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Found 15 papers, 2 papers with code
A neural network-based approach to hybrid systems identification for control
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design.
Control of Cross-Directional Systems using the Generalised Singular Value Decomposition
We demonstrate that the two-array decomposition is linked to a single-array system, which is used to accommodate ill-conditioned systems and compensate for the non-orthogonality of the GSVD.
Robust stabilization of polytopic systems via fast and reliable neural network-based approximations
We consider the design of fast and reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty, including control laws with variable structure and those based on a (minimal) selection policy.
Personalized incentives as feedback design in generalized Nash equilibrium problems
We investigate both stationary and time-varying, nonmonotone generalized Nash equilibrium problems that exhibit symmetric interactions among the agents, which are known to be potential.
Reliably-stabilizing piecewise-affine neural network controllers
A common problem affecting neural network (NN) approximations of model predictive control (MPC) policies is the lack of analytical tools to assess the stability of the closed-loop system under the action of the NN-based controller.
Learning equilibria with personalized incentives in a class of nonmonotone games
We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents.
Model Predictive Control for Electron Beam Stabilization in a Synchrotron
Electron beam stabilization in a synchrotron is a disturbance rejection problem, with hundreds of inputs and outputs, that is sampled at frequencies higher than $10$ kHz.
Probabilistic stabilizability certificates for a class of black-box linear systems
We provide out-of-sample certificates on the controlled invariance property of a given set with respect to a class of black-box linear systems.
Optimization and Control Systems and Control Systems and Control
Stochastic output feedback MPC with intermittent observations
We analyse robustness of the proposed control law with respect to possible uncertainties in the arrival probability of sensor data and we bound the impact of these uncertainties on constraint satisfaction and the discounted cost.
Multi-Array Electron Beam Stabilization using Block-Circulant Transformation and Generalized Singular Value Decomposition
We introduce a novel structured controller design for the electron beam stabilization problem of the UK's national synchrotron light source.
Stochastic MPC with Dynamic Feedback Gain Selection and Discounted Probabilistic Constraints
With dynamic feedback gain selection, the closed loop cost is reduced and conservativeness of Chebyshev's inequality is mitigated.
The optimal transport paradigm enables data compression in data-driven robust control
A new data-enabled control technique for uncertain linear time-invariant systems, recently conceived by Coulson et\ al., builds upon the direct optimization of controllers over input/output pairs drawn from a large dataset.
An active-set algorithm for norm constrained quadratic problems
We present an algorithm for the minimization of a nonconvex quadratic function subject to linear inequality constraints and a two-sided bound on the 2-norm of its solution.
Optimization and Control 90C26, 65F15, 90C90
Distributionally Ambiguous Optimization Techniques for Batch Bayesian Optimization
Our acquisition function is a lower bound on the well-known Expected Improvement function, which requires evaluation of a Gaussian Expectation over a multivariate piecewise affine function.
High-Speed Finite Control Set Model Predictive Control for Power Electronics
To the authors' knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real-time and achieving comparable performance to formulations with long prediction horizons.
Optimization and Control
