17 citations · 39 across the 13 of their papers we have counts for
14 papers
On Piecewise Quadratic Terminal Costs for MPC
Sampath Kumar Mulagaleti, Boris Houska, Mario Zanon +1
This paper presents a novel approach to synthesize stabilizing termi- nal ingredients for linear model predictive control (MPC) schemes, with the aim of increasing the region of at…
Dual MPC for quasi-Linear Parameter Varying systems
Sampath Kumar Mulagaleti, Alberto Bemporad
We present a dual Model Predictive Control (MPC) framework for the simultaneous identification and control of quasi-Linear Parameter Varying (qLPV) systems. The framework is compos…
Configuration-Constrained Tube MPC for Periodic Operation
Filippo Badalamenti, Jose A. Borja-Conde, Sampath Kumar Mulagaleti +3
Periodic operation often emerges as the economically optimal mode in industrial processes, particularly under varying economic or environmental conditions. This paper proposes a ro…
A Regularization and Active Learning Method for Identification of Quasi Linear Parameter Varying Systems
Sampath Kumar Mulagaleti, Alberto Bemporad
This paper proposes an active learning method for designing experiments to identify quasi-Linear Parameter-Varying (qLPV) models. Since informative experiments are costly, input si…
Sample Efficient Certification of Discrete-Time Control Barrier Functions
Sampath Kumar Mulagaleti, Andrea Del Prete
Control Invariant (CI) sets are instrumental in certifying the safety of dynamical systems. Control Barrier Functions (CBFs) are effective tools to compute such sets, since the zer…
Efficient Configuration-Constrained Tube MPC via Variables Restriction and Template Selection
Filippo Badalamenti, Sampath Kumar Mulagaleti, Mario Eduardo Villanueva +2
Configuration-Constrained Tube Model Predictive Control (CCTMPC) offers flexibility by using a polytopic parameterization of invariant sets and the optimization of an associated ve…
Learning Quasi-LPV Models and Robust Control Invariant Sets with Reduced Conservativeness
Sampath Kumar Mulagaleti, Alberto Bemporad
We present an approach to identify a quasi Linear Parameter Varying (qLPV) model of a plant, with the qLPV model guaranteed to admit a robust control invariant (RCI) set. It builds…
Combined Learning of Linear Parameter-Varying Models and Robust Control Invariant Sets
Sampath Kumar Mulagaleti, Alberto Bemporad
Dynamical models identified from data are frequently employed in control system design. However, decoupling system identification from controller synthesis can result in situations…
Configuration-Constrained Tube MPC for Tracking
Filippo Badalamenti, Sampath Kumar Mulagaleti, Alberto Bemporad +2
This paper proposes a novel tube-based Model Predictive Control (MPC) framework for tracking varying setpoint references with linear systems subject to additive and multiplicative…
Data-Driven Synthesis of Configuration-Constrained Robust Invariant Sets for Linear Parameter-Varying Systems
Manas Mejari, Sampath Kumar Mulagaleti, Alberto Bemporad
We present a data-driven method to synthesize robust control invariant (RCI) sets for linear parameter-varying (LPV) systems subject to unknown but bounded disturbances. A finite-l…
Computation of safe disturbance sets using implicit RPI sets
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Given a stable linear time-invariant (LTI) system subject to output constraints, we present a method to compute a set of disturbances such that the reachable set of outputs matches…
Parameter Dependent Robust Control Invariant Sets for LPV Systems with Bounded Parameter Variation Rate
Sampath Kumar Mulagaleti, Manas Mejari, Alberto Bemporad
Real-time measurements of the scheduling parameter of linear parameter-varying (LPV) systems enables the synthesis of robust control invariant (RCI) sets and parameter dependent co…
Data-driven synthesis of Robust Invariant Sets and Controllers
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
This paper presents a method to identify an uncertain linear time-invariant (LTI) prediction model for tube-based Robust Model Predictive Control (RMPC). The uncertain model is det…
Computation of Input Disturbance Sets for Constrained Output Reachability
Sampath Kumar Mulagaleti, Alberto Bemporad, Mario Zanon
Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is eit…