Sharif Digital Repository

The nonlinear model predictive control (MPC) approach is used to control the coupled translational-rotational motion of an all-thruster spacecraft when one of the actuators fails. In order to model the dynamical response of the spacecraft in MPC, instead of direct integration, a neural network (NN) model is utilized. This model is built of a static NN, followed by a dynamic NN. The static NN is used to find the changes of the mapping of “the demanded forces to the thrusters” and “the real torques/forces produced by the remaining thrusters” after the failure occurrence through online training. In this manner, the effect of failed thruster on the dynamics can be found and the need for... 

This paper presents the design of a new robust model predictive control algorithm for nonlinear systems represented by a linear model with unstructured uncertainty. The linear model is obtained by linearizing the nonlinear system at an operating point and the difference between the nonlinear and linear model is considered as a Lipschitz nonlinear function. The controller is designed for the linear model, which fulfills the stabilization condition for the nonlinear term. Unlike previous studies that have not considered a valid Lipschitz matrix of nonlinear term in the design process, we propose an algorithm in this paper in which it is considered. Therefore, the closed loop stability of the... 

Design, implementation and experimental evaluation of a classic PID, and a modern Generalized Predictive Control (GPC) for an off-pump heart tracking system were carried out. Following the design and simulation analysis of the controllers, experimental evaluation was conducted on the slave robot of SINA tele-operational surgical system. Results revealed that considering the volatile high-frequency/speed pattern of heart motion, the agility of the controlled system is the most influential factor on its performance. With this in mind, unlike the Ziegler-Nichols-based tuned PID with emphasis on steady-state condition, the PID control with more transient behavior showed a superior performance.... 

In this paper, terminal engagement of a maneuvering target and a pursuer is investigated. A heuristic nonlinear model predictive guidance algorithm is presented. Nonlinear kinematics of the pursuer and the target is utilized to formulate the guidance problem. Also, the target maneuver is assumed to be unknown. The proposed heuristic guidance algorithm uses an ant-based optimization algorithm to estimate simultaneously the states of the pursuer, the maneuver of the target, and the optimal guidance commands. Performance of the new guidance algorithm against maneuvering and non-maneuvering targets is evaluated using numerical simulations. Also, the results of the guidance algorithm are compared... 

This study aims to investigate the control of the electromagnetic tethered satellite system using a Model Predictive Control (MPC) scheme. The electromagnetic tethered satellite system is actuated by electromagnetic coils to generate controlling forces. The dynamical model of the system is described in high and low levels of accuracy, which are used to design the control framework. Multiple-Horizon Multiple-Model Predictive Control approach is employed to drive the formation to the desired state. Not only does the presented control law satisfy input and output constraints but also has appropriate characteristics in the sense of optimality. The main benefit of using Multiple-Horizon... 

This paper proposes an enhanced hybrid modular multilevel converter (MMC), which utilizes a combination of half-bridge submodules (HBSM) and at least one full-bridge submodule (FBSM). In the proposed structure, FBSMs work with half of the HBSMs nominal voltage in each arm. Due to the presence of FBSMs, HBSMs switching frequency drops significantly, which reduces the converter power loss compared to half-bridge based MMCs. Furthermore, because of redundant FBSMs, (2N + 1)-modulation can be employed to generate the output voltage without increasing the circulating current, thus, the converter performance is improved notably. Besides, with the redundant FBSMs, the converter can continue its... 

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants... 

This paper considers the problem of high-performance global stabilization of an integrator chain via a bounded control at the presence of input disturbance. While nested saturated feedback (NSF) is known as the most inspiring existing solution in the literature, we shall highlight the inherent shortcomings of this approach which cause a poor performance in terms of convergence rate. Then, a novel dual-mode control scheme combining an improved NSF law with a linear matrix inequality (LMI)-based model predictive controller (MPC) is developed to overcome the weaknesses of pure NSF. By offline calculations, a set of nested robust invariant attraction regions and their attributed feedback gains... 

Attitude control of a satellite having only two reaction wheels is a challenging issue. To address this problem, previously published researches considered some simplifying assumptions on the satellites such as diagonality of the moment of the inertia matrix. On the other hand, in some works, the total angular momentum of the satellite is assumed to be zero. In this paper, a linear time-variant model predictive control (LTV MPC) is designed to control a satellite with two reaction wheels. This control method can be applied to a satellite with a non-diagonal inertial matrix in the presence of external torques, to rotate the satellite toward the desired directions in the space and orbit. The... 

Two-dimensional engagement of a pursuer and a maneuvering target, affected by matched uncertainties, is formulated as a nonlinear differential game. The uncertain guidance problem is converted into a nonlinear model predictive control problem by introducing an appropriate cost function. The objective is to calculate the best guidance commands of the pursuer and the worst possible target maneuvers simultaneously, over a receding horizon. The proposed cost function penalizes the line-of-sight rate, the pursuer acceleration, and the uncertainties. It also rewards the target maneuver. A particle swarm-based dynamic optimization algorithm is developed to solve the nonlinear model predictive... 

This paper presents the design of a new robust model predictive control algorithm for nonlinear systems represented by a linear model with unstructured uncertainty. The linear model is obtained by linearizing the nonlinear system at an operating point and the difference between the nonlinear and linear model is considered as a Lipschitz nonlinear function. The controller is designed for the linear model, which fulfills the stabilization condition for the nonlinear term. Unlike previous studies that have not considered a valid Lipschitz matrix of nonlinear term in the design process, we propose an algorithm in this paper in which it is considered. Therefore, the closed loop stability of the... 

This paper investigates a predictive control strategy for a single-phase PFC rectifier exploiting the Sheppard-Taylor converter. By using this converter, the detuning problem occurring in conventional PFC rectifiers, especially at low input voltages, is avoided. This paper addresses a predictive method for designing the current controller of the Sheppard-Taylor PFC converter. The control law is derived for an ideal circuit as well as a more accurate model of the converter including parasitic elements. Input voltage feed-forward compensation provides sinusoidal input current and a desired output voltage even if the input voltage is distorted. To investigate the dynamic performance of the PFC... 

Due to the excellent properties of the model predictive controllers (MPC) in implementing on nonlinear and time varying systems, utilizing these controllers as Active Queue Management (AQM) strategy is proposed for congestion control of computer networks. However, high computational demand to solve the optimization problem exist in these controllers is a major obstacle when they are applied on fast large-scale constrained systems such as the computer networks. Small signal linearized model of the nonlinear TCP/AQM network is used to design MPC controller and then a neural network is trained to approximate the model predictive control strategy. Using this approach, due to the parallel... 

This paper introduces a cyber-physical system for building automation and control that is developed based on a distributed model predictive control. The implemented distributed method significantly reduces computation overhead with respect to the centralized methods. However, continuous data transfer between subsystems, which are often far from each other, is required when using this method. Information transmission between subsystems is very often subject to the limitations of transmission bandwidth and/or short communication range resulting in significant communication overhead. This causes significant time latency between making measurements and applying control commands, which adversely... 

In this study, a novel three-dimensional continuous-time integrated guidance and control (IGC) scheme is presented. The proposed method is developed on the basis of generalized model predictive control (GMPC) approach and super-twisting extended state observer (STESO). The GMPC is used to generate the optimal closed form control law for the interceptor and the STESO is applied to estimate the maneuvering target lateral accelerations as well as the lumped disturbances. To the aim of IGC design, a six-degrees-of-freedom model based on the interceptor-target kinematics and interceptor dynamics is constructed. Afterward, the GMPC control law formulation for a nonlinear system exposed to... 

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... 

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... 

Several attempts have been made to design suitable controllers for DC-DC converters. However, these designs suffer from model inaccuracy or their inability to desirably function in both continuous and discontinuous current modes. This paper presents a novel switching scheme based on hybrid modeling to control a buck converter using mixed logical dynamical (MLD) methodologies. The proposed method is capable of globally controlling the converter in both continuous and discontinuous current modes of operation by considering all constraints in the physical plant such as maximum inductor current and capacitor voltage limits. Different loads and input voltage disturbances are simulated in MATLAB... 

This research deals with developing an intelligent trajectory tracking control approach for an aircraft in the presence of internal and external disturbances. Internal disturbances including actuators faults, unmodeled dynamics, and model uncertainties as well as the external disturbances such as wind turbulence significantly affect the performance of the common trajectory tracking control approaches. There are several fault-tolerant control approaches in the literature to overcome the effects of specific actuator or sensor faults during the flight. However, trajectory tracking control of an air vehicle in the presence of unexpected faults and simultaneous presence of wind turbulence is... 

Haptic interfaces, a kinesthetic link between a virtual environment and a human operator play a pivotal role in the reproduction of realistic haptic force feedback of the virtual reality-based simulators. Since most of the practical control theories are model-based, the identification of the robot’s dynamics, for precise modeling of the system dynamics, is a process of high significance and usage. This research addresses dynamic characterization, performance issues, and structural stability, associated with a parallel haptic device interaction with an admittance type virtual environment. In this regard, considering the Lion identification scheme, we characterized the dynamics of a robot...