Search results for "UNKNOWN INPUTS"

showing 2 items of 2 documents

Average flow constraints and stabilizability in uncertain production-distribution systems

2009

We consider a multi-inventory system with controlled flows and uncertain demands (disturbances) bounded within assigned compact sets. The system is modelled as a first-order one integrating the discrepancy between controlled flows and demands at different sites/nodes. Thus, the buffer levels at the nodes represent the system state. Given a long-term average demand, we are interested in a control strategy that satisfies just one of two requirements: (i) meeting any possible demand at each time (worst case stability) or (ii) achieving a predefined flow in the average (average flow constraints). Necessary and sufficient conditions for the achievement of both goals have been proposed by the aut…

Mathematical optimizationStochastic stabilityControl and OptimizationComputer scienceSCHEDULING POLICIESUNKNOWN INPUTSInventory control; Robust controlRobust controlUncertain systemsUncertain demandsManagement Science and Operations ResearchControl strategies; Inventory systems; Uncertain demands; Worst caseStability (probability)Distribution systemMULTI-INVENTORY SYSTEMSControl theoryProduction (economics)Inventory control Robust control Stochastic stabilityAverage costInventory systemsMathematicsInventory controlStochastic processControl strategiesApplied MathematicsWorst caseNETWORKSControllabilityFlow (mathematics)Bounded functionProduction controlRobust controlSettore MAT/09 - Ricerca OperativaMANUFACTURING SYSTEMS
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Design of unknown inputs proportional integral observers for TS fuzzy models

2014

In this paper the design of unknown inputs proportional integral observers for Takagi-Sugeno (TS) fuzzy models subject to unmeasurable decision variables is proposed. These unknown inputs affect both state and output of the system. The synthesis of these observers is based on two hypotheses that the unknown inputs are under the polynomials form with their kth derivatives zero for the first one and bounded norm for the second one, hence two approaches. The Lyapunov theory and L"2-gain technique are used to develop the stability conditions of such observers in LMIs (linear matrix inequality) formulation. A simulation example is given to validate and compare the proposed design conditions for …

Lyapunov functionUnknown inputs reconstructionCognitive NeuroscienceLinear matrix inequalityComputer Science Applications1707 Computer Vision and Pattern RecognitionFuzzy logicComputer Science ApplicationsStability conditionssymbols.namesakeDecision variablesComputer Science::Systems and ControlArtificial IntelligenceControl theoryBounded functionNorm (mathematics)Unmeasurable decision variablessymbolsTS fuzzy modelsProportional integral observer; TS fuzzy models; Unknown inputs reconstruction; Unmeasurable decision variables; Artificial Intelligence; Computer Science Applications1707 Computer Vision and Pattern Recognition; Cognitive NeuroscienceProportional integral observerMathematicsNeurocomputing
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