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". IEEE Transactions on Control Systems Technology 2006; 14(4): 750-756. https://doi.org/10.1109/TCST.2006.872507Van M. "Adaptive neural integral sliding-mode control for tracking control of fully actuated uncertain surface vessels". International Journal of Robust and Nonlinear Control 2019; 29(5): 1537-1557. https://doi.org/10.1002/rnc.4455Wang N, Su S F,Yin J, Zheng Z, Er MJ. "Global asymptotic model-free trajectory-independent tracking control of an uncertain marine vehicle: An adaptive universe-based fuzzy control approach". Transactions on Fuzzy Systems 2017; 26(3):1613-1625. https://doi.org/10.1109/TFUZZ.2017.2737405Wang, D., Mu, C., & Liu, D. (2017, May). Neural network adaptive critic control with disturbance rejection. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 202-207). IEEE. https://doi.org/10.1109/CCDC.2017.7978092Wondergem M, Lefeber E, Pettersen KY, Nijmeijer H. "Output feedback tracking of ships". IEEE Transactions on Control Systems Technology 2010; 19(2): 442-448. https://doi.org/10.1109/TCST.2010.2045654Xu Z, Ge SS, Hu C, Hu J. "Adaptive Learning Based Tracking Control of Marine Vessels with Prescribed Performance". Mathematical Problems in Engineering 2018; 2018. https://doi.org/10.1155/2018/2595721Yang Y, Zhou C, Ren J. "Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems". Applied Soft Computing 2003; 3(4): 305-316. https://doi.org/10.1016/j.asoc.2003.05.001Yin Z, He W, Yang C. "Tracking control of a marine surface vessel with fullstate constraints". International Journal of Systems Science 2017; 48(3): 535-546. https://doi.org/10.1080/00207721.2016.1193255Yu Y, Guo C, Yu H. "Finite-time predictor line-of-sight-based adaptive neural network path following for unmanned surface vessels with unknown dynamics and input saturation". 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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

MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems under Implementation Imprecisions

The performance of a conventional model-based controller significantly depends on the accuracy of the modeled dynamics. The model of a plant's dynamics is subjected to errors in estimating the numerical values of the physical parameters, and variations over operating environment conditions and time. These errors and variations in the parameters of a model are the major sources of uncertainty within the controller structure. Digital implementation of controller software on an actual electronic control unit (ECU) introduces another layer of uncertainty at the controller inputs/outputs. The implementation uncertainties are mostly due to data sampling and quantization via the analog-to-digital conversion (ADC) unit. The failure to address the model and ADC uncertainties during the early stages of a controller design cycle results in a costly and time consuming verification and validation (V&V) process. In this paper, new formulations of the first and second order discrete sliding mode controllers (DSMC) are presented for a general class of uncertain linear systems. The knowledge of the ADC imprecisions is incorporated into the proposed DSMCs via an online ADC uncertainty prediction mechanism to improve the controller robustness characteristics. Moreover, the DSMCs are equipped with adaptation laws to remove two different types of modeling uncertainties (multiplicative and additive) from the parameters of the linear system model. The proposed adaptive DSMCs are evaluated on a DC motor speed control problem in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The results show that the proposed SISO and MIMO second order DSMCs improve the conventional SISO first order DSMC tracking performance by 69% and 84%, respectively. Moreover, the proposed adaptation mechanism is able to remove the uncertainties in the model by up to 90%.Comment: 10 pages, 11 figures, ASME 2017 Dynamic Systems and Control Conferenc

Adaptive Backstepping Controller Design for Stochastic Jump Systems

In this technical note, we improve the results in a paper by Shi et al., in which problems of stochastic stability and sliding mode control for a class of linear continuous-time systems with stochastic jumps were considered. However, the system considered is switching stochastically between different subsystems, the dynamics of the jump system can not stay on each sliding surface of subsystems forever, therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this technical note, the backstepping techniques are adopted to overcome the problem in a paper by Shi et al.. The resulting closed-loop system is bounded in probability. It has been shown that the adaptive control problem for the Markovian jump systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. A numerical example is given to show the potential of the proposed techniques

Fast Adaptive Robust Differentiator Based Robust-Adaptive Control of Grid-Tied Inverters with a New L Filter Design Method

In this research, a new nonlinear and adaptive state feedback controller with a fast-adaptive robust differentiator is presented for grid-tied inverters. All parameters and external disturbances are taken as uncertain in the design of the proposed controller without the disadvantages of singularity and over-parameterization. A robust differentiator based on the second order sliding mode is also developed with a fast-adaptive structure to be able to consider the time derivative of the virtual control input. Unlike the conventional backstepping, the proposed differentiator overcomes the problem of explosion of complexity. In the closed-loop control system, the three phase source currents and direct current (DC) bus voltage are assumed to be available for feedback. Using the Lyapunov stability theory, it is proven that the overall control system has the global asymptotic stability. In addition, a new simple L filter design method based on the total harmonic distortion approach is also proposed. Simulations and experimental results show that the proposed controller assurances drive the tracking errors to zero with better performance, and it is robust against all uncertainties. Moreover, the proposed L filter design method matches the total harmonic distortion (THD) aim in the design with the experimental result