of the proposed algorithms. In addition, implementation to other systems such as marine vessel is referenced. In this work, the design of controllers in continuous and discrete time is included and experimental and simulation results are shown in a Pioneer 3AT mobile robot. Comparisons are also shown with other controllers proposed in the literature.
This work presents a novel control technique that combines concepts of Sliding Surface with a Linear Algebra methodology for controller design. The result is a controller with an improved robustness, while the chattering effect attributed to great uncertainties is avoided. A First Order Plus Dead Time (FOPDT) model of the process is used to develop a controller based on Linear Algebra and the concept of sliding surface is used to improve its performance under uncertainties. An interesting feature of this new controller is its ability to follow variable references without overshoot, a highly desirable characteristic for most process systems, and avoids the chattering problem. Results of the control of chemical processes performed in a continuous stirred-tank reactor (CSTR) and a laboratory batch reactor using this novel technique are presented. Simulated and experimental results demonstrate the outstanding performance of this new control algorithm.
This work presents a novel controller for the dynamics of robots using a dynamic variations observer. The proposed controller uses a saturated control law based on sintg−1. function instead of tanh.. Besides, this function is an alternative to the use of tanh. in saturation control, since it reaches its maximum value more gradually than the hyperbolic tangent function. Using this characteristic, the transition between states is smoother, with similar accuracy to tanh.. The controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). The originality of this work is the use of a combination of adaptive GRNN with a sliding mode controller (SMC) including a new saturation function. Finally, experiments based on trajectory tracking demonstrate the robustness and simplicity of this method.
Bioethanol is one of the most studied alternative fuels nowadays. Due to its production process complexity and the low quality of the mathematical models that describe it, a reliable controller is needed to maximize the fuel production and minimize its environmental impact, even in the presence of uncertainty. Here, a controller for tracking optimal profiles considering model errors and external perturbations is proposed. This work is an improvement of a previously presented technique. To reduce the earlier mentioned uncertainties’ effect during the fermentation, some tracking error integrators are added in the control action calculation. This simple modification ensures the tracking error convergence to zero, even in the presence of uncertainties (demonstration available). Different tests are carried out and a performance comparison with the original controller is shown to highlight improvements in the tracking error of up to 98% when integrators are incorporated. Furthermore, a classical PI controller is contrasted with the proposed techniques.
The development of controllers for underactuated systems with nonholonomic constraints has been a topic of significant interest for many researchers in recent years. These systems are hard to control because their linearization transform them into uncontrollable systems. The proposed approaches involve the use of a permanent excitation in the reference trajectory; coordinate transformation; discontinuities; or complex calculations. This paper proposes the design of the controller of the second-order chained form system for trajectory tracking by using a simpler approach based on linear algebra. Up to the present time, no controllers based on this approach have been designed for that system. The control problem is solved by setting two of the three systems variables as a reference, while the remaining variable is calculated imposing the condition that the equations system has an exact solution to ensure that tracking errors go to zero. The stability of the proposed controller is theoretically demonstrated, and simulations results show a suitable control system performance. Also, no coordinate transformation is necessary.
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