Distributed Optimization for Model Predictive Control of Linear Dynamic Networks With Control-Input and Output Constraints

2011 ◽  
Vol 8 (1) ◽  
pp. 233-242 ◽  
Author(s):  
Eduardo Camponogara ◽  
Helton F. Scherer
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Distributed Optimization ◽  
Dynamic Networks ◽  
Control Input ◽  
Linear Dynamic ◽  
Input And Output ◽  
Output Constraints

Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation

1998 ◽  
Author(s):  
B. G. Vroemen ◽  
H. A. van Essen ◽  
A. A. van Steenhoven ◽  
J. J. Kok
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Prediction ◽  
Input And Output ◽  
Output Constraints ◽  
Controller Performance ◽  
Standard Linear ◽  
Turbine Installation

The feasibility of Model Predictive Control (MPC) applied to a laboratory gas turbine installation is investigated. MPC explicitly incorporates (input- and output-) constraints in its optimizations, which explains the choice for this computationally demanding control strategy. Strong nonlinearities, displayed by the gas turbine installation, cannot always be handled adequately by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combination of these. We implement these methods, using a nonlinear model of the installation, and compare them to linear MPC. It is shown that controller performance can be improved, without increasing controller execution-time excessively.

Model Predictive Control of an Autonomous Blimp with Input and Output Constraints

2006 ◽  
Author(s):  
Hiroaki Fukushima ◽  
Ryosuke Saito ◽  
Fumitoshi Matsuno ◽  
Yasushi Hada ◽  
Kuniaki Kawabata ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Input And Output ◽  
Output Constraints

Model predictive control of an autonomous blimp with input and output constraints

2006 ◽  
Author(s):  
Hiroaki Fukushima ◽  
Ryosuke Saito ◽  
Fumitoshi Matsuno ◽  
Yasushi Hada ◽  
Kuniaki Kawabata ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Input And Output ◽  
Output Constraints

Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation

1999 ◽  
Vol 121 (4) ◽  
pp. 629-634 ◽  
Author(s):  
B. G. Vroemen ◽  
H. A. van Essen ◽  
A. A. van Steenhoven ◽  
J. J. Kok
Keyword(s):  
Model Predictive Control ◽  
Gas Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Prediction ◽  
Input And Output ◽  
Output Constraints ◽  
Controller Performance ◽  
Standard Linear ◽  
Turbine Installation

The feasibility of model predictive control (MPC) applied to a laboratory gas turbine installation is investigated. MPC explicitly incorporates (input and output) constraints in its optimizations, which explains the choice for this computationally demanding control strategy. Strong nonlinearities, displayed by the gas turbine installation, cannot always be handled adequately by standard linear MPC. Therefore, we resort to nonlinear methods, based on successive linearization and nonlinear prediction as well as the combination of these. We implement these methods, using a nonlinear model of the installation, and compare them to linear MPC. It is shown that controller performance can be improved, without increasing controller execution-time excessively.

ON MODEL PREDICTIVE CONTROL OF QUASI-RESONANT CONVERTERS

2009 ◽  
Vol 18 (07) ◽  
pp. 1167-1183 ◽  
Author(s):  
FARZAD TAHAMI ◽  
MEHDI EBAD
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Linear Models ◽  
Control Strategies ◽  
Sampling Interval ◽  
Control Synthesis ◽  
Resonant Converters ◽  
Control Input ◽  
Model Approximation ◽  
Dynamic Matrix

In this paper, different model predictive control synthesis frameworks are examined for DC–DC quasi-resonant converters in order to achieve stability and desired performance. The performances of model predictive control strategies which make use of different forms of linearized models are compared. These linear models are ranging from a simple fixed model, linearized about a reference steady state to a weighted sum of different local models called multi model predictive control. A more complicated choice is represented by the extended dynamic matrix control in which the control input is determined based on the local linear model approximation of the system that is updated during each sampling interval, by making use of a nonlinear model. In this paper, by using and comparing these methods, a new control scheme for quasi-resonant converters is described. The proposed control strategy is applied to a typical half-wave zero-current switching QRC. Simulation results show an excellent transient response and a good tracking for a wide operating range and uncertainties in modeling.

Settling Control of the Triple-Stage Hard Disk Drives Using Robust Output Feedback Model Predictive Control

2016 ◽  
Author(s):  
Huy Nguyen ◽  
Omid Bagherieh ◽  
Roberto Horowitz
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Output Feedback ◽  
Frequency Control ◽  
Hard Disk Drives ◽  
Low Frequency ◽  
Hard Disk ◽  
Control Input ◽  
Feedback Model ◽  
Actuator Constraints

Track settling control for a hard disk drive with three actuators has been considered. The objective is to settle the read/write head on a specific track by following the minimum jerk trajectory. Robust output feedback model predictive control methodology has been utilized for the control design which can satisfy actuator constraints in the presence of noises and disturbances in the system. The controller is designed based on a low order model of the system and has been applied to a higher order plant in order to consider the model mismatch at high frequencies. Since the settling control generally requires a relatively low frequency control input, simulation result shows that the head can be settled on the desired track with 10 percent of track pitch accuracy while satisfying actuator constraints.

Performance of a Wave-Energy-Converter Array Operating Under Model-Predictive Control Based on a Convex Formulation

2018 ◽  
Author(s):  
Qian Zhong ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Reactive Power ◽  
Optimal Solution ◽  
Energy Output ◽  
Control Input ◽  
Energy Extraction ◽  
Power Performance ◽  
Point Absorbers

Economics decision drives the operation of ocean-wave energy converters (WEC) to be in a “farm mode”. Control strategy developed for a WEC array will be of high importance for improving the aggregate energy extraction efficiency of the whole system. Model-predictive control (MPC) has shown its strong potential in maximizing the energy output in devices with hard constraints on operation states and machinery inputs (See Ref. [1–3]). Computational demands for using MPC to control an array in real time can be prohibitive. In this paper, we formulate the MPC to control an array of heaving point absorbers, by recasting the optimization problem for energy extraction into a convex Quadratic Programming (QP) problem, the solution of which can be carried out very efficiently. Large slew rates are to be penalized, which can also guarantee the convexity of the QP and improve the computational efficiency for achieving the optimal solution. Constraints on both the states and the control input can be accommodated in this MPC method. Full hydro-dynamic interference effects among the WEC array components are taken into account using the theory developed in [4]. Demonstrative results of the application are presented for arrays of two, three, and four point absorbers operating at different incident-wave angles. Effects of the interacting waves on power performance of the array under the new MPC control are investigated, with simulations conducted in both regular and irregular seas. Heaving motions of individual devices at their optimal conditions are shown. Also presented is the reactive power required by the power takeoff (PTO) system of the array to achieve optimality. We are pleased to contribute this article in celebration of our collegiality with Professor Bernard Molin on the occasion of his honoring symposium.

scholarly journals Convergence Guaranteed Nonlinear Constraint Model Predictive Control via I/O Linearization

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Xiaobing Kong ◽  
Xiangjie Liu ◽  
Xiuming Yao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Output Feedback ◽  
Feedback Linearization ◽  
Optimal Solution ◽  
Quadratic Program ◽  
Control Input ◽  
Input Output ◽  
Nonlinear Constraint ◽  
Constraint Model

Constituting reliable optimal solution is a key issue for the nonlinear constrained model predictive control. Input-output feedback linearization is a popular method in nonlinear control. By using an input-output feedback linearizing controller, the original linear input constraints will change to nonlinear constraints and sometimes the constraints are state dependent. This paper presents an iterative quadratic program (IQP) routine on the continuous-time system. To guarantee its convergence, another iterative approach is incorporated. The proposed algorithm can reach a feasible solution over the entire prediction horizon. Simulation results on both a numerical example and the continuous stirred tank reactors (CSTR) demonstrate the effectiveness of the proposed method.

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