Sliding Mode Control for Trajectory Tracking of a Non-holonomic Mobile Robot using Adaptive Neural Networks

In this work a sliding mode control method for a non-holonomic mobile robot using an adaptive neural network is proposed. Due to this property and restricted mobility, the trajectory tracking of this system has been one of the research topics for the last ten years. The proposed control structure combines a feedback linearization model, based on a nominal kinematic model, and a practical design that combines an indirect neural adaptation technique with sliding mode control to compensate for the dynamics of the robot. A neural sliding mode controller is used to approximate the equivalent control in the neighbourhood of the sliding manifold, using an online adaptation scheme. A sliding control is appended to ensure that the neural sliding mode control can achieve a stable closed-loop system for the trajectory-tracking control of a mobile robot with unknown non-linear dynamics. Also, the proposed control technique can reduce the steady-state error using the online adaptive neural network with sliding mode control; the design is based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling mobile robots with large dynamic uncertaintiesFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Estabelecimento de Estrategias de Controle Inteligente na Lamina ao de produtosPlanos

Este trabalho de tese propõe e explicita uma estratégia de controle neural para o processo de controle de variação da espessura da tira num trem de laminação a quente. A qualidade do produto laminado depende da minimização da variação da espessura da tira e da coroa da mesma. O modelo do sistema, extremamente complexo, é apresentado numa formulação matemática e serve de base para um ambiente de simulação, desenvolvido para apoiar a validação das estratégias proposta, que também pode ser utilizado no desenvolvimento de outras estratégias. A estratégia proposta apresenta um melhor desempenho quando comparada com os resultados reais do controle convencional de um trem de laminação, gentilmente fornecidos pela SIDERAR S. A., siderúrgica da Argentina

Adaptive neural dynamic compensator for mobile robots in trajectory tracking control

In the present paper, it will be reported original results concerning the application of Neural Networks (NN) in mobile robot in trajectory tracking control. This work combines a feedback linearization based on a nominal model and an NN adaptive dynamic compensation. In mobile robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematic controller and an inverse dynamic controller. The uncertainty in the nominal dynamic model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The learning laws were deduced by Lyapunovs stability analysis. Finally, the performance of the control system is verified through experiments.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentin

Neural network-based compensation control of mobile robots with partially known structure

This study proposes an inverse non-linear controller combined with an adaptive neural network proportional integral (PI) sliding mode using an on-line learning algorithm. The neural network acts as a compensator for a conventional inverse controller in order to improve the control performance when the system is affected by variations on their dynamics and kinematics. Also, the proposed controller can reduce the steady-state error of a non-linear inverse controller using the on-line adaptive technique based on Lyapunov’s theory. Experimental results show that the proposed method is effective in controlling dynamic systems with unexpected large uncertainties.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Model Reference Adaptive Control for Mobile Robots in Trajectory Tracking Using Radial Basis Function Neural Networks

This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Patiño, Daniel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Carelli Albarracin, Ricardo Oscar. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Design and implementation of adaptive NeuralPID for non linear dynamics in mobile robots

In this work, it will be reported original results concerning the application of PID Adaptive Neural controller in mobile robot in trajectory tracking control. In this control strategy the exact dynamical model of the robot will not need to be known and identified. To implement this strategy, two controllers are implemented separately: a kinematic controller and an adaptive neural PID controller. The uncertainty and dynamics variations in the robot dynamic are compensated by an adaptive neural PID controller. The resulting adaptive neural PID controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance. The stability of the proposed technique (based on Lyapunov’s theory) was demonstrated. Finally, experiments on a mobile robot have been developed to show the performance of the proposed technique, including the comparison with other controllers.Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; ArgentinaFil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; Argentin

Neural Dynamics Variations Observer Designed for Robot Manipulator Control Using a Novel Saturated Control Technique

(is work presents a novel controller for the dynamics of robots using a dynamic variations observer. (e proposed controller uses a saturated control law based on sin(tg− 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(.). (e controller is designed using a saturated SMC (sliding mode controller) and a dynamic variations observer based on GRNN (general regression neural network). (e 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.Fil: Rossomando, Francisco Guido. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Serrano, Mario Emanuel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Soria, Carlos Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentin

Real-time neuro-adaptive PI control of soil moisture using a hybrid model

[EN] In the agriculture developed in the mountain valleys of Argentina, the efficient use of water for irrigation is essential for the development and sustainability of agricultural enterprises. In order to address this challenge, it is proposed to develop a hybrid model to represent as faithfully as possible the dynamics of water content in an irrigated soil, including water extraction by a crop. For this purpose, a mathematical model of the process is formulated based on the general flow equation, which has been solved by means of finite differences. A radial-based neural network is incorporated into this structure to compensate off-line the model output at a point on the ground, identifying the output error. In addition, this study incorporates the design of an adaptive irrigation controller for unknown dynamics. The design is based on sliding surfaces in combination with PI and neural networks, with  the goal of control objective is to maintain the soil water content at reference values setting. [ES] En la agricultura que se desarrolla en los valles cordilleranos de Argentina, el uso eficiente del agua destinada para el riego es fundamental para el desarrollo y sustentabilidad de los emprendimientos agrícolas. A fin de abordar este desafío, se propone lograr un modelo híbrido que permita representar con la mayor fidelidad posible la dinámica del contenido de agua en un suelo bajo riego por goteo, incluyendo la extracción de agua por parte de un cultivo. Para esto, se cuenta con la formulación de un  modelo matemático del proceso basado en la ecuación general de flujo, la cual ha sido resuelta mediante diferencias finitas. Se incorpora a esta estructura una red neuronal de base radial (RBF) para compensar de manera off-line la salida del modelo en un punto del suelo, identificando el error de salida. Además, este estudio incorpora el diseño de un controlador de riego de tipo adaptable para dinámicas desconocidas. El diseño está basado en superficies deslizantes en combinación PI y redes neuronales, siendo el objetivo de control mantener el contenido de agua en el suelo a determinado valor de referencia establecido.Este trabajo ha sido realizado gracias al apoyo de la Universidad Nacional de San Juan y del Consejo Nacional de Investigaciones científicas y Técnicas (CONICET) de Argentina.Gomez, J.; Rossomando, F.; Capraro, F.; Soria, C. (2022). Control PI neuro-adaptable en tiempo real de la humedad en el suelo usando un modelo híbrido. Revista Iberoamericana de Automática e Informática industrial. 20(1):93-103. https://doi.org/10.4995/riai.2022.171069310320

Mixed control for trajectory tracking in marine vessels

[EN] This work proposes the design of an adaptive controller for a marine vessel; the proposed control strategy applies a controller designed on linear algebra for the kinematics and an adaptive control technique for the dynamic part of the vessel. The linear algebra based controller (LABC) for kinematics receives the desired position references and this generates another reference velocity pair for the adaptive (dynamic) controller. The main goal of the application of the adaptive control technique in this kind of enforcement is presented in the case that the mass of the vessel varies with its trajectory (e.g. fishing vessel, refueling vessel, etc.) where the adaptive controller adjusts its parameters through of adaptation law, which in turn generates a control action that compensates dynamic variations of the ship. Besides, this work presents the stability analysis and adaptive adjustment law based on the Lyapunov theory. And the simulation results that are presented prove that the control can deal with non-linearities and time-variant dynamics.[ES] Este trabajo muestra el diseño de un controlador adaptable para un buque marino; la estrategia de control que se propone es la aplicación de un controlador basado en álgebra lineal para la cinemática y una técnica de control adaptable para la parte dinámica del buque. El controlador basado en álgebra lineal (LABC) para cinemática recibe las referencias de posición deseadas y esto genera otro par de velocidad de referencia para el controlador adaptable (dinámico). El objetivo principal de la aplicación de la técnica de control adaptable se presenta en el caso de que la masa del buque varíe con su trayectoria (por ejemplo, buque pesquero, buque de reabastecimiento de combustible, etc.) donde el controlador adaptable ajusta sus parámetros mediante la ley de adaptación, que a su vez genera una acción de control que compensa las variaciones dinámicas del buque. Además, este trabajo presenta el análisis de estabilidad y la ley de ajuste adaptable basada en la teoría de Lyapunov. Los resultados de simulación muestran que el sistema puede seguir las señales de referencia con un error muy bajo aún en presencia de incertidumbre.Vacca Sisterna, C.; Serrano, E.; Scaglia, G.; Rossomando, F. (2021). Control mixto para el seguimiento de trayectoria en buques marinos. Revista Iberoamericana de Automática e Informática industrial. 19(1):27-36. https://doi.org/10.4995/riai.2021.15027OJS2736191Cui R, Chen L, Yang C, Chen M. "Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities". IEEE Transactions on Industrial Electronics 2017; 64(8): 6785-6795. https://doi.org/10.1109/TIE.2017.2694410Dai SL, He S, Lin H. "Transverse function control with prescribed performance guarantees for underactuated marine surface vehicles". International Journal of Robust and Nonlinear Control 2019; 29(5): 1577-1596. https://doi.org/10.1002/rnc.4453Do K, Jiang ZP, Pan J. "Universal controllers for stabilization and tracking of underactuated ships". Systems & Control Letters 2002; 47(4): 299-317. https://doi.org/10.1016/S0167-6911(02)00214-1Fossen T. "Marine control systems. Marine cybernetics". Trondhiem, Norway 2002.Fu M,Wang T,Wang C. "Adaptive Neural-Based Finite-Time Trajectory Tracking Control for Underactuated Marine Surface Vessels With Position Error Constraint".IEEE Access 2019; 7: 16309-16322. https://doi.org/10.1109/ACCESS.2019.2895053Ghommam J, Mnif F, Derbel N. "Global stabilization and tracking control of underactuated surface vessels". IET control theory & applications 2010; 4(1): 71-88. https://doi.org/10.1049/iet-cta.2008.0131Ghommam J, Mnif F, Benali A, Derbel N. "Asymptotic backstepping stabilization of an underactuated surface vessel". IEEE Transactions on Control Systems Technology 2006; 14(6): 1150-1157. https://doi.org/10.1109/TCST.2006.880220He W, Yin Z, Sun C. "Adaptive neural network control of a marine vessel with constraints using the asymmetric barrier Lyapunov function".IEEE transactions on cybernetics 2016; 47(7): 1641-1651. https://doi.org/10.1109/TCYB.2016.2554621Hu X, Du J, Zhu G, Sun Y. "Robust adaptive NN control of dynamically positioned vessels under input constraints". Neurocomputing 2018; 318: 201-212. https://doi.org/10.1016/j.neucom.2018.08.056Liao Yl, Wan L, Zhuang Jy. "Backstepping dynamical sliding mode control method for the path following of the underactuated surface vessel". Procedia Engineering 2011; 15: 256-263. https://doi.org/10.1016/j.proeng.2011.08.051Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., & BastosFilho, T. F. (2008). An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice, 16(11), 1354-1363. https://doi.org/10.1016/j.conengprac.2008.03.004Nie J, Lin X. "Robust Nonlinear Path Following Control of UnderactuatedMSV With Time-Varying Sideslip Compensation in the Presence of Actuator Saturation and Error Constraint". IEEE Access 2018; 6: 71906-71917. https://doi.org/10.1109/ACCESS.2018.2881513Scaglia, Gustavo; Serrano, Emanuel; Albertos, Pedro (2020). Control de Trayectorias Basado en Algebra Lineal. Revista Iberoamericana de Automática e Informática industrial, [S.l.], ago. 2020. ISSN 1697-7920. Disponible en: https://polipapers.upv.es/index.php/RIAI/article/view/13584. https://doi.org/10.4995/riai.2020.13584Scaglia Gustavo, Serrano Mario Emanuel, Albertos Pedro (2020). "Linear Algebra Based Controller - Design and Applications". Publisher: Springer International Publishing. eBook ISBN 978-3-030-42818-1. Hardcover ISBN 978-3-030-42817-4. DOI 10.1007/978-3-030-42818-1.Scaglia, G., Mut, V., Rosales, A., Quintero, O., "Tracking Control of a Mobile Robot using Linear Interpolation", Proceeding of the 3rd International Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2007. vol. 1, pp. 11-15, ISBN: 978-2-9520712-7-7 February 8-10, 2007Serrano M.E., Scaglia G.J.E., Auat Cheein F., Mut V. and Ortiz O.A. (2015).Trajectory-tracking controller design with constraints in the control signals: a case study in mobile robots. Robotica, 33, pp 2186-2203, diciembre 2015. https://doi.org/10.1017/S0263574714001325Serrano ME, Godoy SA, Gandolfo D, Mut V, Scaglia G. "Nonlinear Trajectory Tracking Control for Marine Vessels with Additive Uncertainties". Information Technology And Control 2018; 47(1): 118-130. https://doi.org/10.5755/j01.itc.47.1.17782Tee KP, Ge SS. "Control of fully actuated ocean surface vessels using a class of feedforward approximators". 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State estimation and trajectory tracking control for a nonlinear and multivariable bioethanol production system

In this paper a controller is proposed based on linear algebra for a fed-batch bioethanol production process. It involves fnding feed rate profles (control actions obtained as a solution of a linear equations system) in order to make the system follow predefned concentration profles. A neural network states estimation is designed in order to know those variables that cannot be measured. The controller is tuned using a Monte Carlo experiment for which a cost function that penalizes tracking errors is defned. Moreover, several tests (adding parametric uncertainty and perturbations in the control action) are carried out so as to evaluate the controller performance. A comparison with another controller is made. The demonstration of the error convergence, as well as the stability analysis of the neural network, are included.Fil: Fernández, Maria Cecilia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Pantano, Maria Nadia. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Ortiz, Oscar Alberto. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; ArgentinaFil: Scaglia, Gustavo Juan Eduardo. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Ingeniería Química; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin