Search

The miss distance analysis of the first-order explicit guidance law (EGL) is carried out using linearized equation of motion in the normalized form in order to obtain normalized miss distance curves. The initial heading error, constant target, acceleration limit, radome refraction error, and fifth-order binomial control system are considered. Moreover, body rate feedback is added to the explicit guidance law as a well-known classical compensation method of the radome effect as in proportional navigation. The analysis is performed for different values of the power of the alpha function, defined as the time decrease rate of the zero-effort miss to unit control input. As a special case, the EGL with unit power gives the first-order optimal guidance strategy for minimizing the integral of the square of the commanded acceleration during the total flight time. For the performance/stability analysis, the rms miss distance versus turning rate time constant and radome slope can be plotted for different values of the power of alpha function.

Trajectory optimization is a familiar method for most of re-entry and Re-usable vehicles. This is because of the ability to include almost all of the problem constraints without facing restrictions such as time & Calculation issues. Adding or removing constraints in trajectory optimization problem has significant effects on overall optimization performance which even can upgrade the method to an on-line process. Most of optimization Algorithms such as nonlinear-programming need an initial guess and are also sensitive to it. Hence in this research management of initial guess is done to remove some constraints from optimization problem and transfer them to initial phase. Accordingly an effort is conducted through using a classic guidance method to satisfy constraints of distance error and angle of impact command. The output of guidance initial guess is then fed to the optimization problem. 6Dof Simulation results show the increase of optimization performance via reduced number of iterations and Optimization time and increased solution accuracy.

The atmospheric reentry phase is one of the most important mission steps in space missions, therefore, the guidance and control of reentry vehicles in this phase of mission is important. In this article, a reentry vehicle guidance algorithm is proposed which has suitable robustness in the presence of initial reentry parameters uncertainty. To use any conductive method, first the motion equations must be obtained. In this paper, quadratic nonlinear control method is used to guide the vehicle. In this regard, the equations of motion of reentry vehicles are developed in form of state space and the system and control matrices depending on the state and control variables are extracted. In this article, it is tried to minimize the landing errors at terminal point using Nonlinear Quadratic Tracking (NQT) and chasing a reference trajectory. In order to define a trajectory with different initial states using evolutionary genetic algorithm with changes in weighting matrices Q and R, it is tried to reduce the errors of landing at terminal point. Monte Carlo analysis is used to evaluate the performance of the proposed algorithm. According to the results, the proposed algorithm can reduce the errors more than 90% in the presence of reentry initial parameter uncertainties.

The atmospheric re-entry phase is one of the most significantmission steps in the space missions;hence, theguidance and control of reentry vehicles in this phase of mission is important. In this article, a reentry vehicle guidance algorithm has been proposed which has suitable robustness in the presence of initial reentry parameters uncertainties. Here,it has been tried to minimize the landing errors at terminal point using Nonlinear Quadratic Tracking (NQT) and chasing a reference trajectory. In order to define several trajectories with different initial states using evolutionary genetic algorithm with changes in weighting matrices Q and R, it hasbeen tried to reduce the errors of landing at terminal point. The reentry position of the reentry vehicles may be different from the desired ones with respect to several events. In this situation, reentry vehicles start to move in a new trajectory which is not suitable. Therefore, the reentry vehicles should be guided to come back into the desired trajectory or a new optimum trajectory needs to be redesignedto have the same target position on the ground. To do this, we need optimum weighting matrices R and Q for every new trajectory. In this article, this problem has been resolved using partial least squares regression; meanwhile, obtaining the optimal matrices by genetic algorithms needed many times. Also,it is shown that using this method, in the presence of reentry uncertainties, weighting matrices for each new initial condition hasbeen quickly derived. Additionaly,through the matrices obtained and the nonlinear quadratic tracking controller, reentry vehicle was directedto the target with a good accuracy. The Monte Carlo analysis has been used to evaluate the performance of the proposed algoritm. According to the results, the proposed algoritm has a suitable accuracy level and it can generate the online optimum trajectory.

An optimal explicit guidance law that maximizes terminal velocity is developed for the reentry of a vehicle to a fixed target. The equations of motion are reduced with differential flatness approach and acceleration commands are related to the parameters of trajectory. An optimal trajectory is determined by solving a real-coded genetic algorithm. For online trajectory generation, optimal trajectory is approximated. The approximated trajectory is compared with the pure proportional navigation and genetic algorithm solutions. The near optimal terminal velocity solution compares very well with these solutions. The approach robustness is examined by Monte Carlo simulation. Other advantages such as trajectory representation with minimum parameters, applicability to any reentry vehicle configuration and any control scheme, and Time-to-Go independency make this guidance approach more favorable.

In this paper, a modified proportional navigation (PN) with weighted combination of linear acceleration and line-of-sight (LOS) acceleration feedback is suggested. For this purpose, a comprehensive miss distance analysis is carried out for PN with linear acceletation feedback and PN with LOS acceleration feedback using a fifth-order binomial guidance and control system. The miss distance (MD) due to initial heading error, target acceleration, and seeker noise is separately analysed. As a special case, a modified PN with acceleration feedback using variable gains is suggested based on MD analysis for infra red seekers. The comparison of PN strategies is carried out using an equivalent effective navigation ratio, defined by using LOS rate profile solution. In addition, the first-order optimal guidance law is converted into PN with PD block with variable gains.