Time Delay Compensation of a Robotic Arm based on Multiple Sensors for Indirect Teaching

In this paper, a remote-control system for a six-degree-of-freedom robotic arm that uses an indirect teaching method is proposed. In the indirect teaching method, an essential time delay occurs, which degrades the system performance. To overcome this time delay, which can be modeled using a Smith predictor, a model neural network (MNN) has been adopted. The Smith predictor is a model-based algorithm that is uncertain and prone to interference. In this study, the MNN has been utilized in an effective manner to model the system to support the Smith predictor algorithm. Using this time delay compensation, the outer loop proportional, integral, and derivative (PID) control gains are adjusted in an optimal manner through a PID neural network (PIDNN) to ensure that the robotic arm follows human commands precisely. By using the PIDNN proposed in this paper, the time required for indirect teaching application of the robot arm can be reduced.

[1]  Jian Weng,et al.  Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems , 2019, Swarm Evol. Comput..

[2]  Moonhong Baeg,et al.  Dual-arm robot box taping with kinesthetic teaching , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[3]  Teresa Riesgo,et al.  A serial port based debugging tool to improve learning with arduino , 2015, 2015 Conference on Design of Circuits and Integrated Systems (DCIS).

[4]  Yashwant G. Adhav,et al.  Wireless robotic hand for remote operations using flex sensor , 2016, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT).

[5]  Ravi Kumar Mandava,et al.  Design of PID controllers for 4-DOF planar and spatial manipulators , 2015, 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE).

[6]  Miroslav Krstic,et al.  Nonlinear and adaptive control de-sign , 1995 .

[7]  Siti Fauziah Toha,et al.  PID-Based Control of a Single-Link Flexible Manipulator in Vertical Motion with Genetic Optimisation , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[8]  Jin-Hyun Park,et al.  Collision Detection Algorithm for 6-DOF Manipulator Through Current Analysis , 2019 .

[9]  Józef Duda,et al.  Lyapunov matrices approach to the parametric optimization of a time delay system with a PD controller , 2016, 2016 17th International Carpathian Control Conference (ICCC).

[10]  Bidyadhar Subudhi,et al.  A differential evolution based neural network approach to nonlinear system identification , 2011, Appl. Soft Comput..

[11]  Kyoung Kwan Ahn,et al.  Design of An Advanced Time Delay Measurement and A Smart Adaptive Unequal Interval Grey Predictor for Real-Time Nonlinear Control Systems , 2013, IEEE Transactions on Industrial Electronics.

[12]  Han-Xiong Li,et al.  Smith predictor-based multiple periodic disturbance compensation for long dead-time processes , 2018, Int. J. Control.

[13]  Chong Liu,et al.  Voice control dual arm robot based on ROS system , 2018, 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR).

[14]  J. Duda.  Parametric Optimization of a Second Order Time Delay System with a PD-Controller , 2018, 2019 20th International Carpathian Control Conference (ICCC).

[15]  Arshad Javed,et al.  ROS based service robot platform , 2018, 2018 4th International Conference on Control, Automation and Robotics (ICCAR).

[16]  Anthony J. Calise,et al.  A New Neuroadaptive Control Architecture for Nonlinear Uncertain Dynamical Systems: Beyond $\sigma $- and $e$-Modifications , 2009, IEEE Transactions on Neural Networks.

[17]  Daoud Berkani,et al.  Design of nonlinear PID-smith predictor controllers with large time delays , 2015, 2015 Third World Conference on Complex Systems (WCCS).

[18]  Józef Duda.  Lyapunov matrices approach to the parametric optimization of a time delay system with a PI controller , 2016, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR).

[19]  Song Hua,et al.  Design of electric loading system in flight simulator based on PIDNN , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[20]  Weidong Zhang,et al.  Modified Smith Predictor for Controlling Integrator/Time Delay Processes , 1996 .

[21]  Junwei Wang,et al.  Novel humanoid dual-arm grinding robot , 2016, 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA).

[22]  Derong Liu,et al.  Neural-Network-Based Optimal Control for a Class of Unknown Discrete-Time Nonlinear Systems Using Globalized Dual Heuristic Programming , 2012, IEEE Transactions on Automation Science and Engineering.

[23]  Rachid Mansouri,et al.  Smith Predictor Based Fractional‐Order‐Filter PID Controllers Design for Long Time Delay Systems , 2017 .

[24]  Vijay Kumar,et al.  Comparison between conventional PID and Fuzzy PID supervisor for 3-DOF Scara type robot manipulator , 2014, 2014 IEEE Students' Conference on Electrical, Electronics and Computer Science.