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At least 253 records · Page 14

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NASA NTRS2005

Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

model predictive control algorithm (MPC)↗

NASA NTRS1989

Uncertainty modelling for the control of an active structure

Two approaches to uncertainty modeling of a precision active truss structure for control design are presented. The first approach is based on the H(infinity) optimal control theory, which is used for robust control design with an additive uncertainty model. It is designed to provide vibration suppression to selected modes of the structure at the accelerometer locations. The uncertainty model can be used as an adjustable design parameter which will enable a study of the trade-offs between performance and robust stability. The second approach is based on mu-synthesis. Although the structured singular value can be used to analyze the robustness of any type of uncertainty, there exist difficulties in synthesis if the uncertainty block contains both real and complex variations. Mu-synthesis techniques do not allow for real-parameter variations in the synthesis procedure. The use of a complex variation description enclosing the real variation is suggested.

Blackwood, G. H.↗

NASA NTRS2000

Robust Fixed-Structure Controller Synthesis

The ability to develop an integrated control system design methodology for robust high performance controllers satisfying multiple design criteria and real world hardware constraints constitutes a challenging task. The increasingly stringent performance specifications required for controlling such systems necessitates a trade-off between controller complexity and robustness. The principle challenge of the minimal complexity robust control design is to arrive at a tractable control design formulation in spite of the extreme complexity of such systems. Hence, design of minimal complexitY robust controllers for systems in the face of modeling errors has been a major preoccupation of system and control theorists and practitioners for the past several decades.

Corrado, Joseph R.↗

NASA NTRS1989

Robust high-performance control for robotic manipulators

A robust control scheme to accomplish accurate trajectory tracking for an integrated system of manipulator-plus-actuators is proposed. The control scheme comprises a feedforward and a feedback controller. The feedforward controller contains any known part of the manipulator dynamics that can be used for online control. The feedback controller consists of adaptive position and velocity feedback gains and an auxiliary signal which is simply generated by a fixed-gain proportional/integral/derivative controller. The feedback controller is updated by very simple adaptation laws which contain both proportional and integral adaptation terms. By introduction of a simple sigma modification to the adaptation laws, robustness is guaranteed in the presence of unmodeled dynamics and disturbances.

Seraji, H.↗

NASA NTRS2004

Sliding Mode Control of a Thermal Mixing Process

In this paper we consider the robust control of a thermal mixer using multivariable Sliding Mode Control (SMC). The mixer consists of a mixing chamber, hot and cold fluid valves, and an exit valve. The commanded positions of the three valves are the available control inputs, while the controlled variables are total mass flow rate, chamber pressure and the density of the mixture inside the chamber. Unsteady thermodynamics and linear valve models are used in deriving a 5th order nonlinear system with three inputs and three outputs, An SMC controller is designed to achieve robust output tracking in the presence of unknown energy losses between the chamber and the environment. The usefulness of the technique is illustrated with a simulation.

Richter, Hanz↗

NASA NTRS1987

Nonlinear feedback control of multiple robot arms

Multiple coordinated robot arms are modeled by considering the arms: (1) as closed kinematic chains, and (2) as a force constrained mechanical system working on the same object simultaneously. In both formulations a new dynamic control method is discussed. It is based on a feedback linearization and simultaneous output decoupling technique. Applying a nonlinear feedback and a nonlinear coordinate transformation, the complicated model of the multiple robot arms in either formulation is converted into a linear and output decoupled system. The linear system control theory and optimal control theory are used to design robust controllers in the task space. The first formulation has the advantage of automatically handling the coordination and load distribution among the robot arms. In the second formulation, by choosing a general output equation, researchers can superimpose the position and velocity error feedback with the force-torque error feedback in the task space simultaneously.

Tarn, T. J.↗

NASA NTRS1988

New nonlinear control algorithms for multiple robot arms

Multiple coordinated robot arms are modeled by considering the arms as closed kinematic chains and as a force-constrained mechanical system working on the same object simultaneously. In both formulations, a novel dynamic control method is discussed. It is based on feedback linearization and simultaneous output decoupling technique. By applying a nonlinear feedback and a nonlinear coordinate transformation, the complicated model of the multiple robot arms in either formulation is converted into a linear and output decoupled system. The linear system control theory and optimal control theory are used to design robust controllers in the task space. The first formulation has the advantage of automatically handling the coordination and load distribution among the robot arms. In the second formulation, it was found that by choosing a general output equation it became possible simultaneously to superimpose the position and velocity error feedback with the force-torque error feedback in the task space.

Tarn, T. J.↗

NASA NTRS2014

Virtual Deformation Control of the X-56A Model with Simulated Fiber Optic Sensors

A robust control law design methodology is presented to stabilize the X-56A model and command its wing shape. The X-56A was purposely designed to experience flutter modes in its flight envelope. The methodology introduces three phases: the controller design phase, the modal filter design phase, and the reference signal design phase. A mu-optimal controller is designed and made robust to speed and parameter variations. A conversion technique is presented for generating sensor strain modes from sensor deformation mode shapes. The sensor modes are utilized for modal filtering and simulating fiber optic sensors for feedback to the controller. To generate appropriate virtual deformation reference signals, rigid-body corrections are introduced to the deformation mode shapes. After successful completion of the phases, virtual deformation control is demonstrated. The wing is deformed and it is shown that angle-ofattack changes occur which could potentially be used to an advantage. The X-56A program must demonstrate active flutter suppression. It is shown that the virtual deformation controller can achieve active flutter suppression on the X-56A simulation model.

Suh, Peter M.↗

NASA NTRS2013

Virtual Deformation Control of the X-56A Model with Simulated Fiber Optic Sensors

A robust control law design methodology is presented to stabilize the X-56A model and command its wing shape. The X-56A was purposely designed to experience flutter modes in its flight envelope. The methodology introduces three phases: the controller design phase, the modal filter design phase, and the reference signal design phase. A mu-optimal controller is designed and made robust to speed and parameter variations. A conversion technique is presented for generating sensor strain modes from sensor deformation mode shapes. The sensor modes are utilized for modal filtering and simulating fiber optic sensors for feedback to the controller. To generate appropriate virtual deformation reference signals, rigid-body corrections are introduced to the deformation mode shapes. After successful completion of the phases, virtual deformation control is demonstrated. The wing is deformed and it is shown that angle-of-attack changes occur which could potentially be used to an advantage. The X-56A program must demonstrate active flutter suppression. It is shown that the virtual deformation controller can achieve active flutter suppression on the X-56A simulation model.

Modal Filter↗

NASA NTRS1990

Dissipative controller designs for second-order dynamic systems

The passivity theorem may be used to design robust controllers for structures with positive transfer functions. This result is extended to more general configurations using dissipative system theory. A stability theorem for robust, model-independent controllers of structures which lack collocated rate sensors and actuators is given. The theory is illustrated for non-square systems and systems with displacement sensors.

Morris, K. A.↗

NASA NTRS1998

Fault Accommodation in Control of Flexible Systems

New synthesis techniques for the design of fault accommodating controllers for flexible systems are developed. Three robust control design strategies, static dissipative, dynamic dissipative and mu-synthesis, are used in the approach. The approach provides techniques for designing controllers that maximize, in some sense, the tolerance of the closed-loop system against faults in actuators and sensors, while guaranteeing performance robustness at a specified performance level, measured in terms of the proximity of the closed-loop poles to the imaginary axis (the degree of stability). For dissipative control designs, nonlinear programming is employed to synthesize the controllers, whereas in mu-synthesis, the traditional D-K iteration is used. To demonstrate the feasibility of the proposed techniques, they are applied to the control design of a structural model of a flexible laboratory test structure.

Maghami, Peiman G.↗

NASA NTRS1989

Overview of computational control research at UT Austin

An overview of current research activities at UT Austin is presented to discuss certain technical issues in the following areas: (1) Computer-Aided Nonlinear Control Design: In this project, the describing function method is employed for the nonlinear control analysis and design of a flexible spacecraft equipped with pulse modulated reaction jets. INCA program has been enhanced to allow the numerical calculation of describing functions as well as the nonlinear limit cycle analysis capability in the frequency domain; (2) Robust Linear Quadratic Gaussian (LQG) Compensator Synthesis: Robust control design techniques and software tools are developed for flexible space structures with parameter uncertainty. In particular, an interactive, robust multivariable control design capability is being developed for INCA program; and (3) LQR-Based Autonomous Control System for the Space Station: In this project, real time implementation of LQR-based autonomous control system is investigated for the space station with time-varying inertias and with significant multibody dynamic interactions.

Bong, Wie↗

NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

Hageman, Jacob↗

NASA NTRS2003

Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

Hageman, Jacob J.↗

NASA NTRS2005

Reliability-Based Control Design for Uncertain Systems

This paper presents a robust control design methodology for systems with probabilistic parametric uncertainty. Control design is carried out by solving a reliability-based multi-objective optimization problem where the probability of violating design requirements is minimized. Simultaneously, failure domains are optimally enlarged to enable global improvements in the closed-loop performance. To enable an efficient numerical implementation, a hybrid approach for estimating reliability metrics is developed. This approach, which integrates deterministic sampling and asymptotic approximations, greatly reduces the numerical burden associated with complex probabilistic computations without compromising the accuracy of the results. Examples using output-feedback and full-state feedback with state estimation are used to demonstrate the ideas proposed.

Crespo, Luis G.↗

NASA NTRS1998

Microgravity Vibration Control and Civil Applications

Controlling vibration of structures is essential for both space structures as well as terrestrial structures. Due to the ambient acceleration levels anticipated for the International Space Station, active vibration isolation is required to provide a quiescent acceleration environment for many science experiments. An overview is given of systems developed and flight tested in orbit for microgravity vibration isolation. Technology developed for vibration control of flexible space structures may also be applied to control of terrestrial structures such as buildings and bridges subject to wind loading or earthquake excitation. Recent developments in modern robust control for flexible space structures are shown to provide good structural vibration control while maintaining robustness to model uncertainties. Results of a mixed H-2/H-infinity control design are provided for a benchmark problem in structural control for earthquake resistant buildings.

Whorton, Mark Stephen↗

DOE OSTI2025

Experimental test of model predictive control in a variable air volume system

Model predictive control (MPC) has been widely studied as a promising approach for improving energy efficiency and operational flexibility in buildings, yet its real-world performance for commercial variable air volume (VAV) systems remains insufficiently characterized. In particular, the impacts of model mismatch on control robustness, real-time computational burden, and device-level operation are rarely evaluated using long-term field data. Here, this study presents a comprehensive experimental evaluation of MPC applied to a full-scale VAV system in Oak Ridge National Laboratory’s Flexible Research Platform-2 building with constant cooling/heating temperature setpoints and no occupancy. The study offers three key advantages over existing work: (1) it uses a representative building in a full-scale experimental test, capturing realistic system dynamics and complexity; (2) it evaluates a relatively sophisticated MPC formulation using two different optimization solvers (Gurobi and PSO), fully accounting for computational complexity and methodological diversity; and (3) it systematically assesses potential negative impacts on various building devices, benchmark against a well-established baseline, ASHRAE Guideline 36 (G36). To isolate zone- and air-handling-unit–level supervisory control effects, the supply fan was operated with a fixed static pressure setpoint under all strategies, and the trim-and-response static pressure reset in G36 was not enabled. Results show that MPC maintained thermal comfort while improving energy efficiency. Abrupt solar radiation variations degraded performance. Computation times ranged from ∼1 s (Gurobi) to ∼ 70 s (PSO). Compared with G36, MPC achieves 33% energy savings and reduces median reheat coil output by approximately a factor of 5–10 for a representative cooling day under matched weather conditions. However, it increases the maximum discomfort deviation from 0.5 to 1°C and results in a 32% increase in staging frequency. In addition, PSO-based MPC introduced damper oscillations, also affecting actuator longevity.

ASHRAE guideline 36↗

NASA NTRS2007

LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

algorithms↗