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Full-Text Articles in Engineering
Optimal Control Of A Class Of One-Dimensional Nonlinear Distributed Parameter Systems With Discrete Actuators, Radhakant Padhi, S. N. Balakrishnan
Optimal Control Of A Class Of One-Dimensional Nonlinear Distributed Parameter Systems With Discrete Actuators, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Combining the principles of dynamic inversion and optimization theory, a new approach is presented for stable control of a class of one-dimensional nonlinear distributed parameter systems with a finite number of actuators in the spatial domain. Unlike the existing ''approximate-then-design'' and ''design-then-approximate'' techniques, this approach does not use any approximation either of the system dynamics or of the resulting controller. The formulation has more practical significance because one can implement a set of discrete controllers with relative ease. To demonstrate the potential of the proposed technique, a real-life temperature control problem for a heat transfer application is solved through simulations. …
Adaptive Critic Based Neural Networks For Control-Constrained Agile Missile Control, Dongchen Han, S. N. Balakrishnan
Adaptive Critic Based Neural Networks For Control-Constrained Agile Missile Control, Dongchen Han, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
We investigate the use of an `adaptive critic' based controller to steer an agile missile with a constraint on the angle of attack from various initial Mach numbers to a given final Mach number in minimum time while completely reversing its flightpath angle. We use neural networks with a two-network structure called `adaptive critic' to carry out the optimization process. This structure obtains an optimal controller through solving Hamiltonian equations. This approach needs no external training; each network along with the optimality equations generates the output for the other network. When the outputs are mutually consistent, the controller output is …
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Parallel Implementation Of A Recursive Least Squares Neural Network Training Method On The Intel Ipsc/2, James Edward Steck, Bruce M. Mcmillin, K. Krishnamurthy, M. Reza Ashouri, Gary G. Leininger
Computer Science Faculty Research & Creative Works
An algorithm based on the Marquardt-Levenberg least-square optimization method has been shown by S. Kollias and D. Anastassiou (IEEE Trans. on Circuits Syst. vol.36, no.8, p.1092-101, Aug. 1989) to be a much more efficient training method than gradient descent, when applied to some small feedforward neural networks. Yet, for many applications, the increase in computational complexity of the method outweighs any gain in learning rate obtained over current training methods. However, the least-squares method can be more efficiently implemented on parallel architectures than standard methods. This is demonstrated by comparing computation times and learning rates for the least-squares method implemented …
Planning Optimal Robot Trajectories By Cell Mapping, W. H. Zhu, Ming-Chuan Leu
Planning Optimal Robot Trajectories By Cell Mapping, W. H. Zhu, Ming-Chuan Leu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A cell-mapping method is introduced for planning global trajectories of robotic manipulators in cases where the cell space is composed of combination pairs of plane cells. With the proposed method, optimal trajectory problems in the free field and in the obstacle-constrained field are studied. Two numerical examples are given to show the obtained optimal trajectories and controls.
Characteristics And Optimal Design Of Variable Airgap Linear Force Motors, Ming-Chuan Leu, E. V. Scorza, D. L. Bartel
Characteristics And Optimal Design Of Variable Airgap Linear Force Motors, Ming-Chuan Leu, E. V. Scorza, D. L. Bartel
Mechanical and Aerospace Engineering Faculty Research & Creative Works
An analytical model for predicting the characteristics of variable airgap linear force motors is developed. the model takes into account magnetic losses including the leakage and fringing effects and the reluctance existing at the contacts between permanent magnets and pole pieces. the model is validated by comparing its predicted characteristics with the results obtained from experiments and a finite element program. with the use of the modelled characteristics, computer programs based on the method of constrained steepest descent with state equations are developed for automating and optimising the design of linear force motors. Numerical studies are made for both minimisation …