S. N. Balakrishnan

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Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations(More)
In this paper, a new nonlinear control method is used to design a full-envelope, hybrid bank-to-turn (BTT)/skid-to-turn (STT) autopilot for an air-breathing air-to-air missile. Through this new approach, called the D θ − method, we find approximate solutions to the Hamilton-Jacobi-Bellman (HJB) equation. As a result, the resulting nonlinear feedback law can(More)
This paper investigates the optimal control problem for linear impulsive systems with impulsive moments fixed. Based on adaptive dynamic programming(ADP), a numerical method is proposed to iteratively solve for this optimal impulsive control. The temporal difference of the value functions is used to determine whether the optimality has been achieved. A(More)
—In this study, we develop an adaptive-critic-based controller to steer an agile missile that has a constraint on the minimum flight Mach number from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. This class of bounded state space, free final time problems is very difficult to solve(More)
The concept of approximate dynamic programming and adaptive critic neural network based optimal controller is extended in this study to include systems governed by partial differential equations. An optimal controller is synthesized for a dispersion type tubular chemical reactor, which is governed by two coupled nonlinear partial differential equations. It(More)
An approximate dynamic programming (ADP) based neurocontroller is developed for a heat transfer application. Heat transfer problem for a fin in a car's electronic module is modeled as a nonlinear distributed parameter (infinite-dimensional) system by taking into account heat loss and generation due to conduction, convection and radiation. A low-order,(More)
Adaptive critic (AC) neural network solutions to optimal control designs using dynamic programming has reduced the need of complex computations and storage requirements that typical dynamic programming requires. In this paper, a " single network adaptive critic " (SNAC) is presented. This approach is applicable to a class of nonlinear systems where the(More)
Formation control of network of multi-agent systems with heterogeneous nonlinear dynamics is formulated as an optimal tracking problem and a decentralized controller is developed using the framework of 'adaptive critics' to solve the optimal control problem. The reference signal is assumed available only in online implementation so its dynamics is(More)
This paper presents a new controller design technique for systems driven with impulse inputs. Necessary conditions for optimal impulse control are derived. A neural network structure to solve the resulting equations for optimal control is presented. Solution concepts are illustrated with example problems that exhibit increasing levels of difficulty. Two(More)