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@article{ISI:001298976600001,
title = {Conductive hydrogels-based self-sensing soft robot state perception and trajectory tracking},
author = {Jie Ma and Zhiji Han and Mingge Li and Zhijie Liu and Wei He and Shuzhi Sam Ge},
doi = {10.1002/rob.22420},
times_cited = {0},
issn = {1556-4959},
year = {2024},
date = {2024-08-28},
journal = {JOURNAL OF FIELD ROBOTICS},
publisher = {WILEY},
address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
abstract = {Soft robots face significant challenges in proprioceptive sensing and precise control due to their highly deformable and compliant nature. This paper addresses these challenges by developing a conductive hydrogel sensor and integrating it into a soft robot for bending angle measurement and motion control. A quantitative mapping between the hydrogel resistance and the robot's bending gesture is formulated. Furthermore, a nonlinear differentiator is proposed to estimate the angular velocity for closed-loop control, eliminating the reliance on conventional sensors. Meanwhile, a controller is designed to track both structural and nonstructural trajectories. The proposed approach integrates advanced soft sensing materials and intelligent control algorithms, significantly improving the proprioception and motion accuracy of soft robots. This work bridges the gap between novel material design and practical control applications, opening up new possibilities for soft robots to perform delicate tasks in various fields. The experimental results demonstrate the effectiveness of the proposed sensing and control approach in achieving precise and robust motion control of the soft robot.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ISI:001246582400007,
title = {Controllable Trade-Off Between Performance and Constrained Input for Vibration Suppression in Flexible Space Structures},
author = {Haining Sun and Xiaoqiang Tang and Shuzhi Sam Ge},
doi = {10.1109/TAES.2024.3361434},
times_cited = {0},
issn = {0018-9251},
year = {2024},
date = {2024-06-01},
journal = {IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS},
volume = {60},
number = {3},
pages = {3390-3402},
publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
abstract = {Achieving vibration suppression of flexible space structures requires not only considering the vibration settling time, but also avoiding excessive control forces that might damage the structure. In this article, an active control scheme is designed to suppress undesired vibrations in flexible structures by exerting small cable forces. It employs a cable-driven parallel robot (CDPR) as an actuator. The prominent feature of the controller lies in its ability to achieve a controllable trade-off between the effectiveness of vibration suppression and control inputs. Moreover, the controller can effectively suppress vibrations even when cable forces are constrained. The underlying principle is that the vibration energy is consumed through the negative work done by the cable forces until the tip displacement is ultimately reduced to a small range. The stability of the controller is verified by the Lyapunov method. Simulations demonstrate the effectiveness of the proposed control scheme, while experimental validation on a prototype consisting of a six-meter-long flexible structure and a four-cable CDPR further supports these findings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ISI:001215635300003,
title = {On non-von Neumann flexible neuromorphic vision sensors},
author = {Hao Wang and Bin Sun and Shuzhi Sam Ge and Jie Su and Ming Liang Jin},
doi = {10.1038/s41528-024-00313-3},
times_cited = {2},
year = {2024},
date = {2024-05-07},
journal = {NPJ FLEXIBLE ELECTRONICS},
volume = {8},
number = {1},
publisher = {NATURE PORTFOLIO},
address = {HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY},
abstract = {The structure and mechanism of the human visual system contain rich treasures, and surprising effects can be achieved by simulating the human visual system. In this article, starting from the human visual system, we compare and discuss the discrepancies between the human visual system and traditional machine vision systems. Given the wide variety and large volume of visual information, the use of non-von Neumann structured, flexible neuromorphic vision sensors can effectively compensate for the limitations of traditional machine vision systems based on the von Neumann architecture. Firstly, this article addresses the emulation of retinal functionality and provides an overview of the principles and circuit implementation methods of non-von Neumann computing architectures. Secondly, in terms of mimicking the retinal surface structure, this article introduces the fabrication approach for flexible sensor arrays. Finally, this article analyzes the challenges currently faced by non-von Neumann flexible neuromorphic vision sensors and offers a perspective on their future development.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ISI:001203365100010,
title = {Adaptive Safe Reinforcement Learning With Full-State Constraints and Constrained Adaptation for Autonomous Vehicles},
author = {Yuxiang Zhang and Xiaoling Liang and Dongyu Li and Shuzhi Sam Ge and Bingzhao Gao and Hong Chen and Tong Heng Lee},
doi = {10.1109/TCYB.2023.3283771},
times_cited = {12},
issn = {2168-2267},
year = {2024},
date = {2024-03-01},
journal = {IEEE TRANSACTIONS ON CYBERNETICS},
volume = {54},
number = {3},
pages = {1907-1920},
publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
abstract = {High-performance learning-based control for the typical safety-critical autonomous vehicles invariably requires that the full-state variables are constrained within the safety region even during the learning process. To solve this technically critical and challenging problem, this work proposes an adaptive safe reinforcement learning (RL) algorithm that invokes innovative safety-related RL methods with the consideration of constraining the full-state variables within the safety region with adaptation. These are developed toward assuring the attainment of the specified requirements on the full-state variables with two notable aspects. First, thus, an appropriately optimized backstepping technique and the asymmetric barrier Lyapunov function (BLF) methodology are used to establish the safe learning framework to ensure system full-state constraints requirements. More specifically, each subsystem's control and partial derivative of the value function are decomposed with asymmetric BLF-related items and an independent learning part. Then, the independent learning part is updated to solve the Hamilton-Jacobi-Bellman equation through an adaptive learning implementation to attain the desired performance in system control. Second, with further Lyapunov-based analysis, it is demonstrated that safety performance is effectively doubly assured via a methodology of a constrained adaptation algorithm during optimization (which incorporates the projection operator and can deal with the conflict between safety and optimization). Therefore, this algorithm optimizes system control and ensures that the full set of state variables involved is always constrained within the safety region during the whole learning process. Comparison simulations and ablation studies are carried out on motion control problems for autonomous vehicles, which have verified superior performance with smaller variance and better convergence performance under uncertain circumstances. The effectiveness of the safe performance of overall system control with the proposed method accordingly has been verified.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ISI:001136745800012,
title = {Event-Triggered Tunnel Prescribed Control for Nonlinear Systems},
author = {Ruihang Ji and Shuzhi Sam Ge},
doi = {10.1109/TFUZZ.2023.3290934},
times_cited = {9},
issn = {1063-6706},
year = {2024},
date = {2024-01-01},
journal = {IEEE TRANSACTIONS ON FUZZY SYSTEMS},
volume = {32},
number = {1},
pages = {90-101},
publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
abstract = {This article studies an event-triggered tunnel prescribed control (TPC) for uncertain nonlinear systems under any initial condition. A more general entry capture problem (ECP) is introduced, where the tunnel prescribed performance is satisfied after a certain period of system operation, as opposed to starting from the beginning, thereby, making the control design complex yet challenging. In this case, the normally employed prescribed performance control becomes invalid due to the singularity problem arising from the initial condition violation. An error self-tuning function is proposed to provide a unified approach for handling different initial conditions, which can be extended to other methods. In order to deal with unknown control directions, an orientation function is employed in lieu of Nussbaum-type function, by which the initial control input is always equal to zero avoiding a large initial value. We then develop an event-triggered mechanism from an encoding-decoding viewpoint, by which only 1-bit string, either 1 or 0, is required for each communication between control and actuator. In this way, such event-triggered mechanism can further reduce communication burden and improve communication security at the same time. The developed event-triggered TPC provides an effective solution for the underlying ECP and exhibits low-complexity level since no additional filters or time derivatives of virtual control inputs are required in the control design. Finally, comparative simulations are conducted to illustrate the abovementioned theoretical finding.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}