A Dual Neural Network Architecture for Linear and Nonlinear Control of Inverted Pendulum on a Cart

The use of a self-contained dual neural network architecture for the solution of nonlinear optimal control problems is investigated in this study. The network structure solves the dynamic programming equations in stages and at the convergence, one network provides the optimal control and the second network provides a fault tolerance to the control system. We detail the steps in design and solve a linearized and a nonlinear, unstable, four-dimensional inverted pendulum on a cart problem. Numerical results are presented and compared with linearized optimal control. Unlike the previously published neural network solutions, this methodology does not need any external training, solves the nonlinear problem directly and provides a feedback control

A New Neural Architecture for Homing Missile Guidance

We present a new neural architecture which imbeds dynamic programming solutions to solve optimal target-intercept problems. They provide feedback guidance solutions, which are optimal with any initial conditions and time-to-go, for a 2D scenario. The method discussed in this study determines an optimal control law for a system by successively adapting two networks - an action and a critic network. This method determines the control law for an entire range of initial conditions; it simultaneously determines and adapts the neural networks to the optimal control policy for both linear and nonlinear systems. In addition, it is important to know that the form of control does not need to be known in order to use this metho

Adaptive Critic Based Neural Networks for Control (Low Order System Applications)

Dynamic programming is an exact method of determining optimal control for a discretized system. Unfortunately, for nonlinear systems the computations necessary with this method become prohibitive. This study investigates the use of adaptive neural networks that utilize dynamic programming methodology to develop near optimal control laws. First, a one dimensional infinite horizon problem is examined. Problems involving cost functions with final state constraints are considered for one dimensional linear and nonlinear systems. A two dimensional linear problem is also investigated. In addition to these examples, an example of the corrective capabilities of critics is shown. Synthesis of the networks in this study needs no external training; they do not need any apriori knowledge of the functional form of control. Comparison with specific optimal control techniques show that the networks yield optimal control over the entire range of trainin

Adaptive-Critic-Based Neural Networks for Aircraft Optimal Control

A dual neural network architecture for the solution of aircraft control problems is presented. The neural network structure, consisting of an action network and a critic network, is used to approximately solve the dynamic programming equations associated with optimal control with a high degree of accuracy. Numerical results from applying this methodology to optimally control the longitudinal dynamics of an aircraft are presented. The novelty in this synthesis of the optimal controller network is that it needs no external training inputs; it needs no a priori knowledge of the form of control. Numerical experiments with neural-network-based control as well as other pointwise optimal control techniques are presented. These results show that this network architecture yields optimal control over the entire range of training. In other words, the neural network can function as an autopilot. A scalar problem is also used in this study for easier illustration of the solution development

Adaptive Critic Based Neural Networks for Control

Dynamic Programming is an exact method of determining optimal control for a discretized system. Unfortunately, for nonlinear systems the computations necessary with this method become prohibitive. This study investigates the use of adaptive neural networks that utilize dynamic programming methodology to develop near optimal control laws. First, a one dimensional infinite horizon problem is examined. Problems involving cost functions with final state constraints are considered for one dimensional linear and nonlinear systems. A two dimensional linear problem is also investigated. In addition to these examples, an example of the corrective capabilities of critics is shown. Synthesis of the networks in this study needs no external training; they do not need any apriori knowledge of the functional form of control. Comparison with specific optimal control techniques show that the networks yield optimal control over the entire range of training