Electrical and Electronic Engineering - Research Publications
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ItemRobustness of networked control systems with multiple actuator-links and bounded packet dropouts(IEEE, 2013-01-01)This paper presents an MPC based controller and network protocol co-design strategy for networked control systems with multiple controller-actuator links. These links are closed via unreliable data-like network which allows access to only one actuator node at each time instant. The concept of nonlinear gains is used to show that in the case of uniform boundedness of the number of consecutive packet dropouts, nonlinear gain ℓ2 stability can be ensured via appropriate choice of design parameters. Numerical simulations illustrate the potential of the proposed strategy.
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ItemUniform Global Asymptotic Stability of Networked Control Systems affected with packet dropouts and scheduling issues(IEEE, 2015)We focus on Networked Control Systems where the network induces two communication issues: one of them is packet dropouts while the other is scheduling. To mitigate the corresponding undesirable effects, such as instability or deteriorated performance, we use a protocol and controller co-design method. In particular, we adopt a deterministic Model Predictive Control (MPC) framework. We establish Uniform Global Asymptotic Stability (UGAS) by assuming a finite bound on the number of consecutive packet dropouts and appropriate modifications to standard MPC stability-related assumptions. We show UGAS through finding an appropriate Lyapunov candidate function.
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ItemControllability of Discrete-time Networked Control Systems with Try Once Discard Protocol(IFAC - International Federation of Automatic Control, 2014)This paper investigates controllability of discrete-time Networked Control Systems. The distinguishing feature is that the network imposes scheduling. The network is characterized by a dynamic protocol and different types of additional processing capabilities, as determined by available technology. For NCS with general nonlinear plants we present general controllability results. Finally, for NCS with linear plants we extend ideas motivated by NCS architectures with static protocols to state corresponding controllability results.
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ItemGaussian Processes with Monotonicity Constraints for Preference Learning from Pairwise Comparisons(IEEE, 2018)In preference learning, it is beneficial to incorporate monotonicity constraints for learning utility functions when there is prior knowledge of monotonicity. We present a novel method for learning utility functions with monotonicity constraints using Gaussian process regression. Data is provided in the form of pairwise comparisons between items. Using conditions on monotonicity for the predictive function, an algorithm is proposed which uses the weighted average between prior linear and maximum a posteriori (MAP) utility estimates. This algorithm is formally shown to guarantee monotonicity of the learned utility function in the dimensions desired. The algorithm is tested in a Monte Carlo simulation case study, in which the results suggest that the learned utility by the proposed algorithm performs better in prediction than the standalone linear estimate, and enforces monotonicity unlike the MAP estimate.
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ItemA machine learning approach for tuning model predictive controllers(IEEE, 2018-01-01)Many industrial domains are characterized by Multiple-Input-Multiple-Output (MIMO) systems for which an explicit relationship capturing the nontrivial trade-off between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closed-loop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the human-learned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.