Sharif Digital Repository

In this study, the guidance and control problem of a single-channel spinning missile is investigated. The missile utilizes a single ON-OFF actuator to drive a pair of control surfaces (e.g. elevators) and consequently to perform all required lateral maneouvers. An approximated linear response of the so-called non-rotating frame to ON-OFF input, applied to the rotating frame, is derived using the multiple-input describing function technique. It is shown that there is a relationship between the response of the non-rotating frame and that of the equivalent non-rotating body. It is also shown that the two-channel flight controller, designed for the equivalent non-rotating body, can be reduced to... 

In-motion alignment of a strapdown inertial navigation system, during slow maneuvers, is studied. Terrestrial velocity is fed back to the navigation system to estimate and compensate for the navigation errors. Observability of the errors is analyzed since the integrated navigation system is not fully observable. Then, the accuracy bounds of the navigation system in different motion scenarios are obtained analytically. Also, in order to minimize the errors of the navigation system, special maneuvers are designed based on the analytical derivations. The analytical results, obtained using the linearized error model, are verified through nonlinear simulation of different maneuvering and... 

In this research, Model Aided Inertial Navigation (MAIN) is used during the automatic landing of an Unmanned Aerial Vehicle (UAV). A new MAIN algorithm is proposed which is fast and accurate enough to be used in this phase. In this algorithm, the six Degree of Freedom (6DoF) flight simulation of the UAV is integrated with the Inertial Navigation System (INS) such that the 6DoF model acts as an aiding system for the INS. In the last parts of the landing phase, when the UAV flies in proximity of the ground surface, the proposed integrated navigation system can estimate the altitude of UAV utilizing the “ground effect” phenomenon. Therefore, the method does not have the drawbacks of active... 

In this chapter, a new class of filters based on swarm intelligence is introduced for nonlinear systems state estimation. As a subset of heuristic filters, swarm filters formulate a nonlinear system state estimation problem as a stochastic dynamic optimization problem and utilize swarm intelligence techniques such as particle swarm optimization and ant colony optimization to find and track the best estimate. As a subset of nonlinear filters, swarm filters can successfully compete with well-known nonlinear filters such as unscented Kalman filter, etc  

This paper introduces a new filter for nonlinear systems state estimation. The new filter formulates the state estimation problem as a stochastic dynamic optimization problem and utilizes a new stochastic method based on simplex technique to find and track the best estimation. The vertices of the simplex search the state space dynamically in a similar scheme to the optimization algorithm, known as Nelder-Mead simplex. The parameters of the proposed filter are tuned, using an information visualization technique to identify the optimal region of the parameters space. The visualization is performed using the concept of parallel coordinates. The proposed filter is applied to estimate the state... 

Purpose - The purpose of this paper is to propose a supervisory command-to-line-of-sight guidance law with lead angle which keeps the missile flight within the tracking beam. Design/methodology/approach - A nonlinear supervisory controller is designed and coupled with the main sliding mode controller in the form of an additional control signal. The supervisory control signal is activated when the beam angle constraint goes to be violated. Initially a supervisory controller is designed using nonlinear control theory. Subsequently the main tracking controller is designed using sliding mode approach which forces the missile to fly along the desired line-of-sight. The stability of the... 

This paper proposes a supervisory Command to Line-of-Sight (CLOS) guidance law with lead angle. The proposed scheme guarantees the missile to fly within the beam, when there is a beam angle constraint. In this regard a supervisory controller is coupled with a main Sliding Mode Controller (SMC). The supervisory control signal is activated when the beam constraint goes to be violated. First the supervisory controller is designed based on a recent idea on how the states of a nonlinear system with local stable controller can be controlled to stay within the domain of attraction. Then a sliding mode controller is designed, as the main tracking controller, to force the missile to fly along the... 

This article aims to compensate the velocity and position errors that exist when the star sensor starts to work in a strapdown inertial navigation system aided by celestial navigation. These systems are integrated via unscented Kalman filter to estimate the current attitude and the gyros fixed bias, precisely. Since an accurate integration is desired, the nonlinear attitude equations are utilized in filter and these equations are propagated through a precise discretization method. Then, implementing the back-propagation and smoothing techniques, the initial attitude and the accelerometers fixed bias are also estimated. Finally, carrying out a parallel navigation, the velocity and position... 

The problem of aerodynamic shape optimization of unguided projectiles has been investigated. Two stochastic optimization methods have been applied to solve the problem. These include a Genetic Algorithm (GA) and the recently developed Continuous Ant Colony System (CACS), which is based on the well-known Ant Colony Optimization meta-heuristic. The objective function is defined as the summation of normal force coefficients over a set of given flight conditions. An engineering code (EC) is used to calculate the normal force coefficients over the flight conditions. The obtained results of CACS+EC are compared with those of GA+EC, as well as the results of a previous work (GA +AeroDesign). The... 

There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a special emphasis on heuristic iterative optimization methods employing modern evolutionary and swarm... 

The well-known ant colony optimization meta-heuristic is applied to design a new command to line-of-sight guidance law. In this regard, the lately developed continuous ant colony system is used to optimize the parameters of a pre-constructed fuzzy sliding mode controller. The performance of the resulting guidance law is evaluated at different engagement scenarios. © Springer-Verlag Berlin Heidelberg 2005  

A new heuristic filter, called Continuous Ant Colony Filter, is proposed for non-linear systems state estimation. The new filter formulates the states estimation problem as a stochastic dynamic optimization problem and utilizes a colony of ants to find and track the best estimation. The ants search the state space dynamically in a similar scheme to the optimization algorithm, known as Continuous Ant Colony System. The performance of the new filter is evaluated for a nonlinear benchmark and the results are compared with those of Extended Kalman Filter and Particle Filter, showing improvements in terms of estimation accuracy  

A new heuristic controller, called Continuous Ant Colony Controller, is proposed for nonlinear stochastic systems. The new controller formulates the states estimation and model predictive control problems as a single stochastic dynamic optimization problem and utilizes a colony of virtual ants to find and track the best state estimation and the best control signal. For this purpose an augmented state space is defined. An integrated cost function is also defined to evaluate the ants within the state space. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar... 

A new adaptive real-coded memetic algorithm has been developed for continuous optimization problems. The proposed algorithm utilizes an adaptive variant of Continuous Ant Colony System for local search. Here new adaptive strategies are utilized for online tuning of the number of local search steps and the width of the search interval over each dimension of the search space. A new crossover scheme is also developed and utilized. The new algorithm has been examined with CEC 2005 benchmarks and compared with three other state of the art works in the field. The comparisons have showed relatively better results. © 2009 IEEE  

In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms. © 2017 IEEE  

A heuristic predictive line-of-sight (LOS) guidance law is introduced to intercept a high-speed maneuvering target. A combination of continuous ant colony system and tabu search optimization algorithms is proposed to generate the optimal predictive commands of LOS guidance law. Prediction is driven by the previous positions of the target to estimate the next positions of it. Thus, the guidance system is continually solving a dynamic optimization problem in order to determine the acceleration commands by minimizing a cost function subject to actuators saturation. This innovation distinguishes the proposed guidance law from the classic LOS guidance, described by a simple relation between the... 

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... 

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... 

In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear... 

In this paper, a new nonlinear heuristic filter based on the hybridization of an extended Kalman filter and an ant colony estimator is proposed to estimate the states of a nonlinear system. In this filter, a group of virtual ants searches the state space stochastically and dynamically to find and track the best state estimation while the position of each ant is updated at the measurement time using the extended Kalman filter. The performance of the proposed filter is compared with well-known heuristic filters using a nonlinear benchmark problem. The statistical results show that this algorithm is able to provide promising and competitive results. Then, the new filter is tested on a nonlinear...