Papers by Emmanuel Collins
Robust Control Design for a Benchmark Problem Using the Maximum Entropy Approach
Robust controllers are developed for a benchmark problem by using the Maximum Entropy/Optimal Pro... more Robust controllers are developed for a benchmark problem by using the Maximum Entropy/Optimal Projection design methodology.
Fuzzy PI control of an industrial weigh belt feeder
This paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an i... more This paper proposes and experimentally demonstrates two types of fuzzy logic controllers for an industrial weigh belt feeder. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of
A parameterization of minimal plants
IEEE Transactions on Automatic Control, Apr 1, 1994
We present the input-normal Riccati parameterization, a generalization of Kabamba&#39... more We present the input-normal Riccati parameterization, a generalization of Kabamba's input-normal form, which allows the continuous parameterization of the set of minimal linear systems of a given order that have distinct singular values; the input-normal Riccati form has no requirement that the underlying system be stable. We also present formulas for the use of the input-normal Riccati parameterization for the
Experimental demonstration of active vibration control for flexible structures
ABSTRACT
State Covariance Assignment of Discrete Systems: Development and Applications
Performance objectives that are expressed as upper bounds on the steady state variances of the sy... more Performance objectives that are expressed as upper bounds on the steady state variances of the system outputs and inputs are quite common in stochastic control problems. Past approaches to developing control laws for constrained variance objectives have been indirect. Currently, the most direct approaches are those based on linear-quadratic (LQ) theory which allow the designer to synthesize a controller which minimizes a weighted sum of the input and output variances. Since minimizing a scalar cost does not insure that the multiple variance requirements will be satisfied, iterative schemes must be used to determine the weights in the LQ cost functional. The LQ approaches at present have two deficiencies. First of all, it has not been proved that weights exist which will satisfy the variance requirements even if a satisfying controller is known to exist apriori. Also, the iterative schemes which have been developed to search for the weights are not proven to converge. Now it is possible to meet the output variance requirements by assigning a specified state covariance to the system. Thus, this dissertation introduces and solves the following (state covariance assignment) problem for linear discrete-time systems: (i) characterize the entire set of state covariances which may be assigned to a system by state feedback and (ii) find the set of all state feedback gains which will assign an admissible state covariance to the system
LQG and maximum entropy control design for the Hubble Space Telescope
STIA, 1993
ABSTRACT
A Novel Approach to Adaptive Flow Separation Control
Robotics Collaborative Technology Alliance (RCTA): Technical Exchange Meeting (TEM) 2015
Abstract : This report describes the outcomes of a 2015 Technical Exchange Meeting (TEM) dedicate... more Abstract : This report describes the outcomes of a 2015 Technical Exchange Meeting (TEM) dedicated to identifying the gaps and prioritizing critical research areas in the human-robot interaction (HRI) field, focusing on unique mobility robots (UMRs) with special manipulation capabilities to progress peer-to-peer, tactical human-robot teaming. The TEM was held to develop a unified vision for HRI research to enable teaming among Soldiers and UMRs and to identify points of intersection in the Robotics Collaborative Technology Alliance (RCTA) to facilitate HRI research. Accomplishing this goal required input from all RCTA technical areas including Dexterous Manipulation and Unique Mobility, Intelligence, and Perception. The TEM achieved the following: a joint understanding of the state of the art of UMRs, identification of technical areas in which HRI can assist UMRs and support prototype delivery, and prioritization of an action plan for future HRI in UMR research.
Control design and experimental results of sensor based direct process control for bar turning
Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), Jul 10, 2003
ABSTRACT Describes an in-process measurement and control system called radial error feedback geom... more ABSTRACT Describes an in-process measurement and control system called radial error feedback geometric adaptive control (REFGAC) system for bar turning in CNC turning centers. The REFGAC system was design to compensate for the radial error caused by deflection under cutting force. To compensate for the radial error, Kalman filters for prediction together with PID control laws were designed, based on models of the deflection of the bar. Experimental results showed that the dimensional and geometric accuracy of the workpiece is substantially improved by this radial error feedback geometric adaptive control technique
A homotopy algorithm for the combined H-2/H-infinity model reduction problem
ABSTRACT
Active control experiments for large-optics vibration alleviation
Proceedings of SPIE, Oct 1, 1990
ABSTRACT
Efficiency and optimality in constrained variance control
ABSTRACT
<title>Neural network architecture for solving the algebraic matrix Riccati equation</title>
Proceedings of SPIE, Mar 22, 1996
This paper presents a neurocomputing approach for solving the algebraic matrix Riccati equation. ... more This paper presents a neurocomputing approach for solving the algebraic matrix Riccati equation. This approach is able to utilize a good initial condition to reduce the computation time in comparison to standard methods for solving the Riccati equation. The repeated solutions of closely related Riccati equations appears in homotopy algorithms to solve certain problems in fixed-architecture control. Hence, the new approach has the potential to significantly speed-up these algorithms. It also has potential applications in adaptive control. The structured neural network architecture is trained using error backpropagation based on a steepest-descent learning rule. An example is given which illustrates the advantage of utilizing a good initial condition (i.e., initial setting of the neural network synaptic weight matrix) in the structured neural network.
Demonstrations of LSS active vibration control technology on representative ground-based testbeds
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Efficient solution of linearly coupled Lyapunov equations
ABSTRACT
<title>Reliable design of H<formula><inf><roman>2</roman></inf></formula> optimal reduced-order controllers via a homotopy algorithm</title>
Proceedings of SPIE, Jul 10, 1992
Four experimental demonstrations of active vibration control for flexible structures
AIAA Guidance, Navigation and Control Conference, 1990
Laboratory experiments designed to test prototype active-vibration-control systems under developm... more Laboratory experiments designed to test prototype active-vibration-control systems under development for future flexible space structures are described, summarizing previously reported results. The control-synthesis technique employed for all four experiments was ...
Synthesis of fixed-architecture, robust H/sub 2/ and H/sub /spl infin// controllers
ABSTRACT
Robust control for a benchmark problem via nonlinear matrix inequalities
ABSTRACT
Journal of Dynamic Systems Measurement and Control-transactions of The Asme, Mar 1, 2017
Input constraints are active in robot trajectory planning when a mobile robot traverses mobility ... more Input constraints are active in robot trajectory planning when a mobile robot traverses mobility challenges such as steep hills that limit the acceleration of the robot due to the torque constraints of the motor or engine or in manipulator lifting tasks when the load is sufficiently heavy that the torque constraints of the robot's motor prevent it from statically supporting the load in regions of the robot's workspace. This paper presents a general methodology for solving these planning tasks using a minimum time cost function and applies it to the problem of a multiple degree of freedom manipulator lifting a heavy load. Planning for these types of problems requires use of the robot's dynamic model. Here, we plan using Sampling-Based Model Predictive Optimization, which is only practical if the planning can be done quickly. Hence, substantial attention is given to efficient computations by 1) using the dynamic model without integrating it, 2) using optimal control theory to develop an "optimistic A * estimate of cost-to-goal", which is in this case a rigorous lower bound on the minimum time from a current state to a goal state, and 3) using prior experience to speed up the computation of a new trajectory. The methodology is experimentally verified for heavy lifting using a two-link manipulator.
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Papers by Emmanuel Collins