Simultaneous control of temperature and humidity in a confined space. II - Feedback control synthesis via classical control theory.
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Recently, a robust and practical adaptive control scheme for launch vehicles [ [1] has been introduced. It augments a classical controller with a real-time loop-gain adaptation, and it is therefore called Adaptive Augmentation Control (AAC). The loop-gain will be increased from the nominal design when the tracking error between the (filtered) output and the (filtered) command trajectory is large; whereas it will be decreased when excitation of flex or sloshing modes are detected. There is a need to determine the range and rate of the loop-gain adaptation in order to retain (exponential) stability, which is critical in vehicle operation, and to develop some theoretically based heuristic tuning methods for the adaptive law gain parameters. The classical launch vehicle flight controller design technics are based on gain-scheduling, whereby the launch vehicle dynamics model is linearized at selected operating points along the nominal tracking command trajectory, and Linear Time-Invariant (LTI) controller design techniques are employed to ensure asymptotic stability of the tracking error dynamics, typically by meeting some prescribed Gain Margin (GM) and Phase Margin (PM) specifications. The controller gains at the design points are then scheduled, tuned and sometimes interpolated to achieve good performance and stability robustness under external disturbances (e.g. winds) and structural perturbations (e.g. vehicle modeling errors). While the GM does give a bound for loop-gain variation without losing stability, it is for constant dispersions of the loop-gain because the GM is based on frequency-domain analysis, which is applicable only for LTI systems. The real-time adaptive loop-gain variation of the AAC effectively renders the closed-loop system a time-varying system, for which it is well-known that the LTI system stability criterion is neither necessary nor sufficient when applying to a Linear Time-Varying (LTV) system in a frozen-time fashion. Therefore, a generalized stability metric for time-varying loop=gain perturbations is needed for the AAC.
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Classical methods from optimal control theory are used in deriving general forms for neural network weights. The network learning or application task is encoded in a performance index of a general structure. Consequently, different instances of this performance index lead to special cases of weight rules, including some well-known forms. Comparisons are made with the outer product rule, spectral methods, and recurrent back-propagation. Simulation results and comparisons are presented.
AUTOCON is an automated computer-aided design tool for the synthesis and optimization of linear multivariable control systems based upon user-defined control parameter optimization. Violations in stability and performance requirements are computed from constraints on Single Input/Single Output (SISO) open- and closed-loop transfer function frequency responses, and from constraints on the singular-value frequency responses of Multiple Input/Multiple Output (MIMO) transfer functions, for all critical plant variations. Optimum nonlinear programming algorithms are used in the search for local constrained solutions in which violations in stability and performance are caused either to vanish or be minimized for a proper selection of the control parameters. Classical control system stability and performance design can, in this way, be combined with modern multivariable robustness methods to offer general frequency response loop-shaping via a computer-aided design tool. Complete Nichols, Nyquist, Bode, singular-value Bode magnitude and transient response plots are produced, including user-defined boundary responses. AUTOCON is used to synthesize and optimize the lateral/directional flight control system for a typical high-performance aircraft.
The p-integrator is a digital controller that learns from previous experience executing a repetitive command in order to improve its performance in future repetitions. This class of repetitive controller is an analog of the integral controller of classical control theory. This paper addresses several issues related to the performance of the p-integrator. This is followed by a derivation that will demonstrate the repetitive disturbance rejection capabilities of the controller. The effect of the tracking performance in practical implementations with prefiltering and postfiltering devices are studied - using an exact and approximate formulation. Relations showing the actual system response during steady state operation with these filters are derived. The results are then extended to include the tracking error performance of systems subjected to repetitive disturbances. Finally, the system response between sample times is developed.
The goal is to develop an approach to automating the alignment and adjustment of optical measurement, visualization, inspection, and control systems. Classical controls, expert systems, and neural networks are three approaches to automating the alignment of an optical system. Neural networks were chosen for this project and the judgements that led to this decision are presented. Neural networks were used to automate the alignment of the ubiquitous laser-beam-smoothing spatial filter. The results and future plans of the project are presented.
An advanced design and implementation of a Control Architecture for Long Range Autonomous Planetary Rovers is presented using a hierarchical top-down task decomposition, and the common structure of each design is presented based on feedback control theory. Graphical programming is presented as a common intuitive language for the design when a large design team is composed of managers, architecture designers, engineers, programmers, and maintenance personnel. The whole design of the control architecture consists in the classic control concepts of cyclic data processing and event-driven reaction to achieve all the reasoning and behaviors needed. For this purpose, a commercial graphical tool is presented that includes the mentioned control capabilities. Messages queues are used for inter-communication among control functions, allowing Artificial Intelligence (AI) reasoning techniques based on queue manipulation. Experimental results show a highly autonomous control system running in real time on top the JPL micro-rover Rocky 7 controlling simultaneously several robotic devices. This paper validates the sinergy between Artificial Intelligence and classic control concepts in having in advanced Control Architecture for Long Range Autonomous Planetary Rovers.
A method by which modern and classical control theory techniques may be integrated in a synergistic fashion and used in the design of practical flight control systems is presented. A general procedure is developed, and several illustrative examples are included. Emphasis is placed not only on the synthesis of the design, but on the assessment of the results as well. The first step is to establish the differences, distinguishing characteristics and connections between the modern and classical control theory approaches. Ultimately, this uncovers a relationship between bandwidth goals familiar in classical control and cost function weights in the equivalent optimal system. In order to obtain a practical optimal solution, it is also necessary to formulate the problem very carefully, and each choice of state, measurement and output variable must be judiciously considered. Once design goals are established and problem formulation completed, the control system is synthesized in a straightforward manner. Three steps are involved: filter-observer solution, regulator solution, and the combination of those two into the controller. Assessment of the controller permits and examination and expansion of the synthesis results.
NASA's baseline Space Launch System (SLS) ight control system (FCS) includes an adaptive augmenting control (AAC) portion in addition to the ight-heritage nominal classical controller. The AAC algorithm is intended to improve the robustness and performance of the classical controller. Over the past several years, the AAC algorithm developed at NASA Marshall Space Flight Center (MSFC) has matured significantly through extensive simulation, rigorous analytical proofs, and a series of successful ight tests on a F18 aircraft. This study was part of a SLS program and NASA Engineering and Safety Center (NESC) joint e ort to further increase the confidence level of the AAC algorithm by demonstrating its key functionalities on a classroom type of example, the pole-cart sys- tem, at the NASA Langley Research Center (LaRC) dynamics and control laboratory. The fundamental dynamics behind balancing an inverted pendulum is similar to controlling an aerodynamically unstable rocket. Both systems are inherently open-loop unstable and requires feedback control for attitude stabilization. The principles behind the AAC algorithm is applicable to a wide range of conditionally stable dynamical systems. Hence, the outcomes from this simple and inexpensive exercise has provided the SLS program with additional confidence into the AAC design, operation, robustness, and application.
The relationships between elevon deflection and static margin using elements from static and dynamic stability and control and from classical control theory are emphasized. Expressions are derived and presented for calculating elevon deflections required to trim the vehicle in lg straight-and-level flight and to perform specified longitudinal and lateral maneuvers. Applications of this methodology are made at several flight conditions for the ARW-2 wing. On the basis of these applications, it appears possible to trim and maneuver the vehicle with the existing elevons at -15% static margin.
In July of 1994 the Kuiper Airborne Observatory's (KAO) Telescope Stabilization System (TSS) was upgraded to meet performance goals necessary to view the Shoemaker-Levy 9 comet collision with Jupiter. The KAO is a modified C-141 Aircraft supporting a 36 inch Infrared telescope used to gather and analyze astronomical data. Before the upgrade, the TSS exhibited approximately a 10 arc-second resolution pointing accuracy. The majority of the inaccuracy was attributable to aircraft vibration and wind buffeting entering through the aircraft's telescope door opening; in other words, the TSS was overly sensitive to external disturbances. Because of power limitations and noise requirements, improving the pointing accuracy of the telescope required more sophistication than simply raising the bandwidth as some classical control strategies might suggest. Instead, relationships were developed between the disturbance sensitivity and closed loop transfer functions. These relationships suggested that employing velocity feedback along with an increase in current loop gain would dramatically improve the pointing resolution of the TSS by decreasing the control system's sensitivity to external disturbances. With the implementation of some classical control techniques and the above philosophy, the KAO's TSS's resolution was improved to approximately 2-3 arc-seconds.
It is expected that space systems for the future will evolve to structures of unprecedented size with associated extreme control requirements. A method is necessary that is sufficiently general to initiate stable control of a vehicle and subsequently learn the true nature of the structure. It is suggested that a suitable constructed expert system (ES) would be capable of learning by appending observations to a knowledge base. To verify that an ES can control a large space structure, numerical simulations of a simple structure subjected to periodic vibrations and the performance of a classical controllers were performed. The ES was then exercised to show its ability to truthfully mimic nominal control and to demonstrate its superiority to the classical controller, given sensor failures. An ES generating software package, The Intelligent Machine Model, was employed. It uses the pattern matching technique. Results of this program are discussed.
The use of AI methods to monitor and control semiconductor fabrication in a state-of-the-art manufacturing environment called the Rapid Thermal Multiprocessor is described. Semiconductor fabrication involves many complex processing steps with limited opportunities to measure process and product properties. By applying additional process and product knowledge to that limited data, AI methods augment classical control methods by detecting abnormalities and trends, predicting failures, diagnosing, planning corrective action sequences, explaining diagnoses or predictions, and reacting to anomalous conditions that classical control systems typically would not correct. Research methodology and issues are discussed, and two diagnosis scenarios are examined.
Theoretical and experimental results are obtained in the investigation of a minimum energy control problem associated with a class of electrically driven vehicles. Analytical results are obtained by making several justifiable approximations in the dynamical equations of the plant, the performance index of which is related to the minimization of system energy consumption for any required control action. The control problem of interest is simplified and solved by using Bellman's invariant imbedding technique. The optimality of the resulting control law is compared with those of the bang-bang control law, which is the exact solution for the case of speed-setting control action, and the classical control laws under identical conditions. A stochastic optimization problem is then formulated and solved by using Bellman's dynamic programming technique. The resulting control law is mechanized for a particular vehicle configuration which is simulated in the laboratory.
The Space Station polar platform will carry a variety of earth observation instruments for NASA and the National Oceanic and Atmospheric Administration. In this paper, the asymmetrical platform is modeled as three connected rigid bodies. A generalized angular momentum equation is employed to derive the rotational equations of motion. These equations are linearized and used for preliminary sizing of control devices using a classical control approach. Two control systems are considered to stabilize the platform and satisfy the pointing requirements. The first system is composed of a single variable-speed, double-gimbaled momentum wheel and the second consists of three-reaction wheels. The performance of each system is assessed using a linear optimal control approach.
Methodology accommodates variety of control structures and design techniques. In methodology for combined stochastic feedforward/feedback control, main objectives of feedforward and feedback control laws seen clearly. Inclusion of error-integral feedback, dynamic compensation, rate-command control structure, and like integral element of methodology. Another advantage of methodology flexibility to develop variety of techniques for design of feedback control with arbitrary structures to obtain feedback controller: includes stochastic output feedback, multiconfiguration control, decentralized control, or frequency and classical control methods. Control modes of system include capture and tracking of localizer and glideslope, crab, decrab, and flare. By use of recommended incremental implementation, control laws simulated on digital computer and connected with nonlinear digital simulation of aircraft and its systems.
An integrated flight/aeroelastic control law is developed analytically for a hypothetical large supersonic transport aircraft in which the first aeroelastic mode frequency of the fuselage (6 rad/sec) is near the short-period mode (2 rad/sec). The approach employed is based on a linear-quadratic-regulator (LQR) formulation (yielding model-following state-feedback gains), followed by asymptotic loop-transfer recovery of LQR robustness (to produce an output-feedback control law). The derivation is outlined, and numerical results comparing the performance and multivariate stability robustness of the present controller with those of a classical controller are presented in graphs. The two controllers are shown to have similar characteristics, even with respect to the sources of limitations on robustness.