REDUCTION TECHNIQUE
Gaurav Kumar, Avinash Pandey, Ashutosh Dubey Department of Instrumentation and Control Galgotias College of Engineering and Technology, Greater Noida Abstract: A very great amount of thought has been given to the selection of this topic. Basically, here we are concerned with the problem of dealing with higher order ricatti equation. The amount of work and the number of variables used in various calculations in a power plant is very large and the objective is to minimize the higher order model into a lower order model as the lower order model is easy to work with. The next difficulty is to design the optimal controller for the ricatti equation of higher model. These two problems are the main concern of our topic. The idea for the solution of these problems is the use of Routh Pade Approximation technique. It is a technique that reduces the order of the equation in a very easy and work-friendly way. The Pade Approximation reduces the higher order model into a lower order model and then we design a suboptimal controller for this lower order model. These two things, namely, the order reduction using Routh Pade Approximation and the design of the suboptimal controller are two different things and they have been taken care of separately. As we know the calculations carried out at a place like power plants are massive and the amount of time taken is very large. This whole process makes the calculations easier and there is a great deal in reduction of the total time consumed. The results, as we expect will be same. The result without reducing the model and using the suboptimal controller comes out to be the same as after reducing the model and then designing the suboptimal controller. INTRODUCTION The design of an optimal control law for large scale system becomes quite complex due to the problem of
References: Al Brekht, E. G. (1961). On the optimal stabilization of nonlinear systems. J. Appl. Math. Mech. 25, 1254–1265. Beard, R. W., G. N. Saridis and J. T. Wen (1997). Galerkin approximations of the generalized Hamilton-Jacobi-Bellman equation. Automatica 33(12), 2159–2177. Beard, R. W., G. N. Saridis and J. T. Wen (1998). Approximate solutions to the time-invariant Hamilton-Jacobi-Bellman equation. J. Opt. Theory and Appl. 96(3), 589–626. Beeler, S. C., H. T. Tran and H. T. Banks (2000). Feedback control methodologies for nonlinear systems. Journal on Optimization Theory and Applications 107(1), 1–33. Chen, H., A. Kremling and F. Allg¨ower (1995). Nonlinear predictive control of a benchmark CSTR. In: Proc. 3rd European Control Conference ECC’95. Rome. pp. 3247–3252. Garrard, W. L. (1972). Suboptimal feedback control for nonlinear systems. Automatica 8(2), 219–221. Garrard, W. L. and J. M. Jordan (1977). Design of nonlinear automatic flight control systems. Automatica 13(5), 497–505. Ito, K. and J.D. Schroeter (1998). Reduced order feedback synthesis for viscous incompressible flows. Technical report. Center for Research in Scientific Computation, North Carolina State University. Lawton, J. and R.W. Beard (1999). Successive Galerkin approximation of nonlinear optimal attitude control. In: Proceedings of the American Control Conference. Lukes, D. L. (1969). Optimal regulation of nonlinear dynamical systems. SIAM J. Control Optim. 7(1), 75–101. McLain, T.W. and R.W. Beard (1998). Nonlinear optimal control of underwater robotic vehicle. Technical report. Departement of Mechanical Engineering, Brigham Young University, Provo, UT 84602. Nishikawa, Y., N. Sannomiya and H. Itakura (1971). A method for suboptimal design of nonlinear feedback systems. Automatica 7(6), 703–712. Schweickhardt, T. and F. Allg¨ower (2004b). Trajectory-based approximate optimal controller synthesis. Technical report. Institute for Systems Theory in Engineering, University of Stuttgart, Germany. Wernli, A. and G. Cook (1975). Suboptimal control for the nonlinear quadratic regulator problem. Automatica 11(1), 75–84.