Analysis and Design of Network-Based Control Systems with Binary Modulation

The object of this work is to present computer programs, written using Turbo Pascal version 5.5, which can be utilized for the analysis and design of control systems as required for undergraduate courses. The programs have been designed so that the student uses the computer as a tool for reducing computational drudgery and not as a substitute for engineering analysis. The software, which can be used on an IBM Personal Computer and compatibles, will be made freely available to interested educators. >

A survey is presented of the environment, the hardware, and the system, user interface, and applications software that can make engineering workstations effective tools for the analysis and design of control systems. The hardware discussion covers processors, memory, networks, and diskless workstations. Operating systems, distributed file systems, and software for developing user interfaces are highlighted, along with the most widely used applications packages for control system engineering.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Stability is one of the most important concepts of analysis and design of control systems. Stability analysis of fuzzy control systems has been difficult since fuzzy systems are essentially nonlinear systems. Recently, stability analysis techniques [l]-[6] [13]-[16] based on nonlinear stability theory have been reported. Langari and Tomizuka [2, 6] analyzed stability of fuzzy linguistic control systems. Kitamura [3] realized stability analysis of fuzzy control systems by using extended circle criterion. Farinwata [5] and Kato [14] utilized phase plane analysis type of techniques, tanaka and co-authors [1,3,4] applied Lyapunov approach to fuzzy control systems and recently developed LMI-based designs [10, 17, 28-37], where LMI stands for nonlinear matrix inequality.

A number of problems in the analysis and design of control systems may be reformulated as the problem Of minimizing the largest generalized eigenvalue of a pair of symmetric matrices which depend affinely on the decision variables, subject to constraints that are linear matrix inequalities. For these generalized eigenvalue problems, there exist numerical algorithms that are guaranteed to be globally convergent, have polynomial worst-case complexity, and stopping criteria that guarantee desired accuracy. In this paper, we show how a number of important interpolation problems in control may be solved via generalized eigenvalue minimization.

While linear covariance analysis is widely used for navigation system design and analysis, it is often overlooked as a tool for closed-loop guidance navigation and control (GN&amp;C) system design and analysis. This article presents an overview of the techniques and methods required to develop a linear covariance analysis tool for a close-loop GN&amp;C system. Then, using a simple nonlinear closed-loop GN&amp;C problem as a guide, the capabilities of linear covariance analysis for the design and analysis of closed-loop systems are demonstrated. It is shown that linear covariance can be accurately applied to a closed-loop system with time-to-go guidance, dead-reckoning navigation, and a Kalman filter for state estimation. The accuracy and efficiency of linear covariance analysis is shown by direct comparison to Monte Carlo analysis results, and the value of linear covariance analysis is highlighted by presenting several analysis capabilities that are often required in the design and analysis of closed-loop GN&amp;C systems. It is also shown how the efficiency of linear covariance enables new design methodologies, one of which is presented in this article, that would otherwise be prohibitive with Monte Carlo analysis.

Positive force/torque feedback (PFF) can compensate for the detrimental effects of load dynamics and exogenous force/torque disturbances on the tracking performance of motion control systems. With real world nonideal force/torque sensors/actuators, a PFF controller can be designed to accommodate the sensor's/actuator's dynamics and to minimize the impact of load dynamics or external force/torque disturbances upon the motion tracking loop. This brief shows that PFF is an effective method for many practical control problems. It is also shown that the analysis and design of motion control systems with PFF can be put into the /spl mu/ framework and, thus, the tracking controller and the force/torque feedback controller can be synthesized simultaneously using commercially available /spl mu/ tools. Some practical issues, which are independent of design methods, such as the robust stability (RS) of the PFF loop and the bandwidth requirements for the PFF controller, are also discussed. It is concluded that, outside the system's effective tracking bandwidth, it is not wise to use model-based perfect PFF for a big load due to the lack of robustness of the PFF loop against the actuator uncertainties. Experimental results with a UW hexapod strut show that the /spl mu/ design method for PFF is promising.

In this second part of the paper on the analysis and design of uncertain fuzzy control systems, we present a controller design method for a class of uncertain fuzzy control systems. This class of systems can be represented by the discrete-time uncertain fuzzy models discussed in Part I. Since this model representation considers system uncertainties and system disturbance, the controller design problem for this kind of model becomes a H/sub /spl infin// control problem for a nonlinear uncertain system. A new stability analysis and control system design approach are then developed for this kind of uncertain fuzzy model. Furthermore, a constructive algorithm is developed to obtain the H/sub /spl infin// feedback control law.

To help along the analysis and design of nonlinear control systems the NONLINCON package, an acronym for nonlinear control, was developed. This note addresses the usefulness of symbolic computation, and of the NONLINCON package in particular, for the symbolic analysis and design of nonlinear control systems. The symbolic computation program MAPLE is used as computing substratum. Textbook problems show that the NONLINCON package can be used successfully. A larger scale problem is too complex, however, to be solved with the current versions of NONLINCON and MAPLE. The conclusion is that symbolic computation is a viable approach for textbook problems, but not yet for more complex ones. >

Design, Analysis and Real-time Implementation of Networked Predictive Control Systems

The INteractive Controls Analysis (INCA) program was developed at the Goddard Space Flight Center to provide a user friendly efficient environment for the design and analysis of linear control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. Moreover, the results of the analytic tools imbedded in INCA have been flight proven with at least three currently orbiting spacecraft. This paper describes the INCA program and illustrates, using a flight proven example, how the package can perform complex design analyses with relative ease.

This paper presents a scheduling method for network-based control systems with three kinds of data (periodic data, sporadic data, and messages). As a basic parameter for the scheduling method for network-based control systems, a maximum allowable delay bound is used, which guarantees stability of network-based control systems and is derived from characteristics of the given plant using the Lyapunov theorem. The presented scheduling method for network-based control systems can adjust the sampling period as small as possible and allocate bandwidth of network for three kinds of data. In addition to those, the presented method guarantees real-time transmission of sporadic and periodic data, and minimum transmission for messages. The proposed method is shown to be useful by an example.

The paper presents a method of engineering analysis and design for complex control systems, the time-domain infinite-matrix method. The formulation of the infinite matrix follows from the convolution summation of sampled-data systems. The mathematical basis of the time-domain matrix formulation is related to the applicable concepts of infinite matrices and sequence spaces. The method is applicable to both continuous-data and sampled-data systems. For continuous systems it is necessary to introduce a fictitious sampler and `hold? of sufficient sampling rate to effect an accurate approximation. The time-domain-matrix method is presented and illustrated as a method of analysis and design of linear, non-linear, and time-varying systems of the continuous-data or sampled-data class. The investigation of non-linear systems is greatly simplified by the time-domain approach. Multi-loop systems may be investigated, and the signals at intermediate points throughout the loops are readily available. Also, systems with multiple non-linearities may be investigated. Two methods of design of a discrete compensator for a sampled-data system are presented. These methods are accomplished directly in the time domain and allow for a compromise of specifications in the time domain. In one design procedure the response between sampling instants is also accounted for. The time-domain-matrix method may be readily programmed on a digital computer, and therefore provides a rapid analysis and design technique.

Computer aided modeling, analysis and design of control systems - A perspective (Reprint; ibid, vol. 3, n. 2 83) [25 Years Ago

This paper is concerned with the development of fully Autonomous Underwater Vehicles (AUVs) for deep ocean scientific and commercial applications. Specifically, it is regarding the design of automatic feedback control systems for AUV attitude control. The design method used in this paper formulates the AUV attitude control problem within the framework of the Optimal Control Theory. This approach determines an optimal control law that keeps the state of a dynamic system near a constant reference state while minimizing a quadratic cost functional. This study has developed four optimal control laws based on different performance criteria. First, the standard Linear Quadratic performance index was used to obtain a baseline control design. The LQR control law has been found to be undesirable because it resulted in a closed-loop system with fast and slow dynamics. In order to place the closed-loop eigenvalues close to each other, a pole placement algorithm has been used in connection with the LQR performance index. Two control laws have been designed based on this approach. Finally, a fourth design has been obtained by using a Hilbert Space Norm (also known as H/sub 2/ Norm or Root-Mean-Square) performance index. The main reason for designing a control law based on H/sub 2/ Norm performance index was to minimize the fuel consumption of the AUV for longer endurance. The stability and performance characteristics of the four optimal control laws have been analyzed via computer simulation and frequency response studies. All the closed-loop AUV systems exhibited overdamped response without any overshoot. Because the yaw and pitch channels have right hand zeros indicating the controllers have finite gain margins in these two channels. Hence, stability robustness of the AUV controllers can not be ignored and must be the subject of more detailed investigation in the future.

Computer-aided design of control systems using nonparametric models