scholarly journals A design method of strong stability self-tuning controller based on on-demand type feedback control

2018 ◽  
Vol 205 (2) ◽  
pp. 26-32
Author(s):  
Akira Yanou
Keyword(s):  
Feedback Control ◽  
Design Method ◽  
Strong Stability ◽  
On Demand ◽  
Self Tuning ◽  
Type Feedback

Self-Tuning Generalized Minimum Variance Control Based on On-Demand Type Feedback Controller

2016 ◽  
Vol 28 (5) ◽  
pp. 674-680 ◽  
Author(s):  
Akira Yanou ◽  
◽  
Mamoru Minami ◽  
Takayuki Matsuno
Keyword(s):  
Design Method ◽  
Strong Stability ◽  
Minimum Variance ◽  
Feedback Controller ◽  
Feedback Signal ◽  
Minimum Variance Control ◽  
On Demand ◽  
Control Objective ◽  
Self Tuning ◽  
Type Feedback

[abstFig src='/00280005/08.jpg' width='300' text='Feedback signal is generated on demand' ] This paper proposes a design method of self-tuning generalized minimum variance control based on on-demand type feedback controller. A controller, such as generalized minimum variance control (GMVC), generalized predictive control (GPC) and so on, can be extended by using coprime factorization. Then new design parameter is introduced into the extended controller, and the parameter can re-design the characteristic of the extended controller, keeping the closed-loop characteristic that way. Although strong stability systems can be obtained by the extended controller in order to design safe systems, focusing on feedback signal, the extended controller can adjust the magnitude of the feedback signal. That is, the proposed controller can drive the magnitude of the feedback signal to zero if the control objective was achieved. In other words the feedback signal by the proposed method can appear on demand of achieving the control objective. Therefore this paper proposes on-demand type feedback controller using self-tuning GMVC for plant with uncertainty. A numerical example is shown in order to check the characteristic of the proposed method.

Autoregressive self-tuning feedback control of the Hénon map

1996 ◽  
Vol 54 (6) ◽  
pp. 6201-6206 ◽  
Author(s):  
Michael E. Brandt ◽  
Ahmet Ademoǧlu ◽  
Dejian Lai ◽  
Guanrong Chen
Keyword(s):  
Feedback Control ◽  
Hénon Map ◽  
Henon Map ◽  
Self Tuning

A Test-Based Methodology for A Priori Selection of Gain/Phase Relationships in Proportional, Phase-Shifting Control of Combustion Instabilities

2000 ◽  
Author(s):  
Michael A. Vaudrey ◽  
William R. Saunders ◽  
Bryan Eisenhower
Keyword(s):  
Feedback Control ◽  
Frequency Response ◽  
Limit Cycle ◽  
Design Method ◽  
Phase Relationship ◽  
Operating Conditions ◽  
Phase Shifting ◽  
Nonlinear Phenomena ◽  
The Absolute ◽  
Instability Frequency

Feedback control system design, for general single-in-single-out (SISO) applications, requires accurate knowledge of the loop transfer function. Active combustion control design is usually implemented using such SISO architectures, but is quite challenging because the thermoacoustic response results from a relatively unknown, self-excited system and nonlinear processes that must be understood before learning the gain/phase relationship of the system precisely at the instability frequency. However, recent experiments have shown that it is possible to obtain accurate measurements of the relevant loop transfer (frequency response) functions at frequencies adjacent to the instability frequency. Using a simple tube combustor, operating with a premixed, gaseous, burner-stabilized flame, the loop frequency response measurements have been used to develop a methodology that leads to ‘test-based predictions’ of the absolute phase settings and ‘best’ gain settings for a proportional, phase-shifting controller commanding an acoustic actuator in the combustor. The contributions of this methodology are twofold. First, it means that a manual search for the required phase setting of the controller is no longer necessary. In fact, this technique allows the absolute value of controller phase to be determined without running the controller. To the authors’ knowledge, this has not been previously reported in the literature. In addition, the ‘best’ gain setting of the controller, based on this new design approach, can be defined as one that eliminates or reduces the limit cycle amplitude as much as possible within the constraint of avoiding generation of any controller-induced instabilities. (This refers to the generation of ‘new’ peaks in the controlled acoustic pressure spectrum.) It is shown that this tradeoff in limit cycle suppression and avoidance of controller-induced instabilities is a manifestation of the well-known tradeoff in the sensitivity/complementary sensitivity function for feedback control solutions. The focus of this article is limited to the presentation of the design method and does not discuss the detailed nonlinear phenomena that must be understood to determine the optimal gain/phase settings at the limit cycle frequency for a real (versus theoretical) combustor system. A companion paper describes how the proposed design method can be used to generate an AI controller that maintains stabilizing control for a range of changing operating conditions.

ChunkSim

2010 ◽  
pp. 131-149
Author(s):  
Pedro Furtado
Keyword(s):  
Performance Analysis ◽  
Simulation Analysis ◽  
System Size ◽  
Analysis Tool ◽  
Data Allocation ◽  
Data Warehouses ◽  
Availability Analysis ◽  
On Demand ◽  
Self Tuning ◽  
Insight Into

Self-tuning physical database organization involves tools that determine automatically the best solution concerning partitioning, placement, creation and tuning of auxiliary structures (e.g. indexes), based on the workload. To the best of our knowledge, no tool has focused on a relevant issue in parallel databases and in particular data warehouses running on common off-the-shelf hardware in a sharednothing configuration: determining the adequate tradeoff for balancing load and availability with costs (storage and loading costs). In previous work, we argued that effective load and availability balancing over partitioned datasets can be obtained through chunk-wise placement and replication, together with on-demand processing. In this work, we propose ChunkSim, a simulator for system size planning, performance analysis against replication degree and availability analysis. We apply the tool to illustrate the kind of results that can be obtained by it. The whole discussion in the chapter provides very important insight into data allocation and query processing over shared-nothing data warehouses and how a good simulation analysis tool can be built to predict and analyze actual systems and intended deployments.

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