Showing 1-25 of 14,016 resultsfor "Index Terms":Nonlinear Model Predictive Control
"Index Terms":Nonlinear Model Predictive Control
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A Nonlinear Model Predictive Control Based on Least Squares Support Vector Machines Narx Model
2007 International Conference on Machine Learning and Cybernetics
Year: 2007 | Conference Paper |
Cited by: Papers (4)
In the domain of industry process control, the model identification and predictive control of nonlinear systems are always difficult problems. To solve the problems, an identification method based on least squares support vector machines for function approximation is utilized to identify a nonlinear autoregressive external input (NARX) model. The NARX model is then used to construct a novel nonlin...Show More
Fuzzy Model-Based Predictive Control applied to multivariable level control of multi tank system
2010 5th IEEE International Conference Intelligent Systems
Year: 2010 | Conference Paper |
Cited by: Papers (1)
In this study issues related to applicability of Model-Based Predictive Control (MBPC) to nonlinear and complex processes are addressed. A tank system is taken as an exemplary process, and its prediction model is used for control purposes. Obtained results are applied for level control of a tank process. A Takagi-Sugeno type fuzzy neural network is used to model the nonlinear system. The obtained ...Show More
Generalized Predictive Control for A Class of Bilinear Hammerstein Model
2006 9th International Conference on Control, Automation, Robotics and Vision
Year: 2006 | Conference Paper |
A generalized predictive control strategy aimed at a kind of bilinear Hammerstein model is proposed in the paper. The strategy avoids solving the high level equation by improving the static nonlinearity portion in the bilinear Hammerstein model and adopts the generalized predictive control algorithm in the bilinear portion to avoid the heavy computation of generic nonlinear optimization, thus the ...Show More
Survey on nonlinear reconfigurable flight control
An overview on nonlinear reconfigurable flight control approaches that have been demonstrated in flight-test or high-fidelity simulation is presented. Various approaches for reconfigurable flight control systems are considered, including nonlinear dynamic inversion, parameter identification and neural network technologies, backstepping and model predictive control approaches. The recent research w...Show More
Research on nonlinear model predictive control technology for ship dynamic positioning system
2012 IEEE International Conference on Automation and Logistics
Year: 2012 | Conference Paper |
Cited by: Papers (13)
As an excellent technology, model predictive control has become a more pleasing and popular research topic. In order to solve the higher precision control problem for the nolinear, uncertained and strong-coupled ship dynamic positioning system, this paper introduces the nonlinear model predictive control (NMPC) technology into this system based on lots of foregoing work about its linear applicatio...Show More
A model free predictive control is persented. This method does not need modeling in the predictive control. The eigenvector of generally model is identified and predicted by multi-layer recursive method. The model free predictive control law has advantage as well as model free control.Show More
A Direct Adaptive Predictive Functional Control Based on T-S Fuzzy Model
Sixth International Conference on Intelligent Systems Design and Applications
Year: 2006 | Conference Paper |
Cited by: Papers (1)
An adaptive predictive functional control algorithm based on T-S fuzzy model for discrete-time nonlinear systems is proposed. In this algorithm the consequent parameters of T-S fuzzy model are identified by the weighting recursive least square method. Parameters of the fuzzy model are used to directly recursively compute the model prediction output, and needn't solve Diophantine equations. Analysi...Show More
Nonlinear predictive adaptive controllers for nonlinear systems
2004 IEEE International Symposium on Industrial Electronics
Year: 2004 | Conference Paper |
Cited by: Papers (6)
A new nonlinear predictive adaptive control is presented in this paper. The presence of parameter uncertainty in the system inherently causes bad control behaviour. Hence, an identification algorithm based on reference model is used in parallel with the predictive control design giving the new proposed algorithm. This algorithm is successfully demonstrated by numerical examples. The experiments pr...Show More
Robust Predictive Control Strategy Applied for Propofol Dosing Using BIS as a Controlled Variable During Anesthesia
Clara M. Ionescu $^*$;Robin De Keyser;Bismark Claure Torrico;Tom De Smet;Michel MRF Struys;Julio E. Normey-Rico
This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The robustness properties of the predictive controller are evaluated with respect to inter- and intrapatient v...Show More
Nonlinear predictive control for continuous microalgae cultivation process in a photobioreactor
2008 10th International Conference on Control, Automation, Robotics and Vision
Year: 2008 | Conference Paper |
Cited by: Papers (10)
This paper presents a nonlinear predictive control (NMPC) strategy applied to the continuous microalgae cultivation process in a closed photobioreactor. The photo-bioreactor system is programmed to operate in a constant biomass density mode, in order to maintain the culture at the optimal population density and sustain high biomass production levels. This objective is achieved by regulating the bi...Show More
Aiming at ball and beam system—a typical nonlinear control plant, establish its affine nonlinear model, based on the nonlinear model predictive control method, some amelioration is made, combined with the networked predictive control scheme, a networked nonlinear model predictive control method is introduced, which can compensate the network induced time delay and the packet loss, and used in the ...Show More
Sequential Distributed Model Predictive Control for State-Dependent Nonlinear Systems
2013 IEEE International Conference on Systems, Man, and Cybernetics
Year: 2013 | Conference Paper |
In this paper, sequential nonlinear Distributed Model Predictive Control (DMPC) algorithms for large-scale systems that can handle constraints are proposed. The proposed algorithms are based on nonlinear MPC strategy, which uses a state-dependent nonlinear model to avoid the complexity of the nonlinear programming (NLP) problem. In this distributed framework, local MPCs solve convex optimization p...Show More
Parallel distributed neuro-fuzzy model predictive controller applied to a hydro turbine generator
2008 4th International IEEE Conference Intelligent Systems
Year: 2008 | Conference Paper |
Model predictive control (MPC) has been considered as the most important development in the area of process control in the last two decades. This paper addresses the issue of controlling a nonlinear plant by the use of the nonlinear model predictive control formulation. To handle the nonlinearities, a Takagi-Sugeno neuro-fuzzy model is suggested as a means to model the plant with nonlinearities de...Show More
In this paper, we introduce the general purpose optimal control problem solver RIOTS_95, Matlab toolbox, as a solver for general linear and nonlinear model predictive control (MPC) problems. This optimization toolbox is designed to solve a wide variety of optimal control problems and therefore constitutes a good candidate as a optimization solver in the MPC framework. The illustrative example of a...Show More
The nonlinear model predictive control needs to solve a two-point boundary-value problem (TP-BVP) at every sample time based on the receding horizon control strategy. However, solving a nonlinear algebraic equation for the TP-BVP requires high computational load, so computing the cotrol law in real-time is a significant issue on the nonlinear model prective control. This paper, therefore, proposes...Show More
New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process
In this letter, a novel nonlinear neural network (NN) predictive control strategy based on the new tent-map chaotic particle swarm optimization (TCPSO) is presented. The TCPSO incorporating tent-map chaos, which can avoid trapping to local minima and improve the searching performance of standard particle swarm optimization (PSO), is applied to perform the nonlinear optimization to enhance the conv...Show More
This paper gives stability analysis of the nonlinear predictive control strategy based on the off-line identified RBF-ARX model which is a pseudo-linear time-varying ARX model with system working-point dependent Gaussian RBF neural network style coefficients. The predictive controller doesn't require on-line parameter estimation; it may be applied to a class of smooth nonlinear processes whose wor...Show More
Robust Nonlinear Predictive Control Applied to a Solar Collector Field in a Solar Desalination Plant
Bismark Claure Torrico;Lidia Roca;Julio Elias Normey-Rico;Jose Luis Guzman;Luis Yebra
This brief presents the application of a robust nonlinear predictive controller to the distributed collector field of a solar desalination plant. The main purpose of the controller is to manipulate the water flow rate to maintain the collector outlet-inlet temperature gradient constant in spite of disturbances. The controller uses a robust dead-time compensation structure and a nonlinear model pre...Show More
Nonlinear modeling and predictive control of the four-tank system
The 2nd International Conference on Software Engineering and Data Mining
Year: 2010 | Conference Paper |
The four-tank level control system is a typical control system with nonlinear, coupling and time delays characteristics, and can be used in simulation of multivariate industrial system with nonlinear, time-varying, coupling characteristics, on which the effects of the applications of various control theories on complicated systems can be tested. This paper has analyzed four-tank level control syst...Show More
Fuzzy model predictive control based on modified fuzzy relational model
2013 13th Iranian Conference on Fuzzy Systems (IFSC)
Year: 2013 | Conference Paper |
Cited by: Papers (3)
In this paper, a new approach to the fuzzy model predictive control (FMPC) is presented which is based on the modified fuzzy relational model (MFRM) introduced by Aghili and Menhaj. A comparison with PID and SMC control is provided to study the proficiency of the proposed FMPC. In this regard, DC-DC converter which has highly nonlinear characteristics is considered as the plant. First, the applica...Show More
Stabilization control of the tethered satellite system using nonlinear model predictive control
Proceedings of the 33rd Chinese Control Conference
Year: 2014 | Conference Paper |
Cited by: Papers (2)
The tethered satellite system (TSS) has some potential applications in space interferometry and remote sensing. It is advantageous to traditional free satellite formations as far as relative station-keeping is concerned. However, the dynamics of the TSS in the restricted three-body problem is extraordinary complicated, and the collinear equilibrium points are unstable. To stabilize and keep the TS...Show More
Three nonlinear predictive control algorithms were applied in simulation to a MIMO waste-water treatment process. Two algorithms are based on instantaneous linearization of the nonlinear prediction model and one is based on a branch-and-bound search technique. The prediction model employed is a fuzzy model of the Takagi-Sugeno type. The performance of the controllers is compared in terms of setpoi...Show More
To solve nonlinear multi-objective dynamic optimization problem originates from process control, a modified frame of dynamic modular multivariable controller based on lexicographic optimization strategy is proposed, which had control modules with different priorities. A modified one-step fast nonlinear predictive control algorithm is used to solve control input in every module. Strategy of the sel...Show More
Predictive control of nonlinear Hammerstein systems and application to PH processes
2003 European Control Conference (ECC)
Year: 2003 | Conference Paper |
In this paper, a nonlinear predictive control algorithm based on discrete-time model is developed for nonlinear Hammerstein systems, which is known as NonLinear Hammerstein Predictive Control (NLHPC) algorithm. Following the predictive control strategy, this algorithm uses a Hammerstein model for control prediction. Analysis on the algorithm shows that it not only has good stability and strong rob...Show More
Predictive control using feedback linearization based on dynamic neural models
2007 IEEE International Conference on Systems, Man and Cybernetics
Year: 2007 | Conference Paper |
Cited by: Papers (4)
This paper presents a hybrid control strategy inte grating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an impor tant nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been...Show More
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