Sharif Digital Repository

Integrated guidance and control of an elastic flight vehicle based on constrained robust model predictive control is proposed. The design is based on a partial state feedback control law that minimizes a cost function within the framework of linear matrix inequalities. It is shown that the solution of the defined optimization problem stabilizes the nonlinear plant. Nonlinear kinematics and dynamics are taken into account, and internal stability of the closed-loop nonlinear system is guaranteed. The performance and effectiveness of the proposed integrated guidance and control against non-maneuvering and weaving targets are evaluated using computer simulations  

Special attention is given to vertical takeoff and landing air vehicles due to their unique capabilities and versatile missions. The main problem here is control effectiveness at low flight speeds and transition maneuvers because of the inherent instability. RMIT is a small sized tail-sitter ducted fan air vehicle with a particular configuration layout, multiple control surfaces, low weight, and high-speed flight capability. In the current study, a comprehensive nonlinear model is firstly developed for RMIT, followed by a validation process. This model consists of all parts including aerodynamic forces and moments, control surfaces term together with the gravity and driving fan forces.... 

The practicality of robust model predictive control of systems with model uncertainties depends on the time consumed for solving a defined optimization problem. This paper presents a method for the computational complexity reduction in a robust model predictive control. First a scaled state vector is defined such that the objective function contours in the defined optimization problem become vertical or horizontal ellipses or circles, and then the control input is determined at each sampling time as a state feedback that minimizes the infinite horizon objective function by solving some linear matrix inequalities. The simulation results show that the number of iterations to solve the problem... 

The objective of this paper is to expand the concept of hybrid modeling and control in power electronics area. A new precise and non-averaged model of a DC-DC boost converter is developed on the basis of Mixed Logical Dynamical (MLD) systems, and the approach is extended by a new version of such systems which is called as Extended Mixed Logical Dynamical (EMLD) systems in this paper. A Model Predictive Controller (MPC) based on the Mixed Integer Quadratic Programming (MIQP) is designed for the MLD and EMLD models of the DC-DC boost converter considering all possible dynamics in Continuous and Discontinuous Conduction Modes of operations (CCM-DCM). The simulation results show the satisfactory... 

Objective: The objective of this paper is to develop a hierarchical two-level power system load frequency control. Design: At the button level, standard PI controllers are utilized to control area's frequency and tie-line power interchanges. At the higher layer, model predictive control (MPC) is employed as a supervisory controller to determine the optimal set-point for the PI controllers in the lower layer. The proposed supervisory predictive controller computes the optimal set-points such that to coordinate decentralized local controllers. Blocking and coincidence point technology is employed to alleviate the computational effort of the MPC. In order to achieve the best closed loop... 

In this paper, an autonomous orbit control of a satellite in Low Earth Orbit is investigated using model predictive control. The absolute orbit control problem is transformed to a relative orbit control problem in which the desired states of the reference orbit are the orbital elements of a virtual satellite which is not affected by undesirable perturbations. The relative motion is modeled by Gauss's variational equations including J2 and drag perturbations which are the dominant perturbations in Low Earth Orbit. The advantage of using Gauss's variational equations over the Cartesian formulations is that not only the linearization errors are much smaller, but also each orbital element can be... 

In this paper, a wide area measurement, centralized, load frequency control using model predictive control (MPC) is presented for multi-area power systems. A multivariable constrained MPC was used to calculate optimal control actions including generation rate constraints. To alleviate computational effort and to reduce numerical problems, particularly in large prediction horizon, an exponentially weighted functional MPC was employed. Time-based simulation studies were performed on a three-area power system, and the results were then compared with decentralized MPC and classical PI controller. The results show that the proposed MPC scheme offers significantly better performance against load... 

In this paper, a robust multivariable Model based Predictive Control (MPC) is proposed for the solution of load frequency control (LFC) in a multi-area power system. The proposed control scheme is designed to consider multivariable nature of LFC, system uncertainty and generation rate constraint, simultaneously. A constrained MPC is employed to calculate optimal control input including generation rate constraints. Economic allocation of generation is further ensured by modification of the predictive control objective function. To achieve robustness against system uncertainty and variation of parameters, a linear matrix inequality (LMI) based approach is employed. To validate the... 

It is proposed to employ melting rate, heat input, and detaching droplet diameter as controlled variables to control heat and mass transfer to work piece in a gas metal arc welding process. A two-layer architecture with cascade configuration of PI and MPC controllers is implemented to incorporate existing constraints on the process variables, improve transient behavior of the closed-loop responses and reduce interaction level. Computer simulation results are presented to indicate usefulness of the proposed controlled variables selection and applying two-layer control architecture to control heat and mass transfer to work piece  

Controlling inverters with LC output filters in order to achieve a high quality desired output voltage or current is a challenging problem in power electronics. The LC filter and the binary nature of switch state variables increase the difficulty of achieving a single comprehensive model for the system. In this paper, a hybrid model is presented for a three-phase inverter with an LC output filter and a three-phase RL load. Then, the Model Predictive Control (MPC) algorithm is applied to the model and a geometrical approximate method is used to fit the answers to the binary values. Simulation results for a sample system verify the usability of the method and the quality of the answers  

A predictive functional controller based on ARMarkov model structure has been designed to control welding current and arc voltage in a GMAW process. The closed loop system performance is investigated through computer simulations and is compared by those achieved from implementing two commonly used controllers i.e. PI and feedback linearization based PID. The local stability of the closed loop system is analyzed in the presence of uncertainties in the linearized model of the process as well as the control parameters. Finally it is shown that the proposed controller performs like a PI controller along with a pre-filter compensator  

This paper describes an application of intelligence- based predictive scheme to load-frequency control (LFC) in a two-area interconnected power system. In this investigation, at first, a dynamic model of the present system has to be considered and subsequently an efficient control scheme which is organized based on Takagi-Sugeno-Kang (TSK) fuzzy-based scheme and linear generalized predictive control (LGPC) scheme needs to be developed. In the control scheme proposed, frequency deviation versus load electrical power variation could efficiently be dealt with, at each instant of time. In conclusion, in order to validate the effectiveness of the proposed control scheme, the whole of outcomes are... 

Recent system wide disturbances have demonstrated the need for automatic system protection schemes to prevent such cascading disturbances. In this paper, an adaptive under-voltage load shedding scheme using model predictive control is proposed to protect power system against voltage instability. The proposed scheme calculates the criticality of the system based on the measurements of voltage magnitudes and reactive power generations and then in case of voltage instability a model predictive based step-sized load shedding scheme will be triggered. The speed and amount of load shedding steps are adapted to the severity of contingency in the affected region. By devising the grid into the... 

An integrated approach is presented for predicting granule particle size using Partial Correlation (PC) analysis and Artificial Neural Networks (ANNs). In this approach, the proposed model is an abstract form from the ANN model, which intends to reduce model complexity via reducing the dimension of the input set and consequently improving the generalization capability of the model. This study involves comparing the capability of the proposed model in predicting granule particle size with those obtained from ANN and Multi Linear Regression models, with respect to some indicators. The numerical results confirm the superiority of the proposed model over the others in the prediction of granule... 

Developing efficient and appropriate modeling and control techniques for DC-DC converters is of major importance in power electronics area and has attracted much attention from automatic control theory. Since DC-DC converters have a complex hybrid nature, recently several techniques based on hybrid modeling and control have been introduced. These techniques have shown better results as compared with conventional averaging-based schemes with limited modeling and control abilities. But the current works in this field have not considered all possible dynamics of the converters in both continuous and discontinuous current modes (CCM, DCM) of operations. These dynamics are results of controlled... 

Experimental permeability and diffusivity values for CO2 and CH4 through imidazolium-based ionic liquid 1-hexyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide ([hmim][Tf2N]) were determined in the temperature range of 300-320K using temperature correction factor defined in our previous study. According to literature, experimental values of permeability and diffusivity obtained in this study for CO2 in [hmim][Tf2N], showed good agreement with predictive models reported by other researchers. In addition, experimental values of permeability and diffusivity for CH4 in [hmim][Tf2N] as a function of pressure have been reported in this study. Considering the results of present study and... 

The purpose of this paper is to deal with a novel intelligent predictive control scheme using the multiple models strategy with its application to an industrial tubular heat exchanger system. The main idea of the strategy proposed here is to represent the operating environments of the system, which have a wide range of variation in the span of time by several local explicit linear models. In line with this strategy, the well-known linear generalized predictive control (LGPC) schemes are initially designed corresponding to each one of the linear models of the system. After that, the best model of the system and the LGPC control action are precisely identified, at each instant of time, by an...