TL;DR
GRAMPC-D provides a modular framework for distributed NMPC of networked systems integrating both optimization and communication, while GRAMPC-S facilitates stochastic NMPC for uncertain systems through different uncertainty propagation methods.
Observer design for nonlinear systems is a relevant and challenging task in systems and control design. In this work, we follow the idea of embedding the system in the class of nonlinear parameter-varying systems to benefit from linear structures as in a standard LPV embedding while keeping some nonlinear structures an...
This paper proposes a Koopman-based stochastic model predictive control (SMPC) approach for unknown nonlinear systems by exploiting partial probabilistic information. Unlike existing Koopman-based SMPC methods that primarily rely on the first- and second-order moments of stochastic Koopman modeling error, the proposed...
The modeling and control of soft pneumatic manipulators present significant challenges due to their inherent compliance and history-dependent hysteresis. While the Koopman operator theory offers a promising solution by embedding these nonlinear dynamics into a linear framework, conventional Koopman approaches are limit...
Distributed model predictive control (DMPC) is an effective method for constrained cooperative control of multirobot systems(MRSs). However, conventional DMPC usually relies on known system models and periodic communication, which may lead to degraded performance and unnecessary resource consumption in the presence of...
This paper develops a convex tube model predictive control formulation for constrained nonlinear systems. We consider dynamics described by a discrete-time state-space model with parametric and additive uncertainty that admits a difference-of-convex decomposition. Convex directional bounds of the nonlinear dynamics are...
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