Non-Linear Robust Observers For Systems With Non-Collocated Sensors And Actuators

Challenges in controlling highly nonlinear systems are not limited to the development of sophisticated control algorithms that are tolerant to significant modeling imprecision and external disturbances. Additional challenges stem from the implementation of the control algorithm such as the availability of the state variables needed for the computation of the control signals, and the adverse effects induced by non-collocated sensors and actuators. The present work investigates the adverse effects of non-collocated sensors and actuators on the phase characteristics of flexible structures and the ensuing implications on the performance of structural controllers. Two closed-loop systems are considered and their phase angle contours have been generated as functions of the normalized sensor location and the excitation frequency. These contours were instrumental in the development of remedial actions for rendering structural controllers immune to the detrimental effects of non-collocated sensors and actuators. Moreover, the current work has focused on providing experimental validation for the robust performances of a self-tuning observer and a sliding mode observer. The observers are designed based on the variable structure systems theory and the self-tuning fuzzy logic scheme. Their robustness and self-tuning characteristics allow one to use an imprecise model of the system and eliminate the need for the extensive tuning associated with a fixed rule-based expert fuzzy inference system. The first phase of the experimental work was conducted in a controlled environment on a flexible spherical robotic manipulator whose natural frequencies are configuration-dependent. Both controllers have yielded accurate estimates of the required state variables in spite of significant modeling imprecision. The observers were also tested under a completely uncontrolled environment, which involves a 16-ft boat operating in open-water under different sea states. Such an experimental work necessitates the development of a supervisory control algorithm to perform PTP tasks, prescribed throttle arm and steering tasks, surge speed and heading tracking tasks, or recovery maneuvers. This system has been implemented herein to perform prescribed throttle arm and steering control tasks based on estimated rather than measured state variables. These experiments served to validate the observers in a completely uncontrolled environment and proved their viability as reliable techniques for providing accurate estimates for the required state variables

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

An MPC-based Optimal Motion Control Framework for Pendulum-driven Spherical Robots

Motion control is essential for all autonomous mobile robots, and even more so for spherical robots. Due to the uniqueness of the spherical robot, its motion control must not only ensure accurate tracking of the target commands, but also minimize fluctuations in the robot's attitude and motors' current while tracking. In this paper, model predictive control (MPC) is applied to the control of spherical robots and an MPC-based motion control framework is designed. There are two controllers in the framework, an optimal velocity controller ESO-MPC which combines extend states observers (ESO) and MPC, and an optimal orientation controller that uses multilayer perceptron (MLP) to generate accurate trajectories and MPC with changing weights to achieve optimal control. Finally, the performance of individual controllers and the whole control framework are verified by physical experiments. The experimental results show that the MPC-based motion control framework proposed in this work is much better than PID in terms of rapidity and accuracy, and has great advantages over sliding mode controller (SMC) for overshoot, attitude stability, current stability and energy consumption.Comment: This paper has been submitted to Control Engineering Practic

Modeling and Robust Attitude Controller Design for a Small Size Helicopter

This paper addresses the design and application controller for a small-size unmanned aerial vehicle (UAV). In this work, the main objective is to study the modeling and attitude controller design for a small size helicopter. Based on a non-simplified helicopter model, a new robust attitude control law, which is combined with a nonlinear control method and a model-free method, is proposed in this paper. Both wind gust and ground effect phenomena conditions are involved in this experiment and the result on a real helicopter platform demonstrates the effectiveness of the proposed control algorithm and robustness of its resultant controller.Comment: 6 page

Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system

[EN] Rehabilitation is a hazardous task for a mechanical system, since the device has to interact with the human extremities without the hands-on experience the physiotherapist acquires over time. A gap needs to be filled in terms of designing effective controllers for this type of devices. In this respect, the paper describes the design of a model-based control for an electromechanical lower-limb rehabilitation system based on a parallel kinematic mechanism. A controller-observer was designed for estimating joint velocities, which are then used in a hybrid position/force control scheme. The model parameters are identified by customising an approach based on identifying only the relevant system dynamics parameters. Findings obtained through simulations show evidence of improvement in tracking performance compared with those where the velocity was estimated by numerical differentiation. The controller is also implemented in an actual electromechanical system for lower-limb rehabilitation tasks. Findings based on rehabilitation tasks confirm the findings from simulations.This work was partially financed by the Plan Nacional de I+D, Comision Interministerial de Ciencia y Tecnologia (FEDERCICYT) under the project DPI2013-44227-R and by the Instituto U. de Automatica e Informatica Industrial (ai2) of the Universitat Politecnica de Valencia.Valera Fernández, Á.; Díaz-Rodríguez, M.; Vallés Miquel, M.; Oliver, E.; Mata Amela, V.; Page Del Pozo, AF. (2017). Controller-observer design and dynamic parameter identification for model-based control of an electromechanical lower-limb rehabilitation system. International Journal of Control. 90(4):702-714. https://doi.org/10.1080/00207179.2016.1215529S702714904Åström, K. J., & Murray, R. M. (2010). Feedback Systems. doi:10.2307/j.ctvcm4gdkAtkeson, C. G., An, C. H., & Hollerbach, J. M. (1986). Estimation of Inertial Parameters of Manipulator Loads and Links. The International Journal of Robotics Research, 5(3), 101-119. doi:10.1177/027836498600500306Chia Bejarano, N., Maggioni, S., De Rijcke, L., Cifuentes, C. A., & Reinkensmeyer, D. J. (2015). Robot-Assisted Rehabilitation Therapy: Recovery Mechanisms and Their Implications for Machine Design. Emerging Therapies in Neurorehabilitation II, 197-223. doi:10.1007/978-3-319-24901-8_8Berghuis, H., & Nijmeijer, H. (1993). A passivity approach to controller-observer design for robots. IEEE Transactions on Robotics and Automation, 9(6), 740-754. doi:10.1109/70.265918Briot, S., & Gautier, M. (2013). Global identification of joint drive gains and dynamic parameters of parallel robots. Multibody System Dynamics, 33(1), 3-26. doi:10.1007/s11044-013-9403-6Canudas de Wit, C., & Fixot, N. (1991). Robot control via robust estimated state feedback. IEEE Transactions on Automatic Control, 36(12), 1497-1501. doi:10.1109/9.106170Canudas de Wit, C., & Slotine, J.-J. E. (1991). Sliding observers for robot manipulators. Automatica, 27(5), 859-864. doi:10.1016/0005-1098(91)90041-yCao, J., Xie, S. Q., Das, R., & Zhu, G. L. (2014). Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects. Medical Engineering & Physics, 36(12), 1555-1566. doi:10.1016/j.medengphy.2014.08.005Carretero, J. A., Podhorodeski, R. P., Nahon, M. A., & Gosselin, C. M. (1999). Kinematic Analysis and Optimization of a New Three Degree-of-Freedom Spatial Parallel Manipulator. Journal of Mechanical Design, 122(1), 17-24. doi:10.1115/1.533542Cazalilla, J., Vallés, M., Mata, V., Díaz-Rodríguez, M., & Valera, A. (2014). Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models. Robotics and Computer-Integrated Manufacturing, 30(5), 468-477. doi:10.1016/j.rcim.2014.02.003De Jalon, J.G. & Bayo, E. (1994). Kinematic and dynamic simulation of multibody systems: the real-time challenge. New York: Springer Verlag.Díaz, I., Gil, J. J., & Sánchez, E. (2011). Lower-Limb Robotic Rehabilitation: Literature Review and Challenges. Journal of Robotics, 2011, 1-11. doi:10.1155/2011/759764Díaz-Rodríguez, M., Iriarte, X., Mata, V., & Ros, J. (2009). On the Experiment Design for Direct Dynamic Parameter Identification of Parallel Robots. Advanced Robotics, 23(3), 329-348. doi:10.1163/156855308x397550Díaz-Rodríguez, M., Mata, V., Valera, Á., & Page, Á. (2010). A methodology for dynamic parameters identification of 3-DOF parallel robots in terms of relevant parameters. Mechanism and Machine Theory, 45(9), 1337-1356. doi:10.1016/j.mechmachtheory.2010.04.007Gautier, M. (1991). Numerical calculation of the base inertial parameters of robots. Journal of Robotic Systems, 8(4), 485-506. doi:10.1002/rob.4620080405Gautier, M., & Khalil, W. (s. f.). On the identification of the inertial parameters of robots. Proceedings of the 27th IEEE Conference on Decision and Control. doi:10.1109/cdc.1988.194738Jamwal, P. K., Hussain, S., & Xie, S. Q. (2013). Review on design and control aspects of ankle rehabilitation robots. Disability and Rehabilitation: Assistive Technology, 10(2), 93-101. doi:10.3109/17483107.2013.866986Janabi-Sharifi, F., Hayward, V., & Chen, C.-S. J. (2000). Discrete-time adaptive windowing for velocity estimation. IEEE Transactions on Control Systems Technology, 8(6), 1003-1009. doi:10.1109/87.880606Janot, A., Gautier, M., Jubien, A., & Vandanjon, P. O. (2014). Comparison Between the CLOE Method and the DIDIM Method for Robots Identification. IEEE Transactions on Control Systems Technology, 22(5), 1935-1941. doi:10.1109/tcst.2014.2299544Janot, A., Vandanjon, P.-O., & Gautier, M. (2016). A revised Durbin-Wu-Hausman test for industrial robot identification. Control Engineering Practice, 48, 52-62. doi:10.1016/j.conengprac.2015.12.017Jiménez-Fabián, R., & Verlinden, O. (2012). Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Medical Engineering & Physics, 34(4), 397-408. doi:10.1016/j.medengphy.2011.11.018Khalil, W., Vijayalingam, A., Khomutenko, B., Mukhanov, I., Lemoine, P., & Ecorchard, G. (2014). OpenSYMORO: An open-source software package for symbolic modelling of robots. 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. doi:10.1109/aim.2014.6878246Marchal-Crespo, L., & Reinkensmeyer, D. J. (2009). Review of control strategies for robotic movement training after neurologic injury. Journal of NeuroEngineering and Rehabilitation, 6(1). doi:10.1186/1743-0003-6-20Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., & Xie, S. (Shane). (2015). Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics, 31, 132-145. doi:10.1016/j.mechatronics.2015.04.005Page, A., Candelas, P., & Belmar, F. (2006). On the use of local fitting techniques for the analysis of physical dynamic systems. European Journal of Physics, 27(2), 273-279. doi:10.1088/0143-0807/27/2/010Raibert, M. H., & Craig, J. J. (1981). Hybrid Position/Force Control of Manipulators. Journal of Dynamic Systems, Measurement, and Control, 103(2), 126-133. doi:10.1115/1.3139652Ramsay, J. O., & Silverman, B. W. (2005). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/b98888Saglia, J. A., Tsagarakis, N. G., Dai, J. S., & Caldwell, D. G. (2013). Control Strategies for Patient-Assisted Training Using the Ankle Rehabilitation Robot (ARBOT). IEEE/ASME Transactions on Mechatronics, 18(6), 1799-1808. doi:10.1109/tmech.2012.2214228Vallés, M., Cazalilla, J., Valera, Á., Mata, V., Page, Á., & Díaz-Rodríguez, M. (2015). A 3-PRS parallel manipulator for ankle rehabilitation: towards a low-cost robotic rehabilitation. Robotica, 35(10), 1939-1957. doi:10.1017/s0263574715000120Vallés, M., Cazalilla, J. I., Valera, Á., Mata, V., & Page, Á. (2013). Implementación basada en el middleware OROCOS de controladores dinámicos pasivos para un robot paralelo. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(1), 96-103. doi:10.1016/j.riai.2012.11.009Vallés, M., Díaz-Rodríguez, M., Valera, Á., Mata, V., & Page, Á. (2012). Mechatronic Development and Dynamic Control of a 3-DOF Parallel Manipulator. Mechanics Based Design of Structures and Machines, 40(4), 434-452. doi:10.1080/15397734.2012.687292Wu, F. X., Zhang, W. J., Li, Q., & Ouyang, P. R. (2002). Integrated Design and PD Control of High-Speed Closed-loop Mechanisms. Journal of Dynamic Systems, Measurement, and Control, 124(4), 522-528. doi:10.1115/1.1513179Yang, C., Huang, Q., & Han, J. (2012). Decoupling control for spatial six-degree-of-freedom electro-hydraulic parallel robot. Robotics and Computer-Integrated Manufacturing, 28(1), 14-23. doi:10.1016/j.rcim.2011.06.002Yoon, J., Ryu, J., & Lim, K.-B. (2006). Reconfigurable ankle rehabilitation robot for various exercises. Journal of Robotic Systems, 22(S1), S15-S33. doi:10.1002/rob.2015

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches

Virtual Structure Based Formation Tracking of Multiple Wheeled Mobile Robots: An Optimization Perspective

Today, with the increasing development of science and technology, many systems need to be optimized to find the optimal solution of the system. this kind of problem is also called optimization problem. Especially in the formation problem of multi-wheeled mobile robots, the optimization algorithm can help us to find the optimal solution of the formation problem. In this paper, the formation problem of multi-wheeled mobile robots is studied from the point of view of optimization. In order to reduce the complexity of the formation problem, we first put the robots with the same requirements into a group. Then, by using the virtual structure method, the formation problem is reduced to a virtual WMR trajectory tracking problem with placeholders, which describes the expected position of each WMR formation. By using placeholders, you can get the desired track for each WMR. In addition, in order to avoid the collision between multiple WMR in the group, we add an attraction to the trajectory tracking method. Because MWMR in the same team have different attractions, collisions can be easily avoided. Through simulation analysis, it is proved that the optimization model is reasonable and correct. In the last part, the limitations of this model and corresponding suggestions are given