La caractérisation du transistor par le tuner source & load pull pour l'amplificateur de puissance classe F inverse

RÉSUMÉ: Dans ce travail, la méthodologie de conception de premier passage pour des amplificateurs de puissance de classe F inverse est présentée. Pour concevoir l'amplificateur de puissance de classe F inverse à 3.5 GHz avec un signal 1-ton et un signal LTE, le transistor CGH40010 de Cree Inc. est analysé et caractérisé avec un signal I-ton et un signal LTE. La caractérisation et l'analyse sont exploitées par la simulation source & load pull dans le logiciel ADS 2011.10 et par le système source & load pull tuner multi-harmonique passif de Focus microwaves Inc. Les 2ème et 3ème harmoniques à l'entrée et à la sortie du transistor sont pris en compte dans les caractérisations. L'analyse des résultats de la caractérisation par la simulation et par le système de tuner a prouvé que non seulement les 2ème et 3ème harmoniques à la sortie du transistor, mais également les 2ème et 3ème harmoniques à l'entrée du transistor sont importantes pour atteindre un rendement élevé et une puissance de sortie élevée. Le coefficient de réflexion (r) maximal du tuner passif à des fréquences harmoniques est important dans la caractérisation du transistor, car un r élevé peut augmenter le rendement en puissance ajoutée et la puissance de sortie de l'amplificateur de puissance de classe F inverse. Pour augmenter le coefficient de réflexion (r) maximal de tuner, les accessoires utilisés dans le système de tuner, tels que les circuits de polarisation, les coupleurs directifs et l'isolateur, sont analysés. Sur la base de cette analyse, le r maximal du tuner pourrait être augmenté de 0.902 à 0.930 par choisir les accessoires. Sur la base des résultats des caractérisations obtenus par la simulation et le système source & load pull tuner, les amplificateurs de classe F inverse sont conçus et fabriqués. En comparant les résultats mesurés des amplificateurs de puissance qui sont fabriqués sur la base des résultats de caractérisation correspondants obtenus par la simulation et le système de tuner, nous avons constaté que le système de tuner peut prédire les résultats, tel que le rendement en puissance ajoutée et la puissance de sortie, plus précisément que la simulation avec le modèle du transistor de grand signal. Pour l'amplificateur conçu sur la base des résultats de caractérisation obtenus par le système de tuner avec un signal I-ton, lorsque la puissance de sortie est 40.02 dBm, le rendement en puissance ajoutée est 79.76% avec 12.08 dB de gain. Pour l'amplificateur conçu sur la base des résultats de caractérisation obtenus par le système de tuner avec un signal LTE, lorsque la puissance de sortie est de 34.20 dBm et le gain est 16.20 dB, le rendement en puissance ajoutée est 49.56%. Le taux de puissance du canal adjacent 1, qui a un décalage de 10 MHz, est -29.45 dBc, et le taux de puissance du canal adjacent 2, qui a un décalage de 20 MHz, est -50.87 dBc. -- Mots clés: Le circuit de polarisation, la caractérisation de transistor, l'amplificateur de puissance de classe F inverse, tuner source & load pull. -- ABSTRACT: In this work, a first-pass design methodology of designing the inverse c1ass F power amplifier is presented. In order to design the inverse class F power amplifiers for the I-tone signal and the LTE signal, Cree's CGH40010 transistor is analyzed and characterized with a I-tone signal at 3.5 GHz and a LTE signal at 3.5 GHz with 10 MHz bandwidth. The characterization and analysis are operated by the source & load pull simulation in the software ADS 2011.10 with large signal transistor model and by the passive multiharmonic source & load pull tuner system from Focus microwaves Inc. In the characterizations of transistor, 2nd and 3rd harmonic on both input and output side of the transistor are considered. The analysis of the transistor's characterization shows that, not only the 2nd and 3rd harmonics on the output side of the transistor are important to achieve high power added efficiency and output power for an inverse class F power amplifier, but also the 2nd and 3rd harmonics on the input side of the transistor. High reflection coefficient (r) achieved by the passive source and load pull tuner at harmonic frequencies is important in the transistor characterization, since the high r can increase the power added efficiency and the output power of the inverse class F power amplifier. To increase the maximum r of the passive tuner system, the accessories in the passive tuner system, such as bias tee, directional coupler and isolator, are analyzed. Based on the analysis, the maximum r of the passive tuner system could be increased from 0.902 to 0.930 by choosing the accessories. Based on the characterization results obtained by the simulation and the tuner system, bias circuit and impedance matching networks are analyzed and designed for the inverse class F power amplifiers. Bias circuit is designed to maximize the RF isolation to the De input port and the return loss, and minimize the insertion loss at fundamental frequency. The inverse c1ass F power amplifiers designed based on the characterization results are fabricated and measured. By comparing the measured results of the power amplifiers which fabricated based on the corresponding characterization results obtained by simulation and tuner system, we found that the multi-harmonic passive source & load pull tuner system can predict the power added efficiency and the output power more precisely than the simulation with the large signal transistor model. For the fabricated I-tone inverse class F power amplifier designed based on the I-tone characterization result obtained by the tuner system, when the output power is 40.02 dBm, the power added efficiency is 79.76% with a gain of 12.08 dB. For the fabricated LTE inverse class F power amplifier designed based on the LTE characterization result obtained by the tuner system, the measured output power is 34.20 dBm with a power added efficiency of 49.56% and a gain of 16.20 dB. The measured worst adjacent channel power ratio 1 with 10 MHz offset is -29.45 dBc. The measured worst adjacent channel power ratio 2 with 20 MHz offset is -50.87 dBc. -- Keywords : Bias circuit, characterization, inverse c1ass F power amplifier, source & load pull tuner

EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data

Sample efficiency remains a crucial challenge in applying Reinforcement Learning (RL) to real-world tasks. While recent algorithms have made significant strides in improving sample efficiency, none have achieved consistently superior performance across diverse domains. In this paper, we introduce EfficientZero V2, a general framework designed for sample-efficient RL algorithms. We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs. With a series of improvements we propose, EfficientZero V2 outperforms the current state-of-the-art (SOTA) by a significant margin in diverse tasks under the limited data setting. EfficientZero V2 exhibits a notable advancement over the prevailing general algorithm, DreamerV3, achieving superior outcomes in 50 of 66 evaluated tasks across diverse benchmarks, such as Atari 100k, Proprio Control, and Vision Control.Comment: 21 pages,10 figure

SpaceOctopus: An Octopus-inspired Motion Planning Framework for Multi-arm Space Robot

Space robots have played a critical role in autonomous maintenance and space junk removal. Multi-arm space robots can efficiently complete the target capture and base reorientation tasks due to their flexibility and the collaborative capabilities between the arms. However, the complex coupling properties arising from both the multiple arms and the free-floating base present challenges to the motion planning problems of multi-arm space robots. We observe that the octopus elegantly achieves similar goals when grabbing prey and escaping from danger. Inspired by the distributed control of octopuses' limbs, we develop a multi-level decentralized motion planning framework to manage the movement of different arms of space robots. This motion planning framework integrates naturally with the multi-agent reinforcement learning (MARL) paradigm. The results indicate that our method outperforms the previous method (centralized training). Leveraging the flexibility of the decentralized framework, we reassemble policies trained for different tasks, enabling the space robot to complete trajectory planning tasks while adjusting the base attitude without further learning. Furthermore, our experiments confirm the superior robustness of our method in the face of external disturbances, changing base masses, and even the failure of one arm.Comment: 8 pages, 9 figure

CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models

Foundation models pre-trained on web-scale data are shown to encapsulate extensive world knowledge beneficial for robotic manipulation in the form of task planning. However, the actual physical implementation of these plans often relies on task-specific learning methods, which require significant data collection and struggle with generalizability. In this work, we introduce Robotic Manipulation through Spatial Constraints of Parts (CoPa), a novel framework that leverages the common sense knowledge embedded within foundation models to generate a sequence of 6-DoF end-effector poses for open-world robotic manipulation. Specifically, we decompose the manipulation process into two phases: task-oriented grasping and task-aware motion planning. In the task-oriented grasping phase, we employ foundation vision-language models (VLMs) to select the object's grasping part through a novel coarse-to-fine grounding mechanism. During the task-aware motion planning phase, VLMs are utilized again to identify the spatial geometry constraints of task-relevant object parts, which are then used to derive post-grasp poses. We also demonstrate how CoPa can be seamlessly integrated with existing robotic planning algorithms to accomplish complex, long-horizon tasks. Our comprehensive real-world experiments show that CoPa possesses a fine-grained physical understanding of scenes, capable of handling open-set instructions and objects with minimal prompt engineering and without additional training. Project page: https://copa-2024.github.io

Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization

Reinforcement learning (RL) has achieved promising results on most robotic control tasks. Safety of learning-based controllers is an essential notion of ensuring the effectiveness of the controllers. Current methods adopt whole consistency constraints during the training, thus resulting in inefficient exploration in the early stage. In this paper, we propose an algorithm named Constrained Policy Optimization with Extra Safety Budget (ESB-CPO) to strike a balance between the exploration efficiency and the constraints satisfaction. In the early stage, our method loosens the practical constraints of unsafe transitions (adding extra safety budget) with the aid of a new metric we propose. With the training process, the constraints in our optimization problem become tighter. Meanwhile, theoretical analysis and practical experiments demonstrate that our method gradually meets the cost limit's demand in the final training stage. When evaluated on Safety-Gym and Bullet-Safety-Gym benchmarks, our method has shown its advantages over baseline algorithms in terms of safety and optimality. Remarkably, our method gains remarkable performance improvement under the same cost limit compared with baselines.Comment: 7 pages, 8 figure

DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands

Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throw-catching behavior has the potential to increase picking speed without transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Stability-Constrained Reinforcement Learning (SCRL) algorithm to learn to catch diverse objects with dexterous hands. The SCRL algorithm outperforms baselines by a large margin, and the learned policies show strong zero-shot transfer performance on unseen objects. Remarkably, even though the object in a hand facing sideward is extremely unstable due to the lack of support from the palm, our method can still achieve a high level of success in the most challenging task. Video demonstrations of learned behaviors and the code can be found on the supplementary website

Foundation Reinforcement Learning: towards Embodied Generalist Agents with Foundation Prior Assistance

Recently, people have shown that large-scale pre-training from internet-scale data is the key to building generalist models, as witnessed in NLP. To build embodied generalist agents, we and many other researchers hypothesize that such foundation prior is also an indispensable component. However, it is unclear what is the proper concrete form to represent those embodied foundation priors and how they should be used in the downstream task. In this paper, we propose an intuitive and effective set of embodied priors that consist of foundation policy, value, and success reward. The proposed priors are based on the goal-conditioned MDP. To verify their effectiveness, we instantiate an actor-critic method assisted by the priors, called Foundation Actor-Critic (FAC). We name our framework as Foundation Reinforcement Learning (FRL), since it completely relies on embodied foundation priors to explore, learn and reinforce. The benefits of FRL are threefold. (1) Sample efficient. With foundation priors, FAC learns significantly faster than traditional RL. Our evaluation on the Meta-World has proved that FAC can achieve 100% success rates for 7/8 tasks under less than 200k frames, which outperforms the baseline method with careful manual-designed rewards under 1M frames. (2) Robust to noisy priors. Our method tolerates the unavoidable noise in embodied foundation models. We show that FAC works well even under heavy noise or quantization errors. (3) Minimal human intervention: FAC completely learns from the foundation priors, without the need of human-specified dense reward, or providing teleoperated demos. Thus, FAC can be easily scaled up. We believe our FRL framework could enable the future robot to autonomously explore and learn without human intervention in the physical world. In summary, our proposed FRL is a novel and powerful learning paradigm, towards achieving embodied generalist agents