Performance Investigations of an Improved Backstepping Operational space Position Tracking Control of a Mobile Manipulator

This article implies an improved backstepping control technique for the operational-space position tracking of a kinematically redundant mobile manipulator. The mobile manipulator thought-out for the analysis has a vehicle base with four mecanum wheels and a serial manipulator arm with three rotary actuated joints. The recommended motion controller provides a safeguard against the system dynamic variations owing to the parameter uncertainties, unmodelled system dynamics and unknown exterior disturbances. The Lyapunov’s direct method assists in designing and authenticating the system’s closed-loop stability and tracking ability of the suggested control strategy. The feasibility, effectiveness and robustness of the recommended controller are demonstrated and investigated numerically with the help of computer based simulations. The mathematical model used for the computer-based simulations is derived based on a real-time mobile manipulator and the derived model is further verified with an inbuilt gazebo model in a robot operating system (ROS) environment. In addition, the proposed scheme is verified on an in-house fabricated mobile manipulator system. Further, the recommended controller performance is correlated with the conventional backstepping control design in both computer-based simulations and in real-time experiments

A control-theoretical fault prognostics and accommodation framework for a class of nonlinear discrete-time systems

Fault diagnostics and prognostics schemes (FDP) are necessary for complex industrial systems to prevent unscheduled downtime resulting from component failures. Existing schemes in continuous-time are useful for diagnosing complex industrial systems and no work has been done for prognostics. Therefore, in this dissertation, a systematic design methodology for model-based fault prognostics and accommodation is undertaken for a class of nonlinear discrete-time systems. This design methodology, which does not require any failure data, is introduced in six papers. In Paper I, a fault detection and prediction (FDP) scheme is developed for a class of nonlinear system with state faults by assuming that all the states are measurable. A novel estimator is utilized for detecting a fault. Upon detection, an online approximator in discrete-time (OLAD) and a robust adaptive term are activated online in the estimator wherein the OLAD learns the unknown fault dynamics while the robust adaptive term ensures asymptotic performance guarantee. A novel update law is proposed for tuning the OLAD parameters. Additionally, by using the parameter update law, time to reach an a priori selected failure threshold is derived for prognostics. Subsequently, the FDP scheme is used to estimate the states and detect faults in nonlinear input-output systems in Paper II and to nonlinear discrete-time systems with both state and sensor faults in Paper III. Upon detection, a novel fault isolation estimator is used to identify the faults in Paper IV. It was shown that certain faults can be accommodated via controller reconfiguration in Paper V. Finally, the performance of the FDP framework is demonstrated via Lyapunov stability analysis and experimentally on the Caterpillar hydraulics test-bed in Paper VI by using an artificial immune system as an OLAD --Abstract, page iv

Adaptive fuzzy tracking control for a class of uncertain MIMO nonlinear systems using disturbance observer

In this paper, the adaptive fuzzy tracking control is proposed for a class of multi-input and multioutput (MIMO) nonlinear systems in the presence of system uncertainties, unknown non-symmetric input saturation and external disturbances. Fuzzy logic systems (FLS) are used to approximate the system uncertainty of MIMO nonlinear systems. Then, the compound disturbance containing the approximation error and the time-varying external disturbance that cannot be directly measured are estimated via a disturbance observer. By appropriately choosing the gain matrix, the disturbance observer can approximate the compound disturbance well and the estimate error converges to a compact set. This control strategy is further extended to develop adaptive fuzzy tracking control for MIMO nonlinear systems by coping with practical issues in engineering applications, in particular unknown non-symmetric input saturation and control singularity. Within this setting, the disturbance observer technique is combined with the FLS approximation technique to compensate for the effects of unknown input saturation and control singularity. Lyapunov approach based analysis shows that semi-global uniform boundedness of the closed-loop signals is guaranteed under the proposed tracking control techniques. Numerical simulation results are presented to illustrate the effectiveness of the proposed tracking control schemes

Modelling an Industrial Robot and Its Impact on Productivity

[EN] This research aims to design an efficient algorithm leading to an improvement of productivity by posing a multi-objective optimization, in which both the time consumed to carry out scheduled tasks and the associated costs of the autonomous industrial system are minimized. The algorithm proposed models the kinematics and dynamics of the industrial robot, provides collision-free trajectories, allows to constrain the energy consumed and meets the physical characteristics of the robot (i.e., restriction on torque, jerks and power in all driving motors). Additionally, the trajectory tracking accuracy is improved using an adaptive fuzzy sliding mode control (AFSMC), which allows compensating for parametric uncertainties, bounded external disturbances and constraint uncertainties. Therefore, the system stability and robustness are enhanced; thus, overcoming some of the limitations of the traditional proportional-integral-derivative (PID) controllers. The trade-offs among the economic issues related to the assembly line and the optimal time trajectory of the desired motion are analyzed using Pareto fronts. The technique is tested in different examples for a six-degrees-of-freedom (DOF) robot system. Results have proved how the use of this methodology enhances the performance and reliability of assembly lines.Llopis-Albert, C.; Rubio Montoya, FJ.; Valero Chuliá, FJ. (2021). Modelling an Industrial Robot and Its Impact on Productivity. Mathematics. 9(7):1-13. https://doi.org/10.3390/math907076911397AOYAMA, T., NISHI, T., & ZHANG, G. (2017). Production planning problem with market impact under demand uncertainty. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 11(2), JAMDSM0019-JAMDSM0019. doi:10.1299/jamdsm.2017jamdsm0019Llopis-Albert, C., Rubio, F., & Valero, F. (2015). Improving productivity using a multi-objective optimization of robotic trajectory planning. Journal of Business Research, 68(7), 1429-1431. doi:10.1016/j.jbusres.2015.01.027Rubio, F., Valero, F., Sunyer, J., & Cuadrado, J. (2012). Optimal time trajectories for industrial robots with torque, power, jerk and energy consumed constraints. Industrial Robot: An International Journal, 39(1), 92-100. doi:10.1108/01439911211192538Llopis-Albert, C., Rubio, F., & Valero, F. (2018). Optimization approaches for robot trajectory planning. Multidisciplinary Journal for Education, Social and Technological Sciences, 5(1), 1. doi:10.4995/muse.2018.9867Yang, Y., Pan, J., & Wan, W. (2019). Survey of optimal motion planning. IET Cyber-Systems and Robotics, 1(1), 13-19. doi:10.1049/iet-csr.2018.0003Gasparetto, A., & Zanotto, V. (2008). A technique for time-jerk optimal planning of robot trajectories. Robotics and Computer-Integrated Manufacturing, 24(3), 415-426. doi:10.1016/j.rcim.2007.04.001Mohammed, A., Schmidt, B., Wang, L., & Gao, L. (2014). Minimizing Energy Consumption for Robot Arm Movement. Procedia CIRP, 25, 400-405. doi:10.1016/j.procir.2014.10.055Van den Berg, J., Abbeel, P., & Goldberg, K. (2011). LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information. The International Journal of Robotics Research, 30(7), 895-913. doi:10.1177/0278364911406562Liu, S., Sun, D., & Zhu, C. (2011). Coordinated Motion Planning for Multiple Mobile Robots Along Designed Paths With Formation Requirement. IEEE/ASME Transactions on Mechatronics, 16(6), 1021-1031. doi:10.1109/tmech.2010.2070843Plaku, E., Kavraki, L. E., & Vardi, M. Y. (2010). Motion Planning With Dynamics by a Synergistic Combination of Layers of Planning. IEEE Transactions on Robotics, 26(3), 469-482. doi:10.1109/tro.2010.2047820Rubio, F., Llopis-Albert, C., Valero, F., & Suñer, J. L. (2015). Assembly Line Productivity Assessment by Comparing Optimization-Simulation Algorithms of Trajectory Planning for Industrial Robots. Mathematical Problems in Engineering, 2015, 1-10. doi:10.1155/2015/931048Rubio, F., Llopis-Albert, C., Valero, F., & Suñer, J. L. (2016). Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robotics and Autonomous Systems, 86, 106-112. doi:10.1016/j.robot.2016.09.008Llopis-Albert, C., Valero, F., Mata, V., Pulloquinga, J. L., Zamora-Ortiz, P., & Escarabajal, R. J. (2020). Optimal Reconfiguration of a Parallel Robot for Forward Singularities Avoidance in Rehabilitation Therapies. A Comparison via Different Optimization Methods. Sustainability, 12(14), 5803. doi:10.3390/su12145803Llopis-Albert, C., Valero, F., Mata, V., Escarabajal, R. J., Zamora-Ortiz, P., & Pulloquinga, J. L. (2020). Optimal Reconfiguration of a Limited Parallel Robot for Forward Singularities Avoidance. Multidisciplinary Journal for Education, Social and Technological Sciences, 7(1), 113. doi:10.4995/muse.2020.13352Yang, J., Su, H., Li, Z., Ao, D., & Song, R. (2016). Adaptive control with a fuzzy tuner for cable-based rehabilitation robot. International Journal of Control, Automation and Systems, 14(3), 865-875. doi:10.1007/s12555-015-0049-4Zhang, G., & Zhang, X. (2016). Concise adaptive fuzzy control of nonlinearly parameterized and periodically time-varying systems via small gain theory. International Journal of Control, Automation and Systems, 14(4), 893-905. doi:10.1007/s12555-015-0054-7SUTONO, S. B., ABDUL-RASHID, S. H., AOYAMA, H., & TAHA, Z. (2016). Fuzzy-based Taguchi method for multi-response optimization of product form design in Kansei engineering: a case study on car form design. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 10(9), JAMDSM0108-JAMDSM0108. doi:10.1299/jamdsm.2016jamdsm0108DUBEY, A. K. (2009). Performance Optimization Control of ECH using Fuzzy Inference Application. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 3(1), 22-34. doi:10.1299/jamdsm.3.22Zhang, H., Fang, H., Zhang, D., Luo, X., & Zou, Q. (2020). Adaptive Fuzzy Sliding Mode Control for a 3-DOF Parallel Manipulator with Parameters Uncertainties. Complexity, 2020, 1-16. doi:10.1155/2020/2565316Markazi, A. H. D., Maadani, M., Zabihifar, S. H., & Doost-Mohammadi, N. (2018). Adaptive Fuzzy Sliding Mode Control of Under-actuated Nonlinear Systems. International Journal of Automation and Computing, 15(3), 364-376. doi:10.1007/s11633-017-1108-5Truong, H. V. A., Tran, D. T., To, X. D., Ahn, K. K., & Jin, M. (2019). Adaptive Fuzzy Backstepping Sliding Mode Control for a 3-DOF Hydraulic Manipulator with Nonlinear Disturbance Observer for Large Payload Variation. Applied Sciences, 9(16), 3290. doi:10.3390/app9163290Li, T.-H. S., & Huang, Y.-C. (2010). MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators. Information Sciences, 180(23), 4641-4660. doi:10.1016/j.ins.2010.08.00

Modeling and adaptive tracking for stochastic nonholonomic constrained mechanical systems

This paper is devoted to the problem of modeling and trajectory tracking for stochastic nonholonomic dynamic systems in the presence of unknown parameters. Prior to tracking controller design, the rigorous derivation of stochastic nonholonomic dynamic model is given. By reasonably introducing so-called internal state vector, a reduced dynamic model, which is suitable for control design, is proposed. Based on the backstepping technique in vector form, an adaptive tracking controller is then derived, guaranteeing that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The efficiency of the controller is demonstrated by a mechanics system: a vertical mobile wheel in random vibration environment

Learning Algorithm Design for Human-Robot Skill Transfer

In this research, we develop an intelligent learning scheme for performing human-robot skills transfer. Techniques adopted in the scheme include the Dynamic Movement Prim- itive (DMP) method with Dynamic Time Warping (DTW), Gaussian Mixture Model (G- MM) with Gaussian Mixture Regression (GMR) and the Radical Basis Function Neural Networks (RBFNNs). A series of experiments are conducted on a Baxter robot, a NAO robot and a KUKA iiwa robot to verify the effectiveness of the proposed design.During the design of the intelligent learning scheme, an online tracking system is de- veloped to control the arm and head movement of the NAO robot using a Kinect sensor. The NAO robot is a humanoid robot with 5 degrees of freedom (DOF) for each arm. The joint motions of the operator’s head and arm are captured by a Kinect V2 sensor, and this information is then transferred into the workspace via the forward and inverse kinematics. In addition, to improve the tracking performance, a Kalman filter is further employed to fuse motion signals from the operator sensed by the Kinect V2 sensor and a pair of MYO armbands, so as to teleoperate the Baxter robot. In this regard, a new strategy is developed using the vector approach to accomplish a specific motion capture task. For instance, the arm motion of the operator is captured by a Kinect sensor and programmed through a processing software. Two MYO armbands with embedded inertial measurement units are worn by the operator to aid the robots in detecting and replicating the operator’s arm movements. For this purpose, the armbands help to recognize and calculate the precise velocity of motion of the operator’s arm. Additionally, a neural network based adaptive controller is designed and implemented on the Baxter robot to illustrate the validation forthe teleoperation of the Baxter robot.Subsequently, an enhanced teaching interface has been developed for the robot using DMP and GMR. Motion signals are collected from a human demonstrator via the Kinect v2 sensor, and the data is sent to a remote PC for teleoperating the Baxter robot. At this stage, the DMP is utilized to model and generalize the movements. In order to learn from multiple demonstrations, DTW is used for the preprocessing of the data recorded on the robot platform, and GMM is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. Next, we apply the GMR algorithm to generate a synthesized trajectory to minimize position errors in the three dimensional (3D) space. This approach has been tested by performing tasks on a KUKA iiwa and a Baxter robot, respectively.Finally, an optimized DMP is added to the teaching interface. A character recombination technology based on DMP segmentation that uses verbal command has also been developed and incorporated in a Baxter robot platform. To imitate the recorded motion signals produced by the demonstrator, the operator trains the Baxter robot by physically guiding it to complete the given task. This is repeated five times, and the generated training data set is utilized via the playback system. Subsequently, the DTW is employed to preprocess the experimental data. For modelling and overall movement control, DMP is chosen. The GMM is used to generate multiple patterns after implementing the teaching process. Next, we employ the GMR algorithm to reduce position errors in the 3D space after a synthesized trajectory has been generated. The Baxter robot, remotely controlled by the user datagram protocol (UDP) in a PC, records and reproduces every trajectory. Additionally, Dragon Natural Speaking software is adopted to transcribe the voice data. This proposed approach has been verified by enabling the Baxter robot to perform a writing task of drawing robot has been taught to write only one character