R. Thangavelu

Bio: R. Thangavelu is an academic researcher from Indian Institute of Science. The author has contributed to research in topics: Nonlinear system & Population. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

A differential evolution tuned optimal guidance law

Abstract: The optimal guidance law (OGL) is based on the assumptions of linear kinematical model of missile guidance and unconstrained control in a linear quadratic regulator formulation. These are strong assumptions that are rarely satisfied in practice; the kinematics of the missile-target engagement is highly nonlinear, and infinite lateral acceleration -which is the control -is a physical impossibility. As a consequence, the application of OGL to the actual nonlinear model shows a large miss distance except for a limited range where the linearity assumption is valid and the control needed is not so large as to hit the constraint boundary. This paper proposes a novel improvisation of the OGL using differential evolution (DE) that compensates the effects of these assumptions and increases the range of validity of OGL. Treating the missile-target kinematics as a black box in which nothing is known except the input and the output, the output is optimized for variations in input using DE. The nominal values of the input variables used for the OGL are used to seed the initial population of trial solutions.

On an Optimized Fuzzy Supervized Multiphase Guidance Law

Abstract: Proportional navigation PN is an effective guidance law in chasing a constant speed target. However, intercepting a maneuvering target requires extra provisions to contain target acceleration. This is observed in the next generation of guidance laws, namely augmented proportional navigation APN and the optimal guidance law OGL. The other advanced guidance method is integral sliding mode ISM, which exhibits superb low miss distance MD, but unfortunately at the cost of disappointing effective lateral acceleration Ceff, higher than the modest level that APN and OGL demand. Reducing both MD and Ceff can be achieved using a multiphase algorithm. A setup of APN, OGL and ISM is proposed to integrate the strength of each and overcome their weaknesses. Hybrid algorithm smooth management is conducted by an optimally tuned fuzzy supervisory controller. The results indicate that in facing a constant acceleration target, an APN-ISM twin yields the best results, while in case of a constant jerk target an APN-OGL-ISM triplet renders excellent interception. Miss distance as low as 0.012m can be achieved with significantly lower control effort than required by ISM alone. The simulations results confirm the conclusions and illustrate the capacity of the algorithm in combating maneuvering targets.

An online implementable differential evolution tuned optimal guidance law

Abstract: This paper proposes a novel application of differential evolution to solve a difficult dynamic optimisation or optimal control problem. The miss distance in a missile-target engagement is minimised using differential evolution. The difficulty of solving it by existing conventional techniques in optimal control theory is caused by the nonlinearity of the dynamic constraint equation, inequality constraint on the control input and inequality constraint on another parameter that enters problem indirectly.The optimal control problem of finding the minimum miss distance has an analytical solution subject to several simplifying assumptions. In the approach proposed in this paper, the initial population is generated around the seed value given by this analytical solution. Thereafter, the algorithm progresses to an acceptable final solution within a few generations, satisfying the constraints at every iteration. Since this solution or the control input has to be obtained in real time to be of any use in practice, the feasibility of online implementation is also illustrated.