Modified quasilinearization algorithm for optimal control problems with bounded state

This paper considers the numerical solution of optimal control problems involving a functionalI subject to differential constraints, a state inequality constraint, and terminal constraints. The problem is to find the statex(t), the controlu(t), and the parameter so that the functional is minimized, while the constraints are satisfied to a predetermined accuracy.A modified quasilinearization algorithm is developed. Its main property is the descent property in the performance indexR, the cumulative error in the constraints and the optimality conditions. Modified quasilinearization differs from ordinary quasilinearization because of the inclusion of the scaling factor (or stepsize) in the system of variations. The stepsize is determined by a one-dimensional search on the performance indexR. Since the first variation R is negative, the decrease inR is guaranteed if is sufficiently small. Convergence to the solution is achieved whenR becomes smaller than some preselected value.Here, the state inequality constraint is handled in a direct manner. A predetermined number and sequence of subarcs is assumed and, for the time interval for which the trajectory of the system lies on the state boundary, the control is determined so that the state boundary is satisfied. The state boundary and the entrance conditions are assumed to be linear inx and , and the modified quasilinearization algorithm is constructed in such a way that the state inequality constraint is satisfied at each iteration and along all of the subarcs composing the trajectory.At first glance, the assumed linearity of the state boundary and the entrance conditions appears to be a limitation to the theory. Actually, this is not the case. The reason is that every constrained minimization problem can be brought to the present form through the introduction of additional state variables.In order to start the algorithm, some nominal functionsx(t),u(t), and nominal multipliers (t), (t), , must be chosen. In a real problem, the selection of the nominal functions can be made on the basis of physical considerations. Concerning the nominal multipliers, no useful guidelines have been available thus far. In this paper, an auxiliary minimization algorithm for selecting the multipliers optimally is presented: the performance indexR is minimized with respect to (t), (t), , . Since the functionalR is quadratically dependent on the multipliers, the resulting variational problem is governed by optimality conditions which are linear and, therefore, can be solved without difficulty.The numerical examples illustrating the theory demonstrate the feasibility as well as the rapidity of convergence of the technique developed in this paper.This research was supported by the Office of Scientific Research, Office of Aerospace Research, United States Air Force, Grant No. AF-AFOSR-72-2185. The authors are indebted to Dr. R. R. Iyer and Mr. A. K. Aggarwal for helpful discussions as well as analytical and numerical assistance. This paper is a condensation of the investigations described in Refs. 1–2.