Cloud computing is expanding faster than anticipated;hence, load balancing must be done effectively. Conventional methods like Round Robin and static prioritization tend to struggle under dynamic demand, resulting in ...
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With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the contro...
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ISBN:
(纸本)9781665491907
With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the controller for each morphology to calculate fitness, which is time-consuming. In contrast, the dual-network framework utilizes data collected by individual networks under a specific morphology to train a population network that provides a surrogate function for morphology optimization. This approach replaces the traditional evaluation of a diverse set of candidates, thereby speeding up the training. Despite considerable results, the online training of both networks impedes their performance. To address this issue, we propose a concurrent network framework that combines online and offline reinforcement learning (RL) methods. By leveraging the behavior cloning term in a flexible manner, we achieve an effective combination of both networks. We conducted multiple sets of comparative experiments in the simulator and found that the proposed method effectively addresses issues present in the dual-network framework, leading to overall algorithmic performance improvement. Furthermore, we validated the algorithm on a real robot, demonstrating its feasibility in a practical application.
Biometric authentication has emerged as an important component in strengthening cyber security, providing a superior alternative to conventional password-based systems because of their distinctive and difficult-to-dup...
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The present research looks at the crucial role that Strategic Information systems (SIS) play in promoting organizational transformation and change in the IT industry. In-depth interviews with 10 IT specialists and org...
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In order to quickly realize the composite optical axis stabilization, this article develops a RTX-based Rapid control Prototyping (RCP) system. The composite optical axis stabilization consists of inertial and optical...
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ISBN:
(纸本)9798350375084;9798350375077
In order to quickly realize the composite optical axis stabilization, this article develops a RTX-based Rapid control Prototyping (RCP) system. The composite optical axis stabilization consists of inertial and optical-mechanical image stabilization, by reasonably decomposing the control range of which, the stabilization accuracy of the optical axis is improved. The RTX shared memory technology is used to separate the real-time domain from the non-real-time domain, realizing the real-time control and communication of each controller, and effectively improving the control bandwidth of the system. Experimental test shows that the real-time performance of the RCP platform meets the requirements, achieving functions such as the identification, control and testing of the composite optical axis stabilization system. Due to the generic design, the RCP platform is able to support subsequent controlsystems of the same type.
This research paper investigates the implementation and performance of an smart Fuzzy-PID Temperature control approach within the context of optimizing the microclimate in intelligent buildings. The study delves into ...
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In today's fast-changing business environment, organizations need to be adaptable and responsive to change. Effective decision-making enables companies to cope with uncertainty and adjust their strategies and oper...
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ISBN:
(纸本)9798350373981;9798350373974
In today's fast-changing business environment, organizations need to be adaptable and responsive to change. Effective decision-making enables companies to cope with uncertainty and adjust their strategies and operations promptly. In this context, the emergence of intelligent decision support systems (DSS) shows great promise. These systems enable companies to analyze data, identify patterns, and offer valuable insights to facilitate informed decision-making. An intelligent DSS for updating control plans is presented in this paper. The proposed system incorporates two methods to assist the decision-maker in choosing the optimal control scenario. The first involves making a manual decision based on several criteria. The second uses the case-based reasoning (CBR) technique to make an automatic recommendation. Moreover, the suggested recommender system (RS) enables the control plans to be updated regularly depending on the current state of the process quality. To increase the quality of decisions, CBR is used to acquire the necessary knowledge. The viability and applicability of the suggested RS are demonstrated through a practical case study.
This paper presents a novel approach to automated drifting with a standard passenger vehicle, which involves a Nonlinear Model Predictive control to stabilise and maintain the vehicle at high sideslip angle conditions...
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ISBN:
(纸本)9798350355376;9798350355369
This paper presents a novel approach to automated drifting with a standard passenger vehicle, which involves a Nonlinear Model Predictive control to stabilise and maintain the vehicle at high sideslip angle conditions. The proposed controller architecture is split into three components. The first part consists of the offline computed equilibrium maps, which provide the equilibrium points for each vehicle state given the desired sideslip angle and radius of the path. The second is the predictive controller minimising the errors between the equilibrium and actual vehicle states. The third is a path-following controller, which reduces the path error, altering the equilibrium curvature path. In a high-fidelity simulation environment, we validate the controller architecture capacity to stabilise the vehicle in automated drifting along a desired path, with a maximal lateral path deviation of 1 m. In the experiments with a standard passenger vehicle, we demonstrate that the proposed approach is capable of bringing and maintaining the vehicle at the desired 30 deg sideslip angle in both high and low friction conditions.
When using the FastICA algorithm for blind signal separation, if the time delay of the signals is considered, the mixed signals are convolution superposition between independent signals. At this time, the FastICA algo...
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In this paper a control architecture designed in 3D for landing a fixed-wing drone on a moving Ground Vehicle (GV) is presented. Two control schemes are proposed, one leads the fixed-wing vehicle towards a desired tra...
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ISBN:
(纸本)9798350399462
In this paper a control architecture designed in 3D for landing a fixed-wing drone on a moving Ground Vehicle (GV) is presented. Two control schemes are proposed, one leads the fixed-wing vehicle towards a desired trajectory while the other regulates the GV's speed and tracks the relative position of the aerial vehicle in the x - y plane. The desired trajectory for the aerial vehicle is based on a hyperbolic tangent function, which guarantees a soft descending flight until reach the GV's altitude. The control strategy for the GV allows it to align with the aerial vehicle until its landing. The Lyapunov theory is used for conceiving the control strategies, guaranteeing the stability on each control stage. The control architecture is evaluated in numerical simulations for validating the systems performance in closed loop. Finally, a preliminary experiment is presented for corroborating its well performance.
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