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Full-Text Articles in Engineering
Neural Network Control Of Mobile Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks
Neural Network Control Of Mobile Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, an asymptotically stable (AS) combined kinematic/torque control law is developed for leader-follower-based formation control using backstepping in order to accommodate the complete dynamics of the robots and the formation, and a neural network (NN) is introduced along with robust integral of the sign of the error feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are as and that the NN weights are bounded as opposed to uniformly ultimately bounded stability which is typical with most …
Automatic Drift Compensation Using Phase Correlation Method For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani, Eric W. Bohannan
Automatic Drift Compensation Using Phase Correlation Method For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani, Eric W. Bohannan
Electrical and Computer Engineering Faculty Research & Creative Works
Nanomanipulation and nanofabrication with an atomic force microscope (AFM) or other scanning probe microscope (SPM) are a precursor for nanomanufacturing. It is still a challenging task to accomplish nanomanipulation automatically. In ambient conditions without stringent environmental controls, the task of nanomanipulation requires extensive human intervention to compensate for the spatial uncertainties of the SPM. Among these uncertainties, the thermal drift, which affects spatial resolution, is especially hard to solve because it tends to increase with time, and cannot be compensated simultaneously by feedback from the instrument. In this paper, a novel automatic compensation scheme is introduced to measure and estimate …
Generalized Hamilton-Jacobi-Bellman Formulation-Based Neural Network Control Of Affine Nonlinear Discrete-Time Systems, Zheng Chen, Jagannathan Sarangapani
Generalized Hamilton-Jacobi-Bellman Formulation-Based Neural Network Control Of Affine Nonlinear Discrete-Time Systems, Zheng Chen, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time (DT) systems. The method is based on least squares successive approximation solution of the generalized Hamilton-Jacobi-Bellman (GHJB) equation which appears in optimization problems. Successive approximation using the GHJB has not been applied for nonlinear DT systems. The proposed recursive method solves the GHJB equation in DT on a well-defined region of attraction. The definition of GHJB, pre-Hamiltonian function, HJB equation, and method of updating the control function for the affine nonlinear DT systems under small perturbation assumption are proposed. …