Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their real-world deployment. This letter constructs a decentralized RL-based controller for inverter-based real-time voltage control in distribution systems. It features two components: a transient control policy and a steady-state performance optimizer. The transient policy is parameterized as a neural network, and the steady-state optimizer represents the gradient of the long-term operating cost function. The two parts are synthesized through a safe gradient flow framework, which prevents the violation of reactive power capacity constraints. We prove that if the output of the transient controller is bounded and monotonically decreasing with respect to its input, then the closed-loop system is asymptotically stable and converges to the optimal steady-state solution. We demonstrate the effectiveness of our method by conducting experiments with IEEE 13-bus and 123-bus distribution system test feeders.
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Deep Policy Gradient for Reactive Power Control in Distribution Systems
Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates
well fast, efficient, and robust reactive power control. Optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control (a.k.a., policy) is parameterized in the present work by a deep neural network, whose unknown weights are updated by
minimizing the accumulated power loss over a number of historical and simulated training pairs, using the policy gradient method. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the ‘optimal’ reactive power control decision with only several matrix-vector multiplications. The merits of this novel deep policy gradient approach include its computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real solar and consumption data corroborate these practical merits.
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- Award ID(s):
- 1901134
- NSF-PAR ID:
- 10273949
- Date Published:
- Journal Name:
- Proceedings of IEEE Smartgridcom Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between generalization of control policies to novel environments and generalization of hypotheses in the supervised learning setting. In particular, we utilize the probably approximately correct (PAC)-Bayes framework, which allows us to obtain upper bounds that hold with high probability on the expected cost of (stochastic) control policies across novel environments. We propose policy learning algorithms that explicitly seek to minimize this upper bound. The corresponding optimization problem can be solved using convex optimization (relative entropy programming in particular) in the setting where we are optimizing over a finite policy space. In the more general setting of continuously parameterized policies (e.g., neural network policies), we minimize this upper bound using stochastic gradient descent. We present simulated results of our approach applied to learning (1) reactive obstacle avoidance policies and (2) neural network-based grasping policies. We also present hardware results for the Parrot Swing drone navigating through different obstacle environments. Our examples demonstrate the potential of our approach to provide strong generalization guarantees for robotic systems with continuous state and action spaces, complicated (e.g., nonlinear) dynamics, rich sensory inputs (e.g., depth images), and neural network-based policies.
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Feature representations from pre-trained deep neural networks have been known to exhibit excellent generalization and utility across a variety of related tasks. Fine-tuning is by far the simplest and most widely used approach that seeks to exploit and adapt these feature representations to novel tasks with limited data. Despite the effectiveness of fine-tuning, itis often sub-optimal and requires very careful optimization to prevent severe over-fitting to small datasets. The problem of sub-optimality and over-fitting, is due in part to the large number of parameters used in a typical deep convolutional neural network. To address these problems, we propose a simple yet effective regularization method for fine-tuning pre-trained deep networks for the task of k-shot learning. To prevent overfitting, our key strategy is to cluster the model parameters while ensuring intra-cluster similarity and inter-cluster diversity of the parameters, effectively regularizing the dimensionality of the parameter search space. In particular, we identify groups of neurons within each layer of a deep network that shares similar activation patterns. When the network is to be fine-tuned for a classification task using only k examples, we propagate a single gradient to all of the neuron parameters that belong to the same group. The grouping of neurons is non-trivial as neuron activations depend on the distribution of the input data. To efficiently search for optimal groupings conditioned on the input data, we propose a reinforcement learning search strategy using recurrent networks to learn the optimal group assignments for each network layer. Experimental results show that our method can be easily applied to several popular convolutional neural networks and improve upon other state-of-the-art fine-tuning based k-shot learning strategies by more than10%more » « less
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Abstract Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance. However, prior work that seeks to find the optimal shared control ratio needs an accurate human model, which is usually challenging to obtain. In this study, the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient (DDPG) (TD3X)‐based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally. The robot's states, shared control ratio in the previous time step, and human's control input is used as inputs to the reinforcement learning (RL) agent, which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model. Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio. Furthermore, to accelerate the training process and protect the robot, a navigation demonstration policy and a safety guard are developed. A neural network (NN) structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed. In addition, an extended DAGGER (DAGGERX) human agent is developed for training the RL agent to reduce human workload. Robot simulations and experiments with humans in the loop are conducted. The results show that the DAGGERX human agent can simulate real human inputs in the worst‐case scenarios with a mean square error of 0.0039. Compared to the original TD3 agent, the TD3X‐based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment. The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment, while the performance is equally good in the complex environment because of the use of an advanced human agent. In the human subject tests, participants' average perceived workload was significantly lower in shared control than that in exclusively manual control (26.90 vs. 40.07,
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Accepted Manuscript1.0
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