Model predictive control of nonlinear hybrid systems with discrete inputs employing a hybrid fuzzy model

Karer, Gorazd; Mušič, Gasper; Škrjanc, Igor; Zupančič, Borut

doi:10.1016/j.nahs.2006.06.005

Cited by 21 publications

(20 citation statements)

References 22 publications

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“…The solar thermal system implemented in the ENERGYbase comprises a glass-covered flat plate solar collector field with a total aperture area of 274m 2 [14]. The steady-state behavior of a flat plate solar collector can be easily described using its efficiency, see (5).…”

Section: B Solar Collector Modelmentioning

confidence: 99%

“…c p describes the specific heat capacity of the heating medium.Q col is taken from (6) whereas the collector efficiency is provided by (5).…”

Section: B Solar Collector Modelmentioning

confidence: 99%

“…Though it has been the elected control scheme in many research areas, in particular for mechanical, electrical or chemical systems (see e.g. : [4], [5] or [6]), its use in building research is relatively still very rare. However, the benefits of MBPC have attracted attention of many researchers in building engineering, therefore increasing its popularity rapidly.…”

Section: Introductionmentioning

confidence: 99%

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Model based predictive control for a solar-thermal system

2011

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Buildings and their components account for a major amount of the overall global energy consumption. There is a rising demand to increase the end-use energy efficiency. Advanced automation and control for buildings and their components is one possibility how to achieve the desired goal of lower energy consumption. The model based predictive control approach as a special form of optimal control offers a good way to increase energy efficiency. This paper presents the employment of a model based predictive control algorithm for the energy efficient temperature control of a solar-thermal system consisting of a solar collector and a heat exchanger. The design of the controller is based upon a physical lumped model of the system components. In order to illustrate the potential of the model predictive approach for the use in building automation the comparison to a standard PI control approach is made where the energy consumption for both control concepts is analyzed.

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Section: B Solar Collector Modelmentioning

confidence: 99%

“…c p describes the specific heat capacity of the heating medium.Q col is taken from (6) whereas the collector efficiency is provided by (5).…”

Section: B Solar Collector Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model based predictive control for a solar-thermal system

2011

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“…Such control technique already exists in many applications, especially for systems where the reference trajectory is well known a priori, such as robots [6] or robotic arms [7], chemical or biochemical systems [8], aerospace [9], and cement industry [7]. It is also a simple way to approach a control law in the time domain, and demonstrate many control related problems related to the regulation of multivariable systems [10], unstable systems [11], systems with delay [14], nonlinear systems [13], and hybrid systems [12]. Because of these advantages and applications of MPC, in this paper we present a model predictive control using laguerre functions to design a dual-stage servo control.…”

Section: Introductionmentioning

confidence: 99%

Discrete-time model predictive control for head-positioning servomechanism in a dual-stage hard disk drive

Rahman

Mamun

Yao

et al. 2014

2014 IEEE International Conference on Mechatronics and Automation

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A dual-stage Hard disk drive (HDD) consists of a coarse actuator for large motion and a micro-actuator facilitating small but precise motion to improve tracking performance. Control design of dual-stage HDD servomechanism suffers from hard constraints due to actuator saturation limits and position output trajectory requirements. This paper presents a discrete time model predictive control using Laguerre functions with the application to a dual-stage hard disk drive which expressly considers actuator constrains and does not need any controller tuning for different references. Different simulation results are shown to substantiate the robust tracking performance of the controller and efficient disturbance rejection capability.

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“…Many applications of such control technique already exist, especially for systems for which the path to follow is well known a priori, such as robots [14] or robotic arms [15], machine tools [16], oil industry [17] [18], chemical or biochemical systems [19] [20], aerospace [21], thermal [14] and cement industry [15]. It is also a simple way to approach a control law in the time domain [14], and demonstrate, through its qualities, many problems related to the regulation of multivariable systems [30], unstable systems [31], systems with delay [22], nonlinear systems [33], and hybrid systems [32]. In this paper, predictive schemes are applied under nominal conditions as well as in different situations including disturbances and uncertainties on the system parameters.…”

Section: Introductionmentioning

confidence: 99%

Model predictive tracking control for a head-positioning in a Hard-Disk-Drive

Taktak-Meziou¹,

Chemori

Ghommam³

et al. 2013

21st Mediterranean Conference on Control and Automation

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This paper deals with the track-following problem of a Read/Write (R/W) head of a Hard-Disk-Drive (HDD) servo-system, which is resolved with two control algorithms generated from Model Predictive Control (MPC). The first approach consists of a classical linear MPC without constraints. The second method is inspired from the MPC technique but uses a reference trajectory to steer the actuator as close as possible to the set-point trajectory. Numerical simulation results of these proposed controllers are presented and compared with those of a classical Proportional Derivative Controller (PID) controller. Different simulation scenarios are presented including nominal case, external disturbance rejection, and robustness under parameters uncertainties.

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Model predictive control of nonlinear hybrid systems with discrete inputs employing a hybrid fuzzy model

Cited by 21 publications

References 22 publications

Model based predictive control for a solar-thermal system

Model based predictive control for a solar-thermal system

Discrete-time model predictive control for head-positioning servomechanism in a dual-stage hard disk drive

Model predictive tracking control for a head-positioning in a Hard-Disk-Drive

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