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Airborne wind energy systems aim to harvest the power of winds blowing at altitudes higher than what conventional wind turbines reach. They employ a tethered flying structure, usually a wing, and exploit the aerodynamic lift to produce electrical power. In the case of ground-based systems, where the traction force on the tether is used to drive a generator on the ground, a two-phase power cycle is...
This paper presents a practicable Scenario-Based Model Predictive Control (Scenario MPC) approach for linear, time-varying systems with additive disturbances. Robust MPC propagates uncertainty through the dynamics based on uncertainty sets and Stochastic MPC by multi-variable convolutions of probability distributions. The idea of Scenario MPC is to propagate the uncertainty by using sampled uncertainty...
This paper is concerned with the design of linear state feedback control laws for linear systems with additive Gaussian disturbances. The objective is to find the feedback gain that minimizes a quadratic cost function in closed-loop operation, while observing chance constraints on the input and/or the state. It is shown that this problem can be cast as a semi-definite program (SDP), in which the chance...
In this work, synthesis and closed-loop operation of robust distributed model predictive control (MPC) for linear systems using distributed optimization is discussed. Previous work has shown that a nominal MPC controller for this setup can be synthesized and operated in a purely distributed manner. This paper extends this concept to linear systems subject to additive bounded disturbance. It is shown...
This work concerns the closed loop performance evaluation of actuators for constrained linear systems. The main motivation is the optimal placement of High Voltage Direct Current links (HVDC) in a meshed AC power system, but the method is also applicable to other actuator selection problems. The goal is the computation of a performance measure, that can be used to rank different candidate actuators...
Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibility, stability, and robustness, at reduced and predictable computational cost, i.e. computation time and storage space. This paper shows how the input...
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