Control concepts for the alleviation of windshears and gusts

Automatic control system design methods for gust and shear alleviation were studied. It is shown that automatic gust/shear alleviation systems can be quite effective if both throttle and elevator are used in harmony to produce the forces and moments required to counter the effects of the windshear. Regulation with respect to ground speed or airspeed results in very similar system designs. The application of the NASA total energy probe in the detection of windshear and criteria for alleviation is considered. The theory and application of robust output observers is extended. Design examples show how implementation of the control laws can be accomplished using observers, and thereby resulting in less complex control system configurations

A robust multivariable control for an electropneumatic system using backstepping design

International audienceDuring the last twenty years, the parallel development of pneumatic systems and control theory has lead to the implementation of modern control laws in pneumatic devices. This paper deals with the robust control problem of a pneumatic actuator subjected to mass flow leakage inside the servodistributor and load disturbances. The control strategy is based on backstepping design. For this, backstepping is presented in an informal setting. The nonlinear model of the electropneumatic system is presented. This one is transformed to be nonlinear affine model and a coordinate transformation is then related to make possible the implementation of the nonlinear controller. Control laws are developed using backstepping design to control position and pressure. The robustness visa -vis modeling errors and some unknown terms is proved. Finally, the experiment results are presented and discussed

Design and implementation of robust decentralized control laws for the ACES structure at Marshall Space Flight Center

Many large space system concepts will require active vibration control to satisfy critical performance requirements such as line-of-sight accuracy. In order for these concepts to become operational it is imperative that the benefits of active vibration control be practically demonstrated in ground based experiments. The results of the experiment successfully demonstrate active vibration control for a flexible structure. The testbed is the Active Control Technique Evaluation for Spacecraft (ACES) structure at NASA Marshall Space Flight Center. The ACES structure is dynamically traceable to future space systems and especially allows the study of line-of-sight control issues

Depth control of autonomous underwater vehicle using discrete time sliding mode controller

This study presents a Discrete Time Sliding Mode Controller (DSMC) application on depth plane of Autonomous Underwater Vehicle (AUV). The main contribution on this work is an implementation of DSMC on NSP AUV II. Sliding Mode Control (SMC) is a robust type of controller and certainly suitable for controlling AUV in the presence of environmental disturbances and uncertainties. DSMC preserves the properties of standard SMC. Linearized dynamic model of NSP AUV II is used in the numerical simulations. Discrete Proportional Integral Derivative (PID) controllers are used for performance comparative analysis. The design of discrete PID and DSMC for NSP AUV II depth is described. Comparative study between the control laws is presented. The simulated results illustrate strong robustness, improve performance and satisfactory stability of DSMC as compared to discrete-time PID controller

Flutter suppression control law synthesis for the Active Flexible Wing model

The Active Flexible Wing Project is a collaborative effort between the NASA Langley Research Center and Rockwell International. The objectives are the validation of methodologies associated with mathematical modeling, flutter suppression control law development and digital implementation of the control system for application to flexible aircraft. A flutter suppression control law synthesis for this project is described. The state-space mathematical model used for the synthesis included ten flexible modes, four control surface modes and rational function approximation of the doublet-lattice unsteady aerodynamics. The design steps involved developing the full-order optimal control laws, reducing the order of the control law, and optimizing the reduced-order control law in both the continuous and the discrete domains to minimize stochastic response. System robustness was improved using singular value constraints. An 8th order robust control law was designed to increase the symmetric flutter dynamic pressure by 100 percent. Preliminary results are provided and experiences gained are discussed

Collocated versus Non-collocated Multivariable Control for Flexible Structure

Future space structures have many closely spaced, lightly damped natural frequencies throughout the frequency domain. To achieve desired performance objectives, a number of these modes must actively be controlled. For control, a combination of collocated and noncollocated sensors and actuators will be employed. The control designs will be formulated based on models which have inaccuracies due to unmodeled dynamics, and variations in damping levels, natural frequencies and mode shapes. Therefore, along with achieving the performance objectives, the control design must be robust to a variety of uncertainty. This paper focuses on the benefits and limitations associated with multivariable control design using noncollocated versus collocated sensors and actuators. We address the question of whether performance is restricted due to the noncollocation of the sensors and actuators or the uncertainty associated with modeling of the flexible structures. Control laws are formulated based on models of the system and evaluated analytically and experimentally. Results of implementation of these control laws on the Caltech flexible structure are presented

EASILY VERIFIABLE CONTROLLER DESIGN WITH APPLICATION TO AUTOMOTIVE POWERTRAINS

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is mainly created due to (i) digital implementation of controller software that introduces sampling and quantization imprecisions via analog-to-digital conversion (ADC), and (ii) uncertainties in the modeled plant’s dynamics, which directly propagate through the controller structure. The failure to identify and handle these implementation and model uncertainties results in undesirable controller performance and costly iterative loops for completing the controller verification and validation (V&V) process. This PhD dissertation develops a novel theoretical framework to design controllers that are robust to implementation imprecision and uncertainties within the models. The proposed control framework is generic and applicable to a wide range of nonlinear control systems. The final outcome from this study is an uncertainty/imprecisions adaptive, easily verifiable, and robust control theory framework that minimizes V&V iterations in the design of complex nonlinear control systems. The concept of sliding mode controls (SMC) is used in this study as the baseline to construct an easily verifiable model-based controller design framework. SMC is a robust and computationally efficient controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. The SMC structure allows for further modification to improve the controller robustness against implementation imprecisions, and compensate for the uncertainties within the plant model. First, the conventional continuous-time SMC design is improved by: (i) developing a reduced-order controller based on a novel model order reduction technique. The reduced order SMC shows better performance, since it uses a balanced realization form of the plant model and reduces the destructive internal interaction among different states of the system. (ii) developing an uncertainty-adaptive SMC with improved robustness against implementation imprecisions. Second, the continuous-time SMC design is converted to a discrete-time SMC (DSMC). The baseline first order DSMC structure is improved by: (i) inclusion of the ADC imprecisions knowledge via a generic sampling and quantization uncertainty prediction mechanism which enables higher robustness against implementation imprecisions, (ii) deriving the adaptation laws via a Lyapunov stability analysis to overcome uncertainties within the plant model, and (iii) developing a second order adaptive DSMC with predicted ADC imprecisions, which provides faster and more robust performance under modeling and implementation imprecisions, in comparison with the first order DSMC. The developed control theories from this PhD dissertation have been evaluated in real-time for two automotive powertrain case studies, including highly nonlinear combustion engine, and linear DC motor control problems. Moreover, the DSMC with predicted ADC imprecisions is experimentally tested and verified on an electronic air throttle body testbed for model-based position tracking purpose

Robust Model Predictive Control via Scenario Optimization

This paper discusses a novel probabilistic approach for the design of robust model predictive control (MPC) laws for discrete-time linear systems affected by parametric uncertainty and additive disturbances. The proposed technique is based on the iterated solution, at each step, of a finite-horizon optimal control problem (FHOCP) that takes into account a suitable number of randomly extracted scenarios of uncertainty and disturbances, followed by a specific command selection rule implemented in a receding horizon fashion. The scenario FHOCP is always convex, also when the uncertain parameters and disturbance belong to non-convex sets, and irrespective of how the model uncertainty influences the system's matrices. Moreover, the computational complexity of the proposed approach does not depend on the uncertainty/disturbance dimensions, and scales quadratically with the control horizon. The main result in this paper is related to the analysis of the closed loop system under receding-horizon implementation of the scenario FHOCP, and essentially states that the devised control law guarantees constraint satisfaction at each step with some a-priori assigned probability p, while the system's state reaches the target set either asymptotically, or in finite time with probability at least p. The proposed method may be a valid alternative when other existing techniques, either deterministic or stochastic, are not directly usable due to excessive conservatism or to numerical intractability caused by lack of convexity of the robust or chance-constrained optimization problem.Comment: This manuscript is a preprint of a paper accepted for publication in the IEEE Transactions on Automatic Control, with DOI: 10.1109/TAC.2012.2203054, and is subject to IEEE copyright. The copy of record will be available at http://ieeexplore.ieee.or

Incorporation of Defined Quality Attributes into Solutions Based on Service-Oriented Architecture

The shift of the Service-Oriented Architecture (SOA) to the\ud Information Technology, its use and implementation enables the feature of having more robust system offered to the Service Industry. This paper will present enhancement efforts to software platform solution with including quality\ud attributes and will describe the design of the Quality Service-Oriented Architecture. A well known branch of the service industry market in the US is the Pest Control Services which should be part of every single physical object in the USA. There are many Service-Oriented Platforms that handle the Pest Control Industry and our target in this article will be to describe a Quality Oriented and Service-Oriented Design for the Pest Control Industry, which will meet the criteria and the needs of the industry as well to meet all laws requirements to each\ud US state related to the pest control policies and rules. Most important is that the presented software platform provides competitive edge

Emergence of robust growth laws from optimal regulation of ribosome synthesis

Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large‐scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome‐wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply‐driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms.ISSN:1744-429