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Thesis defences

PhD Oral Exam - Ningyu Zhu, Mechanical Engineering

Trajectory Planning and Control of Cooperative Robotic System for Automated Fiber Placement

Date & time
Tuesday, June 25, 2024
10 a.m. – 1 p.m.

This event is free


School of Graduate Studies


Nadeem Butt

Wheel chair accessible


When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.


Cooperative robotic system for automated fiber placement (AFP) is a promising solution to fulfill the requirement of manufacturing fiber composites on intricate structures. This project works on the trajectory planning and control of a 13-degree-of-freedom (13-DOF) cooperative AFP system, which is a vital topic since it has significant influence on the quality of final composite components. In the AFP system, a 1-DOF rotary stage is attached to the end-effector platform of a 6-Revolute-Spherical-Spherical (6-RSS) parallel robot to hold a Y-shape mandrel, while an AFP head is attached to the end-effector of a 6-DOF serial robot to place fiber on the mandrel with the desired degree. A photogrammetry sensor C-Track 780 can measure the end-effector pose of the robots in real time.

To ensure the desired cooperation performance and limit the communication cost, a distributed control structure with an event-triggered network is developed for the cooperative robotic system, based on the measured end-effector pose of the serial robot using C-Track 780. To address uncertain noises, an adaptive Kalman filter (AKF) is employed to enhance the pose estimation accuracy. A leader-follower trajectory planning strategy is proposed with the serial robot as the leader and the parallel robot as the follower. Since the kinematic and dynamic constraints of the serial robot may interrupt the fiber layup process, a time-jerk optimal trajectory planning scheme is designed for the serial robot considering the motion constraints. Consequently, the AFP head path could be deviated from the desired fiber path, and the AFP head roller direction may not keep perpendicular to the mandrel surface. To compensate the serial robot motion and satisfy the AFP geometric constraints, a vision-based trajectory generation approach is developed for the parallel robot.

Moreover, a position-based visual servoing (PBVS) strategy using adaptive sliding mode control is proposed for the parallel robot in Cartesian space. The visual measurements for the pose of the robot end-effector can avoid the calculation of robot forward kinematics and provide more flexibility for controller design. To enable the parallel robot to effectively track different trajectories under time-varying conditions, the online updates of the control gains in the controller are necessary. An adaptive sliding mode control method based on radial basis function (RBF) neural network is developed to guarantee system robustness and realize the self-tuning of the control gains, which makes the controller adaptive to the variations of system parameters. Based on Lyapunov theorem, the stability analysis of the controller has been done.

In the presence of dynamic uncertainties and external disturbances, a distributed control approach based on adaptive sliding mode controller (ASMC) is developed. A joint-space controller for the serial robot and a task-space controller for the parallel robot are designed, respectively. The ASMC employs a deep recurrent neural network (DRNN) to estimate the lumped system uncertainties. The DRNN incorporates a feedforward structure through three hidden layers and a feedback loop connecting the output layer to the input layer. This architecture demonstrates superior learning capability and dynamic adaptability compared to shallow feedforward neural networks. To ensure the stability of the controller, the adaptation laws of the neural network parameters are formulated through Lyapunov theorem.

The effectiveness and superiority of the trajectory planning and tracking control algorithms have been validated by simulation and experiment, and comparisons are made with the previous published research work.

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