Optimal control by Pontryagin on hybrid computer.
Pontryagin maximum principle applied to solution of optimal control problems by hybrid computer, using digital parameter optimizer to solve two- point boundary value problem
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Pontryagin maximum principle applied to solution of optimal control problems by hybrid computer, using digital parameter optimizer to solve two- point boundary value problem
Optimal linear filter derivation using Pontryagin maximum principle and gradient matrices for optimal filter coefficients
Determining optimum atmospheric reentry trajectories using Pontryagin maximum principle
Iterative solution of time optimal control boundary value problem resulting from application of Pontryagin maximum principle
Deterministic optimal control, discussing Bellman dynamic programming method, Pontryagin maximum principle, orbital transfer, interplanetary guidance, etc
Deterministic optimal control, discussing Bellman dynamic programming method, Pontryagin maximum principle, orbital transfer, interplanetary guidance, etc
Throughout the years, many researchers have calculated and optimized trajectory solutions for lunar landing systems by employing sophisticated mathematical methods, that include: Hamilton’s Principle of Variation, Pontryagin’s maximum principle, and well known convex-optimization techniques among others. Many of these approaches typically require expensive computational resources to achieve convergence in the solution. In an effort to reduce complexity and the computational load required to obtain real-time guidance commands, a simple physics-based work-energy approach has been formulated. This approach is based on the dissipation of the mechanical energy of the vehicle to its final desired energy state required to achieve a safe landing. The rocket engine(s) employed during landing (among other maneuvers) dissipates mechanical energy by both doing work against the velocity vector of the vehicle (thus defining the trajectory path), and by jettisoning mass. Therefore, by solving the energy dissipation problem at every step of the maneuver, a much simpler formulation that naturally and quickly attains convergence is obtained. This formulation is not limited to approach, landing, and divert maneuvers, but in principle it can be employed during de-orbiting, braking burn, ascent, as well as orbit insertion.
Throughout the years, many researchers have calculated and optimized trajectory solutions for lunar landing systems by employing sophisticated mathematical methods, that include: Hamilton’s Principle of Variation, Pontryagin’s maximum principle, and well known convex-optimization techniques among others. Many of these approaches typically require expensive computational resources to achieve convergence in the solution. In an effort to reduce complexity and the computational load required to obtain real-time guidance commands, a simple physics-based work-energy approach has been formulated. This approach is based on the dissipation of the mechanical energy of the vehicle to its final desired energy state required to achieve a safe landing. The rocket engine(s) employed during landing (among other maneuvers) dissipates mechanical energy by both doing work against the velocity vector of the vehicle (thus defining the trajectory path), and by jettisoning mass. Therefore, by solving the energy dissipation problem at every step of the maneuver, a much simpler formulation that naturally and quickly attains convergence is obtained. This formulation is not limited to approach, landing, and divert maneuvers, but in principle it can be employed during de-orbiting, braking burn, ascent, as well as orbit insertion.
Pontryagin optimal principle applied to minimum fuel satellite attitude control with two feedback controls
We address the problem of navigating a set (fleet) of aircraft in an aerial route network so as to bring each aircraft to its destination at a specified time and with minimal distance separation assured between all aircraft at all times. The speed range, initial position, required destination, and required time of arrival at destination for each aircraft are assumed provided. Each aircraft's movement is governed by a controlled differential equation (state equation). The problem consists in choosing for each aircraft a path in the route network and a control strategy so as to meet the constraints and reach the destination at the required time. The main contribution of the paper is a model that allows to recast this problem as a decoupled collection of problems in classical optimal control and is easily generalized to the case when inertia cannot be neglected. Some qualitative insight into solution behavior is obtained using the Pontryagin Maximum Principle. Sample numerical solutions are computed using a numerical optimal control solver. The proposed model is first step toward increasing the fidelity of continuous time control models of air traffic in a terminal airspace. The Pontryagin Maximum Principle implies the polygonal shape of those portions of the state trajectories away from those states in which one or more aircraft pair are at minimal separation. The model also confirms the intuition that, the narrower the allowed speed ranges of the aircraft, the smaller the space of optimal solutions, and that an instance of the optimal control problem may not have a solution at all (i.e., no control strategy that meets the separation requirement and other constraints).
Pontryagin maximum principle to optimize attitude control systems on the basis of minimum fuel or energy consumption
Pursuit problem improper solutions under existence assumptions emphasizing control parameters and pursuer trajectory
Satellite attitude control in elliptic orbit
Pontryagin maximum principle, calculus of variations, and dynamic programming optimization techniques applied to trajectory and guidance problems
Pontryagin maximum principle used to study stability of periodic equations in time optimal control problems
Production scheduling optimization by using discrete form of Pontryagin maximum principle
Fixed time fuel optimal control of linear time invariant plant, using Pontryagin minimum principle and Newton method
Adaptive random search algorithm utilizing boundary cost-function hypersurfaces measurement to implement Pontryagin maximum principle, discussing hybrid computer use, iterative solution and convergence properties