Supervisory control

About: Supervisory control is a research topic. Over the lifetime, 7128 publications have been published within this topic receiving 129025 citations.

Supervisory Control of an Adaptive-Droop Regulated DC Microgrid With Battery Management Capability

Abstract: DC power systems are gaining an increasing interest in renewable energy applications because of the good matching with dc output type sources such as photovoltaic (PV) systems and secondary batteries. In this paper, several distributed generators (DGs) have been merged together with a pair of batteries and loads to form an autonomous dc microgrid (MG). To overcome the control challenge associated with coordination of multiple batteries within one stand-alone MG, a double-layer hierarchical control strategy was proposed. 1) The unit-level primary control layer was established by an adaptive voltage-droop method aimed to regulate the common bus voltage and to sustain the states of charge (SOCs) of batteries close to each other during moderate replenishment. The control of every unit was expanded with unit-specific algorithm, i.e., finish-of-charging for batteries and maximum power-point tracking (MPPT) for renewable energy sources, with which a smooth online overlap was designed and 2) the supervisory control layer was designed to use the low-bandwidth communication interface between the central controller and sources in order to collect data needed for adaptive calculation of virtual resistances (VRs) as well as transit criteria for changing unit-level operating modes. A small-signal stability for the whole range of VRs. The performance of developed control was assessed through experimental results.

Advanced Process Control

Abstract: This chapter explores both run-by-run and supervisory process control strategies. Such advanced process control techniques are required more and more for increasingly sophisticated modern semiconductor manufacturing applications. The chapter addresses control in two main categories. In this first, we deal with controllers that utilize polynomial process models, but limit their adaptation to the constant term of the model. Afterwards, we discuss controllers that can adapt the entire model.

Supervisory control of discrete-event processes with partial observations

Abstract: Extends certain aspects of the work of P.J. Ramadge and W.M. Wonham (see SIAM J. Control Optimiz., vol.25, Jan. 1987) on the control of a class of discrete-event processes. The controlled process is described by a language L having strings that specify the sequences of events sigma /sub 1/ . . . sigma /sub n/ that the process can execute. The controller makes partial observations on the process events. Based on these observations the controller must enable or disable certain process events so that the resulting language generated by the closed-loop process is the specified sublanguage K contained in/implied by L. The case of decentralized control in which there are several controllers each of which makes partial observations and controls a subset of the process events is also studied. The results are illustrated for an example of communication protocols. >

A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles

Abstract: Hybrid electric vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. The parallel HEV control problem involves the determination of the time profiles of the power flows from the engine and the electric motor. This is also referred to as the power split between the conventional and the electric sources. The objective of HEV control is in fact to find out the sequence of optimal power splits at each instant of time that minimizes the fuel consumption over a given driving schedule. Big obstacles to the control design are the model complexity and the necessity of "a priori" knowledge of torque and velocity profiles. This paper presents three different energy management approaches for the control of a parallel hybrid electric sport-utility-vehicle that do not require a priori knowledge of the driving cycle. The considered approaches are: a rule-based control, an adaptive equivalent fuel consumption minimization strategy (A-ECMS), and the Hinfin control. Results, compared with the optimal solution given by the dynamic programming, show that the A-ECMS strategy is the best performing strategy

The unfalsified control concept and learning

Abstract: Without a plant model or other prejudicial assumptions, a theory is developed for identifying control laws which are consistent with performance objectives and past experimental data-possibly before the control laws are ever inserted in the feedback loop. The theory complements model-based methods such as H/sup /spl infin//-robust control theory by providing a precise characterization of how the set of suitable controllers shrinks when new experimental data is found to be inconsistent with prior assumptions or earlier data. When implemented in real time, the result is an adaptive switching controller. An example is included.