作者：
Wei, QinglaiLi, HongyangLi, TaoWang, Fei-YueChinese Acad Sci
Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci
Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci
Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci
Inst Automation State Key Lab Management & Control Complex Syst Beijing Peoples R China
This article presents a novel data-based fault-tolerant control method for multicontroller linear systems via distributed policy iteration. The traditional fault-tolerant control methods based on policy iteration may ...
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This article presents a novel data-based fault-tolerant control method for multicontroller linear systems via distributed policy iteration. The traditional fault-tolerant control methods based on policy iteration may cause a huge-computational burden under the situation of high-dimension control laws. In order to solve this problem, a novel distributed policy iteration method is presented, where only one iterative control law is updated in each iteration, to realize the fault-tolerant control of multicontroller linear systems. The main contributions can be highlighted as follows: 1) a novel data-based distributed policy iteration method is presented to reduce the computational burden;2) the fault-tolerant control method is presented via designing fault compensators;and 3) the developed data-based method only requires the partial system information. First, the model-based fault-tolerant control via distributed policy iteration and fault compensation is provided. Based on the model-based method, a data-based fault-tolerant control method is presented. Finally, numerical experiments are given to show the performance of the presented method.
In artificial intelligence(AI)based-complex power systemmanagement and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,near...
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In artificial intelligence(AI)based-complex power systemmanagement and control technology,one of the urgent tasks is to evaluate AI intelligence and invent a way of autonomous intelligence ***,there is,currently,nearly no standard technical framework for objective and quantitative intelligence *** this article,based on a parallel system framework,a method is established to objectively and quantitatively assess the intelligence level of an AI agent for active power corrective control of modern power systems,by resorting to human intelligence evaluation *** this basis,this article puts forward an AI self-evolution method based on intelligence assessment through embedding a quantitative intelligence assessment method into automated reinforcement learning(AutoRL)systems.A parallel system based quantitative assessment and self-evolution(PLASE)system for power grid corrective control AI is thereby constructed,taking Bayesian Optimization as the measure of AI evolution to fulfill autonomous evolution of AI under guidance of their intelligence assessment *** results exemplified in the power grid corrective control AI agent show the PLASE system can reliably and quantitatively assess the intelligence level of the power grid corrective control agent,and it could promote evolution of the power grid corrective control agent under guidance of intelligence assessment results,effectively,as well as intuitively improving its intelligence level through selfevolution.
This letter proposes a robust stochastic differential equation approach for learning point-to-point motions in an adversarial way. The proposed stochastic dynamical model combines the advantages of the stochastic diff...
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This letter proposes a robust stochastic differential equation approach for learning point-to-point motions in an adversarial way. The proposed stochastic dynamical model combines the advantages of the stochastic differential equation and the transformer-like function together to achieve both robustness and accuracy of the learning. The adversarial training method is proposed to simplify the way of updating the parameters of the model. The state of the proposed stochastic dynamical system is mathematically proved to converge asymptotically in the mean square sense, and it has been experimentally validated on the LASA dataset and by the trajectory-programming task of the Franka Emika robot. The experimental results show that: (1) the adversarial training method helps the model to achieve higher reproduction accuracy;(2) the trajectories generated by the proposed model achieve higher accuracy in both the noise-free condition (by approximately 14.9%) and the noisy condition (by approximately 17.8%) compared with the state-of-the-art methods in terms of the similarity to the demonstration;and (3) the proposed approach can learn smoother trajectories even if the observations are contaminated by noises.
Soft pressure sensors have recently attracted considerable attention because of their applications in human-machine interface, soft robotics, and prosthetics. However, there remain some challenges in achieving satisfa...
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Soft pressure sensors have recently attracted considerable attention because of their applications in human-machine interface, soft robotics, and prosthetics. However, there remain some challenges in achieving satisfactory performance (e.g., high sensitivity, wide sensing range, high stability) for soft pressure sensors. This article reports an intentional blocking based photoelectric pressure sensor. Two different blocking methods are investigated: the single-row-pyramid blocking and the double-row-pyramid blocking. The sensor has a simple structure, which is made of a light-emitting diode, photosensitive element, and silicone sensor shell. Experiments demonstrate that the sensor has a high sensitivity (the maximum sensitivity is 48.07 kPa(-1), and the minimum measurement pressure is 0.8 Pa), large pressure-sensing range (the sensing range is up to 120 kPa), superior stability (a drift about 0.4% over 12,130 repetitive cycles at 0-80 kPa), low drift (< +/- 0.2% in different 3-day testing), negligible hysteresis, and high signal-to-noise ratio (over 55 dB). By mounting the pressure sensor at the end of a robotic arm, the robot can detect subtle collisions (such as touching a balloon through a pinpoint). In addition, this article fabricates a tactile glove based on the proposed pressure sensor and shows the application of this glove for music playing and object weighing. This study provides a new structure for photoelectric sensors to increase sensitivity and also provides a more convenient way to fabricate photoelectric pressure sensors.
This letter aims to learn a global representation for each point in a random cluster using only purely local geometric or topological information. Based on this, distributed tags for indoor positioning break the atomi...
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This letter aims to learn a global representation for each point in a random cluster using only purely local geometric or topological information. Based on this, distributed tags for indoor positioning break the atomicity of tags and make deployment more arbitrary. It also allows NP-hard matches to be quickly estimated with only one local observation. The novel self-supervised topological representation learning method only takes local point clusters as input and utilizes the proposed cluster-based sampling, training, and loss functions to form global self-comparison. The training samples are generated in real-time virtually, and there are few matching errors after being transferred to practice. The compact backbone network directly processes the coordinates of points and abandons the iterative optimization commonly used in matching. Moreover, it uses the representation to measure similarity directly, and the inference speed reaches the millisecond level. In the actual and virtual experiments, the local point clusters are surprisingly accurately matched to the random global ones. The localization based on this is also verified, and the relevant results prove the effectiveness of the proposed method.
This paper presents a novel and efficient approach to pricing equity-linked guaranteed minimum death benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. Our method assumes that the underl...
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This paper presents a novel and efficient approach to pricing equity-linked guaranteed minimum death benefits (GMDB) with European-style geometric Asian and arithmetic Asian payoffs. Our method assumes that the underlying asset process follows a regime-switching Levy model, which captures the key features of market dynamics in the continuous transition of the economy. To derive the approximate value of GMDB products, we employ the complex Fourier series (CFS) expansion method. Our error analysis demonstrates that this approach exhibits an exponential convergence rate. In our numerical experiments, we compare the CFS approach to other Fourier transform methods and Monte Carlo simulation. The results show that our method outperforms the other approaches in terms of both efficiency and accuracy. This paper contributes to the literature on pricing equity-linked GMDB products by proposing a novel and efficient approach based on regime-switching Levy models and complex Fourier series expansion. The implications of our results may have significant practical implications for the insurance industry and financial markets.
The broad and powerful pectoral fins of manta rays are crucial to their efficient and maneuverable swimming. However, very little is currently known about the pectoral-fin-driven 3-D locomotion of manta-inspired robot...
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The broad and powerful pectoral fins of manta rays are crucial to their efficient and maneuverable swimming. However, very little is currently known about the pectoral-fin-driven 3-D locomotion of manta-inspired robots. This study is focused on the development and 3-D path-following control of an agile robotic manta. First, a novel robotic manta with 3-D mobility is constructed, of which the distinctive pectoral fins provide the only propulsion. Specifically, the unique pitching mechanism is detailed in which the time-coupled coordination movement of the pectoral fins is applied. Second, based on a 6-axis force measuring platform, the propulsion characteristics of the flexible pectoral fins are analyzed. Then, the force-data-driven 3-D dynamic model is further established. Third, a control scheme combined with a line-of-sight (LOS) guidance system and a sliding-mode fuzzy controller is conceived, addressing the 3-D path-following task. Finally, various simulated and aquatic experiments are conducted, demonstrating the superior performance of our prototype and the effectiveness of the proposed path-following scheme. This study will hopefully generate fresh insights into the updated design and control of agile bioinspired robots performing underwater tasks in dynamic environments.
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