A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems

This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version

Variance-constrained multiobjective control and filtering for nonlinear stochastic systems: A survey

The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixed H 2 / H ∞ control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out

Sliding mode control for robust and smooth reference tracking in robot visual servoing

[EN] An approach based on sliding mode is proposed in this work for reference tracking in robot visual servoing. In particular, 2 sliding mode controls are obtained depending on whether joint accelerations or joint jerks are considered as the discontinuous control action. Both sliding mode controls are extensively compared in a 3D-simulated environment with their equivalent well-known continuous controls, which can be found in the literature, to highlight their similarities and differences. The main advantages of the proposed method are smoothness, robustness, and low computational cost. The applicability and robustness of the proposed approach are substantiated by experimental results using a conventional 6R industrial manipulator (KUKA KR 6 R900 sixx [AGILUS]) for positioning and tracking tasks.Spanish Government, Grant/Award Number: BES-2010-038486; Generalitat Valenciana, Grant/Award Number: BEST/2017/029 and APOSTD/2016/044Muñoz-Benavent, P.; Gracia, L.; Solanes, JE.; Esparza, A.; Tornero, J. (2018). Sliding mode control for robust and smooth reference tracking in robot visual servoing. International Journal of Robust and Nonlinear Control. 28(5):1728-1756. https://doi.org/10.1002/rnc.3981S17281756285Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5), 651-670. doi:10.1109/70.538972Chaumette, F., & Hutchinson, S. (2008). Visual Servoing and Visual Tracking. Springer Handbook of Robotics, 563-583. doi:10.1007/978-3-540-30301-5_25Corke, P. (2011). Robotics, Vision and Control. Springer Tracts in Advanced Robotics. doi:10.1007/978-3-642-20144-8RYAN, E. P., & CORLESS, M. (1984). Ultimate Boundedness and Asymptotic Stability of a Class of Uncertain Dynamical Systems via Continuous and Discontinuous Feedback Control. IMA Journal of Mathematical Control and Information, 1(3), 223-242. doi:10.1093/imamci/1.3.223Chaumette, F., & Hutchinson, S. (2006). Visual servo control. I. Basic approaches. IEEE Robotics & Automation Magazine, 13(4), 82-90. doi:10.1109/mra.2006.250573Chaumette, F., & Hutchinson, S. (2007). Visual servo control. II. Advanced approaches [Tutorial]. IEEE Robotics & Automation Magazine, 14(1), 109-118. doi:10.1109/mra.2007.339609Bonfe M Mainardi E Fantuzzi C Variable structure PID based visual servoing for robotic tracking and manipulation 2002 Lausanne, Switzerland https://doi.org/10.1109/IRDS.2002.1041421Solanes, J. E., Muñoz-Benavent, P., Girbés, V., Armesto, L., & Tornero, J. (2015). On improving robot image-based visual servoing based on dual-rate reference filtering control strategy. Robotica, 34(12), 2842-2859. doi:10.1017/s0263574715000454Elena M Cristiano M Damiano F Bonfe M Variable structure PID controller for cooperative eye-in-hand/eye-to-hand visual servoing 2003 Istanbul, Turkey https://doi.org/10.1109/CCA.2003.1223145Hashimoto, K., Ebine, T., & Kimura, H. (1996). Visual servoing with hand-eye manipulator-optimal control approach. IEEE Transactions on Robotics and Automation, 12(5), 766-774. doi:10.1109/70.538981Chan A Leonard S Croft EA Little JJ Collision-free visual servoing of an eye-in-hand manipulator via constraint-aware planning and control 2011 San Francisco, CA, USA https://doi.org/10.1109/ACC.2011.5991008Allibert, G., Courtial, E., & Chaumette, F. (2010). Visual Servoing via Nonlinear Predictive Control. Lecture Notes in Control and Information Sciences, 375-393. doi:10.1007/978-1-84996-089-2_20Kragic, D., & Christensen, H. I. (2003). Robust Visual Servoing. The International Journal of Robotics Research, 22(10-11), 923-939. doi:10.1177/027836490302210009Mezouar Y Chaumette F Path planning in image space for robust visual servoing 2000 San Francisco, CA, USA https://doi.org/10.1109/ROBOT.2000.846445Morel, G., Zanne, P., & Plestan, F. (2005). Robust visual servoing: bounding the task function tracking errors. IEEE Transactions on Control Systems Technology, 13(6), 998-1009. doi:10.1109/tcst.2005.857409Hammouda, L., Kaaniche, K., Mekki, H., & Chtourou, M. (2015). Robust visual servoing using global features based on random process. International Journal of Computational Vision and Robotics, 5(2), 138. doi:10.1504/ijcvr.2015.068803Yang YX Liu D Liu H Robot-self-learning visual servoing algorithm using neural networks 2002 Beijing, China https://doi.org/10.1109/ICMLC.2002.1174473Sadeghzadeh, M., Calvert, D., & Abdullah, H. A. (2014). Self-Learning Visual Servoing of Robot Manipulator Using Explanation-Based Fuzzy Neural Networks and Q-Learning. Journal of Intelligent & Robotic Systems, 78(1), 83-104. doi:10.1007/s10846-014-0151-5Lee AX Levine S Abbeel P Learning Visual Servoing With Deep Features and Fitted Q-Iteration 2017Fakhry, H. H., & Wilson, W. J. (1996). A modified resolved acceleration controller for position-based visual servoing. Mathematical and Computer Modelling, 24(5-6), 1-9. doi:10.1016/0895-7177(96)00112-4Keshmiri, M., Wen-Fang Xie, & Mohebbi, A. (2014). Augmented Image-Based Visual Servoing of a Manipulator Using Acceleration Command. IEEE Transactions on Industrial Electronics, 61(10), 5444-5452. doi:10.1109/tie.2014.2300048Edwards, C., & Spurgeon, S. (1998). Sliding Mode Control. doi:10.1201/9781498701822Zanne P Morel G Piestan F Robust vision based 3D trajectory tracking using sliding mode control 2000 San Francisco, CA, USAOliveira TR Peixoto AJ Leite AC Hsu L Sliding mode control of uncertain multivariable nonlinear systems applied to uncalibrated robotics visual servoing 2009 St. Louis, MO, USAOliveira, T. R., Leite, A. C., Peixoto, A. J., & Hsu, L. (2014). Overcoming Limitations of Uncalibrated Robotics Visual Servoing by means of Sliding Mode Control and Switching Monitoring Scheme. Asian Journal of Control, 16(3), 752-764. doi:10.1002/asjc.899Li, F., & Xie, H.-L. (2010). Sliding mode variable structure control for visual servoing system. International Journal of Automation and Computing, 7(3), 317-323. doi:10.1007/s11633-010-0509-5Kim J Kim D Choi S Won S Image-based visual servoing using sliding mode control 2006 Busan, South KoreaBurger W Dean-Leon E Cheng G Robust second order sliding mode control for 6D position based visual servoing with a redundant mobile manipulator 2015 Seoul, South KoreaBecerra, H. M., López-Nicolás, G., & Sagüés, C. (2011). A Sliding-Mode-Control Law for Mobile Robots Based on Epipolar Visual Servoing From Three Views. IEEE Transactions on Robotics, 27(1), 175-183. doi:10.1109/tro.2010.2091750Parsapour, M., & Taghirad, H. D. (2015). Kernel-based sliding mode control for visual servoing system. IET Computer Vision, 9(3), 309-320. doi:10.1049/iet-cvi.2013.0310Xin J Ran BJ Ma XM Robot visual sliding mode servoing using SIFT features 2016 Chengdu, ChinaZhao, Y. M., Lin, Y., Xi, F., Guo, S., & Ouyang, P. (2016). Switch-Based Sliding Mode Control for Position-Based Visual Servoing of Robotic Riveting System. Journal of Manufacturing Science and Engineering, 139(4). doi:10.1115/1.4034681Moosavian, S. A. A., & Papadopoulos, E. (2007). Modified transpose Jacobian control of robotic systems. Automatica, 43(7), 1226-1233. doi:10.1016/j.automatica.2006.12.029Sagara, S., & Taira, Y. (2008). Digital control of space robot manipulators with velocity type joint controller using transpose of generalized Jacobian matrix. Artificial Life and Robotics, 13(1), 355-358. doi:10.1007/s10015-008-0584-7Khalaji, A. K., & Moosavian, S. A. A. (2015). Modified transpose Jacobian control of a tractor-trailer wheeled robot. Journal of Mechanical Science and Technology, 29(9), 3961-3969. doi:10.1007/s12206-015-0841-3Utkin, V., Guldner, J., & Shi, J. (2017). Sliding Mode Control in Electro-Mechanical Systems. doi:10.1201/9781420065619Utkin, V. (2016). Discussion Aspects of High-Order Sliding Mode Control. IEEE Transactions on Automatic Control, 61(3), 829-833. doi:10.1109/tac.2015.2450571Romdhane, H., Dehri, K., & Nouri, A. S. (2016). Discrete second-order sliding mode control based on optimal sliding function vector for multivariable systems with input-output representation. International Journal of Robust and Nonlinear Control, 26(17), 3806-3830. doi:10.1002/rnc.3536Sharma, N. K., & Janardhanan, S. (2017). Optimal discrete higher-order sliding mode control of uncertain LTI systems with partial state information. International Journal of Robust and Nonlinear Control. doi:10.1002/rnc.3785LEVANT, A. (1993). Sliding order and sliding accuracy in sliding mode control. International Journal of Control, 58(6), 1247-1263. doi:10.1080/00207179308923053Levant, A. (2003). Higher-order sliding modes, differentiation and output-feedback control. International Journal of Control, 76(9-10), 924-941. doi:10.1080/0020717031000099029Bartolini, G., Ferrara, A., & Usai, E. (1998). Chattering avoidance by second-order sliding mode control. IEEE Transactions on Automatic Control, 43(2), 241-246. doi:10.1109/9.661074Siciliano, B., Sciavicco, L., Villani, L., & Oriolo, G. (2009). Robotics. Advanced Textbooks in Control and Signal Processing. doi:10.1007/978-1-84628-642-1Deo, A. S., & Walker, I. D. (1995). Overview of damped least-squares methods for inverse kinematics of robot manipulators. Journal of Intelligent & Robotic Systems, 14(1), 43-68. doi:10.1007/bf01254007WHEELER, G., SU, C.-Y., & STEPANENKO, Y. (1998). A Sliding Mode Controller with Improved Adaptation Laws for the Upper Bounds on the Norm of Uncertainties. Automatica, 34(12), 1657-1661. doi:10.1016/s0005-1098(98)80024-1Yu-Sheng Lu. (2009). Sliding-Mode Disturbance Observer With Switching-Gain Adaptation and Its Application to Optical Disk Drives. IEEE Transactions on Industrial Electronics, 56(9), 3743-3750. doi:10.1109/tie.2009.2025719Chen, X., Shen, W., Cao, Z., & Kapoor, A. (2014). A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. Journal of Power Sources, 246, 667-678. doi:10.1016/j.jpowsour.2013.08.039Cong, B. L., Chen, Z., & Liu, X. D. (2012). On adaptive sliding mode control without switching gain overestimation. International Journal of Robust and Nonlinear Control, 24(3), 515-531. doi:10.1002/rnc.2902Taleb, M., Plestan, F., & Bououlid, B. (2014). An adaptive solution for robust control based on integral high-order sliding mode concept. International Journal of Robust and Nonlinear Control, 25(8), 1201-1213. doi:10.1002/rnc.3135Zhu, J., & Khayati, K. (2016). On a new adaptive sliding mode control for MIMO nonlinear systems with uncertainties of unknown bounds. International Journal of Robust and Nonlinear Control, 27(6), 942-962. doi:10.1002/rnc.3608Hafez AHA Cervera E Jawahar CV Hybrid visual servoing by boosting IBVS and PBVS 2008 Damascus, SyriaKermorgant O Chaumette F Combining IBVS and PBVS to ensure the visibility constraint 2011 San Francisco, CA, USACorke, P. I., & Hutchinson, S. A. (2001). A new partitioned approach to image-based visual servo control. IEEE Transactions on Robotics and Automation, 17(4), 507-515. doi:10.1109/70.954764Yang, Z., & Shen, S. (2017). Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration. IEEE Transactions on Automation Science and Engineering, 14(1), 39-51. doi:10.1109/tase.2016.2550621Chesi G Hashimoto K Static-eye against hand-eye visual servoing 2002 Las Vegas, NV, USABourdis N Marraud D Sahbi H Camera pose estimation using visual servoing for aerial video change detection 2012 Munich, GermanyShademan A Janabi-Sharifi F Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing 2005 USAMalis, E., Mezouar, Y., & Rives, P. (2010). Robustness of Image-Based Visual Servoing With a Calibrated Camera in the Presence of Uncertainties in the Three-Dimensional Structure. IEEE Transactions on Robotics, 26(1), 112-120. doi:10.1109/tro.2009.2033332Chen J Behal A Dawson D Dixon W Adaptive visual servoing in the presence of intrinsic calibration uncertainty 2003 USAMezouar Y Malis E Robustness of central catadioptric image-based visual servoing to uncertainties on 3D parameters 2004 Sendai, JapanMarchand, E., Spindler, F., & Chaumette, F. (2005). ViSP for visual servoing: a generic software platform with a wide class of robot control skills. IEEE Robotics & Automation Magazine, 12(4), 40-52. doi:10.1109/mra.2005.157702

A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems

This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding Mode Controller for the control of dynamic uncertain systems. The proposed controller combines the advantages of Second order Sliding Mode Control, Fuzzy Logic Control and Adaptive Control. The reaching conditions, stability and robustness of the system with the proposed controller are guaranteed. In addition, the proposed controller is well suited for simple design and implementation. The effectiveness of the proposed controller over the first order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based simulations performed on a DC-DC Buck converter. Based on this comparison, the proposed controller is shown to obtain the desired transient response without causing chattering and error under steady-state conditions. The proposed controller is able to give robust performance in terms of rejection to input voltage variations and load variations.Comment: 14 page

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Analysis, filtering, and control for Takagi-Sugeno fuzzy models in networked systems

Copyright © 2015 Sunjie Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 11301118 and 61174136, the Natural Science Foundation of Jiangsu Province of China under Grant BK20130017, the Fundamental Research Funds for the Central Universities of China under Grant CUSF-DH-D-2013061, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Robust sliding mode control for discrete stochastic systems with mixed time delays, randomly occurring uncertainties, and randomly occurring nonlinearities

This is the post-print version of the paper. The official published version can be accessed from the link below - Copyright @ 2012 IEEEThis paper investigates the robust sliding mode control (SMC) problem for a class of uncertain nonlinear stochastic systems with mixed time delays. Both the sectorlike nonlinearities and the norm-bounded uncertainties enter into the system in random ways, and such randomly occurring uncertainties and randomly occurring nonlinearities obey certain mutually uncorrelated Bernoulli distributed white noise sequences. The mixed time delays consist of both the discrete and the distributed delays. The time-varying delays are allowed in state. By employing the idea of delay fractioning and constructing a new Lyapunov–Krasovskii functional, sufficient conditions are established to ensure the stability of the system dynamics in the specified sliding surface by solving a certain semidefinite programming problem. A full-state feedback SMC law is designed to guarantee the reaching condition. A simulation example is given to demonstrate the effectiveness of the proposed SMC scheme.This work was supported in part by the National Natural Science Foundation of China under Grants 61028008, 60825303 and 60834003, National 973 Project under Grant 2009CB320600, the Fok Ying Tung Education Fund under Grant 111064, the Special Fund for the Author of National Excellent Doctoral Dissertation of China under Grant 2007B4, the Key Laboratory of Integrated Automation for the Process Industry Northeastern University) from the Ministry of Education of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany