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Full-Text Articles in Mechanical Engineering

Robust/Optimal Temperature Profile Control Of A High-Speed Aerospace Vehicle Using Neural Networks, Vivek Yadav, Radhakant Padhi, S. N. Balakrishnan Jan 2007

Robust/Optimal Temperature Profile Control Of A High-Speed Aerospace Vehicle Using Neural Networks, Vivek Yadav, Radhakant Padhi, S. N. Balakrishnan

Mechanical and Aerospace Engineering Faculty Research & Creative Works

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. a 1-D distributed parameter model of a fin is developed from basic thermal physics principles. ldquoSnapshotrdquo solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the ldquoproper orthogonal decompositionrdquo (POD) technique and the snapshot solutions. a low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. an ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with ...


Robust State Dependent Riccati Equation Based Robot Manipulator Control, Ming Xin, S. N. Balakrishnan, Zhongwu Huang Jan 2001

Robust State Dependent Riccati Equation Based Robot Manipulator Control, Ming Xin, S. N. Balakrishnan, Zhongwu Huang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

We present a new optimal control approach to robust control of robot manipulators in the framework of state dependent Riccati equation (SDRE) technique. To treat this highly nonlinear control system, we formulate it as a nonlinear optimal regulator problem. SDRE technique was used to synthesize an optimal controller to this class of robot control problem. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the parameter uncertainties. A typical two-link robot position control problem was studied to show the effectiveness of SDRE approach and robust extra control design to robotic application.


Robust State Dependent Riccati Equation Based Guidance Laws, S. N. Balakrishnan, Ming Xin Jan 2001

Robust State Dependent Riccati Equation Based Guidance Laws, S. N. Balakrishnan, Ming Xin

Mechanical and Aerospace Engineering Faculty Research & Creative Works

A robust state dependent Riccati equation based guidance/control is investigated in this study. In order to have a better design tool in terms of required interceptor accelerations, the target intercept geometry is formulated in a set of polar coordinates. With this formulation, we formulate a cost function with state dependent weights. In this study, we investigate the effects of such cost functions on the levels of interceptor accelerations. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the target acceleration. In this manner, we will not need target acceleration estimation explicitly ...


Robust Adaptive Critic Based Neurocontrollers For Systems With Input Uncertainties, S. N. Balakrishnan, Zhongwu Huang Jan 2000

Robust Adaptive Critic Based Neurocontrollers For Systems With Input Uncertainties, S. N. Balakrishnan, Zhongwu Huang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

A two-neural network approach to solving optimal control problems is described in this study. This approach called the adaptive critic method consists of two neural networks: one is called the supervisor or critic, and the other is called an action network or controller. The inputs to both these networks are the current states of the system to be controlled. Each network is trained through an output of the other network and the conditions for optimal control. When their outputs are mutually consistent, the controller network output is optimal. The optimality is limited to the underlying model. Hence, we develop a ...


Robustness Analysis Of Hopfield And Modified Hopfield Neural Networks In Time Domain, Jie Shen, S. N. Balakrishnan Jan 1998

Robustness Analysis Of Hopfield And Modified Hopfield Neural Networks In Time Domain, Jie Shen, S. N. Balakrishnan

Mechanical and Aerospace Engineering Faculty Research & Creative Works

A variant of the Hopfield network, called the modified Hopfield network is formulated. This network which consists of two mutually recurrent networks has more free parameters than the well-known Hopfield network. Stability analysis of this network is presented. The analysis is carried out in the time domain with an application of the Lyapunov method and robust control Lyapunov function. The current flow in the network is treated as a "control". This "controller" is shown to guarantee "a practically stabilizing control". Analysis of the Hopfield network is also included for completion.


System Modeling And Control Of Smart Structures, Frank J. Kern, Leslie Robert Koval, K. Chandrashekhara, Vittal S. Rao Jan 1995

System Modeling And Control Of Smart Structures, Frank J. Kern, Leslie Robert Koval, K. Chandrashekhara, Vittal S. Rao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents multidisciplinary research and curriculum efforts at the University of Missouri-Rolla in the smart structures area. The primary objective of our project is to integrate research results with curriculum development for the benefit of students in electrical, and mechanical and aerospace engineering and engineering mechanics. The approach to the accomplishment of curriculum objectives is the development of a two-course sequence in the smart structures area with an integrated laboratory. The research portion of the project addresses structural identification and robust control methods for smart structures. A brief summary of the research results and a description of curriculum development ...