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This article aims at designing a robust wide-area damping controller (WADC) to address the problems associated with communication failures. The proposed controller is a state feedback-based controller, which uses generators' states as the controller inputs. The structure of the proposed controller's feedback gain matrix is a combination of two feedback gain matrices to achieve adequate robust performance over different disturbances and communication losses. These two feedback gain matrices are; full-scale feedback gain matrix and decentralized scale feedback gain matrix. Both models are designed by considering all generators' states are available at the controller input side. However, both matrices' structures are different from each other. The conventional LQR state feedback control technique is used to design the full-scale matrix. On the other hand, the decentralized scale matrix is computed using a structurally constrained H 2 -norm optimization technique to acquire the required structure. The full-scale model gives robust performance under different faults. Whereas, the decentralized scale matrix will result robust performance against communication failures. Therefore, to achieve superior performance under all conditions, the proposed controller can be used. To show the efficacy of the proposed controller, it is compared with different existing controllers and the results are illustrated. Simulation studies are conducted on well-known IEEE 39-bus and 68-bus test systems to validate the robustness of the proposed controller considering different faults and communication failures. [ABSTRACT FROM AUTHOR]

Background and problem statement: Model-free or learning-based control, in particular, reinforcement learning (RL), is expected to be applied for complex robotic tasks. Traditional RL requires that a policy to be optimized is state-dependent, that means, the policy is a kind of feedback (FB) controllers. Due to the necessity of correct state observation in such a FB controller, it is sensitive to sensing failures. To alleviate this drawback of the FB controllers, feedback error learning integrates one of them with a feedforward (FF) controller. RL can be improved by dealing with the FB/FF policies, but to the best of our knowledge, a methodology for learning them in a unified manner has not been developed. Contribution: In this paper, we propose a new optimization problem for optimizing both the FB/FF policies simultaneously. Inspired by control as inference, the proposed optimization problem considers minimization/maximization of divergences between trajectories, one is predicted by the composed policy and a stochastic dynamics model, and others are inferred as optimal/non-optimal ones. By approximating the stochastic dynamics model using variational method, we naturally derive a regularization between the FB/FF policies. In numerical simulations and a robot experiment, we verified that the proposed method can stably optimize the composed policy even with the different learning law from the traditional RL. In addition, we demonstrated that the FF policy is robust to the sensing failures and can hold the optimal motion. [ABSTRACT FROM AUTHOR]

This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration. [ABSTRACT FROM AUTHOR]