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Zilian Yi

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2 papers

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IROS Conference 2023 Conference Paper

A Mangasarian-Soldov Function Based Neural Network for Constrained Control of Parallel and Serial Robots

Weibing Li
Yanying Zou
Zilian Yi
Haimei Wu
Yongping Pan 0001

Zeroing neural networks (ZNNs) are powerful alternatives to solving quadratic programming (QP) for constrained control of parallel and serial robots. A recent study showed that a ZNN solver designed based on a perturbed Fischer-Burmeister function (pFB-ZNN) achieves more satisfactory performance than other ZNN solvers. The pFB-ZNN solver suffers from manual tuning of an extra hyper-parameter and may encounter residual error peaks. To tackle the above issues, this paper proposes a new Mangasarian-Solodov function-based ZNN (MS-ZNN) solver. The MS-ZNN solver has no extra hyper-parameter to be tuned and it can eliminate residual error peaks appeared in the pFB-ZNN solver, ensuring a higher solution accuracy. Mathematically, this paper details the design and convergence analysis of the MS-ZNN solver, demonstrating its convergence in the sense of Lyapunov. Numerical studies are comparatively performed, verifying the effectiveness and superiority of the MS-ZNN solver. The MS-ZNN solver is then successfully applied to kinematic control of a parallel robot and a serial robot under joint constraints. Both simulative and experimental results demonstrate that the proposed MS-ZNN solver is applicable to constrained control of parallel and serial robots with joint-limit avoidance achieved.

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IROS Conference 2023 Conference Paper

A Novel Obstacle-Avoidance Solution With Non-Iterative Neural Controller for Joint-Constrained Redundant Manipulators

Weibing Li
Zilian Yi
Yanying Zou
Haimei Wu
Yang Yang
Yongping Pan 0001

Obstacle avoidance (OA) and joint-limit avoidance (JLA) are essential for redundant manipulators to ensure safe and reliable robotic operations. One solution to OA and JLA is to incorporate the involved constraints into a quadratic programming (QP), by solving which OA and JLA can be achieved. There exist a few non-iterative solvers such as zeroing neural networks (ZNNs), which can solve each sampled QP problem using only one iteration, yet no solution is suitable for OA and JLA due to the absence of some derivative information. To tackle these issues, this paper proposes a novel solution with a non-iterative neural controller termed NCP-ZNN for joint-constrained redundant manipulators. Unlike iterative methods, the neural controller involving derivative information proposed in this paper possesses some positive features including non-iterative computing and convergence with time. In this paper, the reestablished OA-JLA scheme is first introduced. Then, the design details of the neural controller are presented. After that, some comparative simulations based on a PA10 robot and an experiment based on a Franka Emika Panda robot are conducted, demonstrating that the proposed neural controller is more competent in OA and JLA.

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