According to the existing problems of flexible manipulator control, a kind of fuzzy variable structure controller based on the principle of sliding-mode variable structure control is proposed by well considered both t...
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ISBN:
(纸本)0780378652
According to the existing problems of flexible manipulator control, a kind of fuzzy variable structure controller based on the principle of sliding-mode variable structure control is proposed by well considered both the dynamic and static control properties of the system. Fuzzy variable structure is a hybrid controller that uses fuzzy logic to eliminate chattering phenomenon that is inherent to a sliding-mode variable structure control system. Because the designed fuzzy controller in this paper is a single-input and single-output controller, and the corresponding fuzzy control rules are few, so the approach is very simple for real-time implementation. At last, the control method is verified by computer simulations, based on the dynamics model of a two-link flexible manipulator. Simulation results indicate that the control system exhibits perfect control effects on chattering-reducing and trajectory-tracking.
In this paper, we present a new foraging algorithm applied to multi-robot system. The algorithm classifies robots into two groups; one group is engaged in navigating, while the other in collecting. Due to different du...
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ISBN:
(纸本)9810483643
In this paper, we present a new foraging algorithm applied to multi-robot system. The algorithm classifies robots into two groups; one group is engaged in navigating, while the other in collecting. Due to different duties of robots in foraging, the robots team displays diversity during the foraging process. The results of simulation show that the team of greater diversity would be more efficient than the team without diversity in accomplishing the foraging task. Under the consideration of such factors as the robot team's size and the density of attractors in environment, the relationship between the duty-based diversity and the performance of multi-robot system is discussed based on simulation results.
It is well known, the design of an intelligent control engineering system often applies a top-down strategy, which emphasizes centralized control. But for life systems and life-like systems, adaptation is an important...
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It is well known, the design of an intelligent control engineering system often applies a top-down strategy, which emphasizes centralized control. But for life systems and life-like systems, adaptation is an important property of such systems, and the description of a life system is a bottom-up strategy, which emphasizes a distributed system. From the view point of complexity, the adaptation property of a system emerges by interaction between subsystems of the system, as well as interaction between subsystems and environment. The difference between the concepts of control and emergence is briefly discussed.
Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving soft...
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Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.
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