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Full-Text Articles in Aerospace Engineering
High Bandwidth Control Of Precision Motion Instrumentation, Douglas A. Bristow, Jingyan Dong, Andrew G. Alleyne, Srinivasa M. Salapaka, Placid M. Ferreira
High Bandwidth Control Of Precision Motion Instrumentation, Douglas A. Bristow, Jingyan Dong, Andrew G. Alleyne, Srinivasa M. Salapaka, Placid M. Ferreira
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This article presents a high-bandwidth control design suitable for precision motion instrumentation. Iterative learning control (ILC), a feedforward technique that uses previous iterations of the desired trajectory, is used to leverage the repetition that occurs in many tasks, such as raster scanning in microscopy. Two ILC designs are presented. The first design uses the motion system dynamic model to maximize bandwidth. The second design uses a time-varying bandwidth that is particularly useful for nonsmooth trajectories such as raster scanning. Both designs are applied to a multiaxis piezoelectric-actuated flexure system and evaluated on a nonsmooth trajectory. The ILC designs demonstrate significant …
Issues On Stability Of Adp Feedback Controllers For Dynamical Systems, S. N. Balakrishnan, Jie Ding, F. L. Lewis
Issues On Stability Of Adp Feedback Controllers For Dynamical Systems, S. N. Balakrishnan, Jie Ding, F. L. Lewis
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This paper traces the development of neural-network (NN)-based feedback controllers that are derived from the principle of adaptive/approximate dynamic programming (ADP) and discusses their closed-loop stability. Different versions of NN structures in the literature, which embed mathematical mappings related to solutions of the ADP-formulated problems called “adaptive critics” or “action-critic” networks, are discussed. Distinction between the two classes of ADP applications is pointed out. Furthermore, papers in “model-free” development and model-based neurocontrollers are reviewed in terms of their contributions to stability issues. Recent literature suggests that work in ADP-based feedback controllers with assured stability is growing in diverse forms.
Design Of A Linear Time-Varying Cross-Coupled Iterative Learning Controller, K. L. Barton, Douglas A. Bristow, Andrew G. Alleyne
Design Of A Linear Time-Varying Cross-Coupled Iterative Learning Controller, K. L. Barton, Douglas A. Bristow, Andrew G. Alleyne
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In many manufacturing applications contour tracking is more important than individual axis tracking. Many control techniques, including iterative learning control (ILC), target individual axis error. Because individual axis error only indirectly relates to contour error, these approaches may not be very effective for contouring applications. Cross-coupled ILC (CCILC) is a variation on traditional ILC that targets the contour tracking directly. In contour trajectories with rapid changes, high frequency control is necessary in order to meet tracking requirements. This paper presents an improved CCILC that uses a linear time-varying (LTV) filter to provide high frequency control for short durations. The improved …
Weighting Matrix Design For Robust Monotonic Convergence In Norm Optimal Iterative Learning Control, Douglas A. Bristow
Weighting Matrix Design For Robust Monotonic Convergence In Norm Optimal Iterative Learning Control, Douglas A. Bristow
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper we examine the robustness of norm optimal ILC with quadratic cost criterion for discrete-time, linear time-invariant, single-input single-output systems. A bounded multiplicative uncertainty model is used to describe the uncertain system and a sufficient condition for robust monotonic convergence is developed. We find that, for sufficiently large uncertainty, the performance weighting can not be selected arbitrarily large, and thus overall performance is limited. To maximize available performance, a time-frequency design methodology is presented to shape the weighting matrix based on the initial tracking error. The design is applied to a nanopositioning system and simulation results are presented.
Frequency Domain Analysis And Design Of Iterative Learning Control For Systems With Stochastic Disturbances, Douglas A. Bristow
Frequency Domain Analysis And Design Of Iterative Learning Control For Systems With Stochastic Disturbances, Douglas A. Bristow
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this work we examine the performance of iterative learning control (ILC) for systems with non-repeating disturbances and random noise. Single-input, single- output linear time-invariant systems and iteration-invariant learning filters are considered. We find that a tradeoff exists between the convergence rate and converged error spectrum. Optimal filter designs, which are dependant on the disturbance and noise spectra, are developed. We also present simple design guidelines for the case when explicit models of disturbance and noise spectra are not available. A numerical design example is presented.
Monotonic Convergence Of Iterative Learning Control For Uncertain Systems Using A Time-Varying Filter, Douglas A. Bristow, Andrew G. Alleyne
Monotonic Convergence Of Iterative Learning Control For Uncertain Systems Using A Time-Varying Filter, Douglas A. Bristow, Andrew G. Alleyne
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Iterative learning control (ILC) is a learning technique used to improve the performance of systems that execute the same task multiple times. Learning transient behavior has emerged as an important topic in the design and analysis of ILC systems. In practice, the learning control is often low-pass filtered with a ldquoQ-filterrdquo to prevent transient growth, at the cost of performance. In this note, we consider linear time-invariant, discrete-time, single-input single-output systems, and convert frequency-domain uncertainty models to a time-domain representation for analysis. We then develop robust monotonic convergence conditions, which depend directly on the choice of the Q-filter and are …
Neuroadaptive Model Following Controller Design For A Nonaffine Uav Model, Nishant Unnikrishnan, S. N. Balakrishnan
Neuroadaptive Model Following Controller Design For A Nonaffine Uav Model, Nishant Unnikrishnan, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This paper proposes a new model-following adaptive control design technique for nonlinear systems that are nonaffine in control. The adaptive controller uses online neural networks that guarantee tracking in the presence of unmodeled dynamics and/or parameter uncertainties present in the system model through an online control adaptation procedure. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture the unmodeled (neglected) dynamics or model uncertainties due to parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a reference model. This method …
Optimal Control Synthesis Of A Class Of Nonlinear Systems Using Single Network Adaptive Critics, Radhakant Padhi, Nishant Unnikrishnan, S. N. Balakrishnan
Optimal Control Synthesis Of A Class Of Nonlinear Systems Using Single Network Adaptive Critics, Radhakant Padhi, Nishant Unnikrishnan, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
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 optimal control (stationary) equation is explicitly solvable for control in terms of state and costate variables. The SNAC architecture offers three potential advantages; a simpler architecture, significant savings of computational load and reduction in approximation errors. In order to demonstrate these benefits, a real-life micro-electro-mechanical-system (MEMS) problem has been …
Development And Implementation Of New Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan
Development And Implementation Of New Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UAV platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch and yaw accelerations and rates along with the roll and pitch. The 100400 mini-air data boom from spaceage control provides the airspeed, altitude, angle of attack and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control …
Experimental Implementation Of Adaptive-Critic Based Infinite Time Optimal Neurocontrol For A Heat Diffusion System, Prashant Prabhat, S. N. Balakrishnan, Dwight C. Look
Experimental Implementation Of Adaptive-Critic Based Infinite Time Optimal Neurocontrol For A Heat Diffusion System, Prashant Prabhat, S. N. Balakrishnan, Dwight C. Look
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Recently the synthesis methodology for the infinite time optimal neuro-controllers for PDE systems in the framework of adaptive-critic design has been developed. In this paper, first we model an experimental setup representing one dimensional heat diffusion problems. Then we synthesize and implement an adaptive-critic based neuro-controller for online temperature profile control of the experimental setup.
Convergence Analysis Of Adaptive Critic Based Optimal Control, S. N. Balakrishnan, Xin Liu
Convergence Analysis Of Adaptive Critic Based Optimal Control, S. N. Balakrishnan, Xin Liu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Adaptive critic based neural networks have been found to be powerful tools in solving various optimal control problems. The adaptive critic approach consists of two neural networks which output the control values and the Lagrangian multipliers associated with optimal control. These networks are trained successively and when the outputs of the two networks are mutually consistent and satisfy the differential constraints, the controller network output produces optimal control. In this paper, we analyze the mechanics of convergence of the network solutions. We establish the necessary conditions for the network solutions to converge and show that the converged solution is optimal.
Stability Analysis Of Nonlinear Machining Force Controllers, Robert G. Landers, Yen-Wen Lu
Stability Analysis Of Nonlinear Machining Force Controllers, Robert G. Landers, Yen-Wen Lu
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Model parameters vary significantly during a normal operation, thus, adaptive techniques have predominately been used. However, model-based techniques that carefully account for changes in the force process have again been examined due to the reduced complexity afforded by such techniques. In this paper, the effect of model parameter variations on the closed-loop stability for two model-based force controllers is examined. It was found that the stability boundary in the process parameter space can be exactly determined for force control systems designed for static force processes. For force control systems designed for first-order force processes, it was found that the stability …
Adaptive Critic Based Neurocontroller For Autolanding Of Aircrafts, S. N. Balakrishnan, Gaurav Saini
Adaptive Critic Based Neurocontroller For Autolanding Of Aircrafts, S. N. Balakrishnan, Gaurav Saini
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper, adaptive critic based neural networks have been used to design a controller for a benchmark problem in aircraft autolanding. The adaptive critic control methodology comprises successive adaptations of two neural networks, namely action and critic network (which approximate the Hamiltonian equations associated with optimal control theory) until closed loop optimal control is achieved. The autolanding problem deals with longitudinal dynamics of an aircraft which is to be landed in a specified touchdown region (within acceptable ranges of speed, pitch angle and sink rate) in the presence of wind disturbances and gusts using elevator deflection as the control …