The maximum power points (MPPs) of photovoltaic (PV) panels vary with atmospheric conditions (solar irradiance, ambient temperature, shading conditions, etc.). Simulation platforms are widely used by engineers and sci...
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ISBN:
(纸本)9781665436137
The maximum power points (MPPs) of photovoltaic (PV) panels vary with atmospheric conditions (solar irradiance, ambient temperature, shading conditions, etc.). Simulation platforms are widely used by engineers and scientists to create models, analyze data, and develop algorithms for the maximum power point tracking (MPPT) of PV systems. However, the behavior, which the algorithm develops in simulations, is usually specific to the characteristics of the models. Strategies that succeed in simulations may not be victoriously transferred to the real world because the modeling errors and sensor noise. In this paper, we create a dynamic model of the PV system only based on the data-sheet from manufacturers. A sim-to-real transfer strategy is proposed for the maximum power point tracking of PV systems. By dynamically randomizing the environments for the agent during the training, the proposed policy can adapt to very different atmospheric conditions. Simulations show that the proposed strategy can maintain a similar level of performance when deployed on the real PV panels.
In this paper, we study randomized consensus processing over general random graphs. At time step k, each node will follow the standard consensus algorithm, or stick to current state by a simple Bernoulli trial with su...
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ISBN:
(纸本)9781457710964;9781457710957
In this paper, we study randomized consensus processing over general random graphs. At time step k, each node will follow the standard consensus algorithm, or stick to current state by a simple Bernoulli trial with success probability p(k). Connectivity-independent and arc-independent graphs are defined, respectively, to capture the fundamental independence of random graph processes with respect to a consensus convergence. Sufficient and/or necessary conditions are presented on the success probability sequence for the network to reach a global a.s. consensus under various conditions of the communication graphs. Particularly, for arc-independent graphs with simple self-confidence condition, we show that Sigma(k) p(k) = infinity is a sharp threshold corresponding to a consensus 0 - 1 law, i.e., the consensus probability is 0 for almost all initial conditions if Sigma(k) p(k) converges, and jumps to 1 for all initial conditions if Sigma(k) p(k) diverges.
Simulations are widely used in the field of photovoltaic systems as they provide an abundant source of data for the building and training of numerical methods or artificial intelligence techniques. However, the strate...
详细信息
ISBN:
(纸本)9781665436137
Simulations are widely used in the field of photovoltaic systems as they provide an abundant source of data for the building and training of numerical methods or artificial intelligence techniques. However, the strategies that succeed in simulation may not be victoriously transferred to the real world due to the modeling errors. In this paper, we propose a Gaussian process regression with domain randomization, which is able to bridge the 'Sim-to-Real' gap in the application of maximum power point estimation. By randomizing the parameters of the models for the training process, the Gaussian process regression models can minimize the 'Sim-to-Real' transfer cost and adapt the dynamics of the real-world environment.
Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of p...
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Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot.
In this paper, we study randomized consensus processing over general random graphs. At time step k, each node will follow the standard consensus algorithm, or stick to current state by a simple Bernoulli trial with su...
详细信息
ISBN:
(纸本)9781457710957
In this paper, we study randomized consensus processing over general random graphs. At time step k, each node will follow the standard consensus algorithm, or stick to current state by a simple Bernoulli trial with success probability p_k, Connectivity-independent and arc-independent graphs are defined, respectively, to capture the fundamental independence of random graph processes with respect to a consensus convergence. Sufficient and/or necessary conditions are presented on the success probability sequence for the network to reach a global a.s. consensus under various conditions of the communication graphs. Particularly, for arc-independent graphs with simple self-confidence condition, we show that ∑_k p_k = ∞ is a sharp threshold corresponding to a consensus 0-1 law, i.e., the consensus probability is 0 for almost all initial conditions if ∑_k p_k converges, and jumps to 1 for all initial conditions if ∑_k p_k diverges.
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