Multi-stage Economic Nonlinear Model Predictive Control of bioreactors using dynamic flux balance analysis models

Non-causal economic model predictive control for ocean wave energy maximisation based on wave-to-wire model

Multistage economic MPC for systems with a cyclic steady state: A gas network case study

Large wind turbines control using stable economic model predictive pitch technique with state constraints.

Centralised economic model predictive control of froth flotation banks with experimental implementation

Real time multi-inputs multi-outputs economic model predictive control for directional drilling based on fast modeling and sensor fusion

Koopman operator-based stable economic model predictive control for nonlinear systems

ABSTRACT This work proposes an output feedback robust economic model predictive control scheme for constrained nonlinear systems subject to additive disturbances and the estimation error. Through a robustly stable observer, the estimation error set is updated at each sampling instant. We propose a set‐valued estimation approach based on optimization to enhance output feedback and improve closed‐loop economic performance. The homothetic tube approach is utilized to constrain predicted trajectories with uncertainties in order to ensure closed‐loop robust constraints. We prove the recursive feasibility of the optimization control problem and the bounded stability of the closed‐loop system. Simulation results verify the advantages of the proposed scheme.

Abstract We study carbon-priced modal split on capacity-constrained transport networks with piecewise-linear tariff schedules (“tariff blocks”) and quadratic adjustment frictions. We establish turnpike theorems showing that, for any finite horizon, optimal policies spend all but $$O(1)$$ time near the minimizers of a static average-cost program that aggregates logistics costs and carbon charges; the resulting $$O(1)$$ value gap is uniform in the horizon. We then analyze comparative statics of the steady modal mix with respect to the carbon weight and other parameters. Because the steady problem is a parametric convex program with a polyhedral feasible set and piecewise-linear objective, there exists a finite set of policy thresholds at which the optimal steady mix can change discontinuously, while both the steady value and selected minimizers vary Lipschitz-continuously away from those thresholds. We further give envelope identities (e.g., the derivative of the steady value with respect to the carbon weight equals steady emissions) and show that receding-horizon (economic MPC) implementations are near-optimal with bounded, horizon-independent loss. The analysis is purely theoretical; a small synthetic network illustrates threshold locations, jump magnitudes, and boundary-layer dynamics. The results provide transparent diagnostics for pricing vs. capacity instruments and support regime-aware planning on multimodal networks.

Load Frequency Control of Multiarea Interconnected Power System Based on Distributed Economic Model Predictive Control With Guaranteed Stability

Economic model predictive control of vanadium redox flow batteries for power arbitrage

A Flexible Economic MPC Approach for Multimode Operation of Industrial Systems

Maximizing power output from wind turbines while minimizing harmful loads is a critical challenge for operators. Previous research explored the use of economic nonlinear model predictive control for wind turbine operation, but several important aspects were not fully addressed. This study advances the field by optimizing the weighting coefficients of the economic nonlinear model predictive control to improve performance. A comprehensive comparison is carried out between this method and conventional proportional–integral control. In addition, the economic nonlinear model predictive control is evaluated under extreme wind gust conditions in accordance with industry standards. Results demonstrate that the method increases power generation, reduces fatigue and structural loads on turbine components, and shows superior performance under both normal and gusty wind conditions. These findings highlight the potential of the optimized economic nonlinear model predictive control as a robust and effective strategy for improving efficiency and reliability in wind turbine operations.

This paper proposes a robust distributed economic model predictive control (EMPC) strategy for constrained nonlinear systems affected by communication disturbances. The control strategy is formulated within a min–max optimization framework that explicitly addresses communication uncertainties. The controller is determined by online solving a receding horizon economic min–max problem with embedded contractive constraints. Sufficient conditions are provided to ensure recursive feasibility and input-to-state stability of the resulted closed-loop system under disturbances. The effectiveness and robustness of the proposed strategy are validated through a case study of a five-vehicle platoon.

Economic nonlinear model predictive control and scheduling of multiple fed-batch fermenters in a lignocellulosic biorefinery

Machine learning-based economic model predictive control for energy hubs with variable energy efficiencies

An economic model predictive control strategy for EV-integrated microgrids considering battery degradation

Efficient thermoelectric and humidification management of integrated PEMFC systems under zone economic model predictive control

Optimizing energy conversion efficiency of nonlinear wave energy converters via robust Koopman economic model predictive control

Finite-control-set economic model predictive control for a DC/AC inverter