19,366 research outputs found
Incorporating control performance tuning into economic model predictive control
This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ECC.2015.7330599Economic model predictive control (eMPC), where an economic objective is used directly as the objective function of the control system, has gained much popularity in recent literature. However, with a purely economic objective, the control designer has no influence over the control performance of the process. In this paper, we propose a means of tuning the objective function in order to give some level of control performance. Also, the stability proof for eMPC relies on some strict-dissipativity condition. We also show how this condition can be satisfied when the system is only dissipative with respect to the original objective function.O I. Olanrewaju is sponsored by the Federal Government of Nigeri
Reliability-based economic model predictive control for generalized flow-based networks including actuators' health-aware capabilities
This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamically allocate safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuatorsâ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the considered case study.Peer ReviewedPostprint (author's final draft
Min-Max Predictive Control of a Five-Phase Induction Machine
In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for
the predictive stator current control of a five-phase induction machine (IM). The min-max operator
is explored for the first time as an alternative to the traditional loss function. With this proposal,
the selection of voltage vectors does not need weighting factors that are normally used within
the loss function and require a cumbersome procedure to tune. In order to cope with conflicting
criteria, the proposal uses a decision function that compares predicted errors in the torque producing
subspace and in the x-y subspace. Simulations and experimental results are provided, showing how
the proposal compares with the traditional method of fixed tuning for predictive stator current control.Ministerio de EconomĂa y Competitividad DPI 2016-76493-C3-1-R y 2014/425UniĂłn Europea DPI 2016-76493-C3-1-R y 2014/425Universidad de Sevilla DPI 2016-76493-C3-1-R y 2014/42
Predictive control for energy management in all/more electric vehicles with multiple energy storage units
The paper describes the application of Model Predictive Control (MPC) methodologies for application to electric and hybrid-electric vehicle drive-train formats incorporating multiple energy/power sources. Particular emphasis is given to the co-ordinated management of energy flow from the multiple sources to address issues of extended vehicle range and battery life-time for all-electric drive-trains, and emissions reduction and drive-train torsional oscillations, for hybrid-electric counterparts, whilst accommodating operational constraints and, ultimately, generic non-standard driving cycles
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