Discrete dynamics analysis for nonlinear collocated multivariable mass-damper-spring intelligent mechanical vibration systems

A new time-discretization method for the development of nonlinear collocated multivariable mass-damper-spring (MDS) intelligent mechanical vibration systems is proposed. It is based on the Runge-Kutta series expansion method and zero-order hold assumption. In this paper, we show that the mathematical structure of the new discretization scheme is explored and characterized in order to represent the discrete dynamics properties for nonlinear collocated multivariable MDS intelligent mechanical vibration systems. In particular, the decent effects of the time-discretization method on key properties of nonlinear multivariable MDS mechanical vibration systems, such as discrete zero dynamics and asymptotic stability, are examined. The resulting time-discretization provides discrete dynamics behavior for nonlinear MDS mechanical vibration systems, which enabling the application of existing controller design techniques. The ideas presented here generalize well-known results from the linear case to nonlinear plants

Robust multivariable controller design for flexible spacecraft

Large, flexible spacecraft are typically characterized by a large number of significant elastic modes with very small inherent damping, low, closely spaced natural frequencies, and the lack of accurate knowledge of the structural parameters. Summarized here is some recent research on the design of robust controllers for such spacecraft, which will maintain stability, and possible performance, despite these problems. Two types of controllers are considered, the first being the linear-quadratic-Gaussian-(LQG)-type. The second type utilizes output feedback using collocated sensors and actuators. The problem of designing robust LQG-type controllers using the frequency domain loop transfer recovery (LTR) method is considered, and the method is applied to a large antenna model. Analytical results regarding the regions of stability for LQG-type controllers in the presence of actuator nonlinearities are also presented. The results obtained for the large antenna indicate that the LQG/LTR method is a promising approach for control system design for flexible spacecraft. For the second type of controllers (collocated controllers), it is proved that the stability is maintained in the presence of certain commonly encountered nonlinearities and first-order actuator dynamics. These results indicate that collocated controllers are good candidates for robust control in situations where model errors are large

A variational approach to linear control structure problems

Imperial Users onl

Approximate identity operator in linear continuous and sampled-data servomechanisms

Call number: LD2668 .R4 1964 R24

Control system design using artificial intelligence

Includes bibliography.Successful multivariable control system design demands knowledge, skill and creativity of the designer. The goal of the research described in this dissertation was to investigate, implement, and evaluate methods by which artificial intelligence techniques, in a broad sense, may be used in a design system to assist the user. An intelligent, interactive, control system design tool has been developed to fulfil this aim. The design tool comprises two main components; an expert system on the upper level, and a powerful CACSD package on the lower level. The expert system has been constructed to assist and guide the designer in using the facilities provided by the underlying CACSD package. Unlike other expert systems, the user is also aided in formulating and refining a comprehensive and achievable design specification, and in dealing with conflicts which may arise within this specification. The assistance is aimed at both novice and experienced designers. The CACSD package includes a synthesis program which attempts to find a controller that satisfies the design specification. The synthesis program is based upon a recent factorization theory approach, where the linear multivariable control system design problem is translated into, and techniques efficiency solved as, a quadratic programming problem, which significantly improve the time and space of this method have been developed, making it practical to solve substantial multivariable design problems using only a microcomputer. The design system has been used by students at the University of Cape Town. Designs produced using the expert system tool are compared against those produced using classical design methods

Discrete dynamics analysis for nonlinear collocated multivariable mass-damper-spring intelligent mechanical vibration systems

A new time-discretization method for the development of nonlinear collocated multivariable mass-damper-spring (MDS) intelligent mechanical vibration systems is proposed. It is based on the Runge-Kutta series expansion method and zero-order hold assumption. In this paper, we show that the mathematical structure of the new discretization scheme is explored and characterized in order to represent the discrete dynamics properties for nonlinear collocated multivariable MDS intelligent mechanical vibration systems. In particular, the decent effects of the time-discretization method on key properties of nonlinear multivariable MDS mechanical vibration systems, such as discrete zero dynamics and asymptotic stability, are examined. The resulting time-discretization provides discrete dynamics behavior for nonlinear MDS mechanical vibration systems, which enabling the application of existing controller design techniques. The ideas presented here generalize well-known results from the linear case to nonlinear plants

Nonlinear and sampled data control with application to power systems

Sampled data systems have come into practical importance for a variety of reasons. The earliest of these had primarily to do with economy of design. A more recent surge of interest was due to increase utilization of digital computers as controllers in feedback systems. This thesis contributes some control design for a class of nonlinear system exhibition linear output. The solution of several nonlinear control problems required the cancellation of some intrinsic dynamics (so-called zero dynamics) of the plant under feedback. It results that the so-dened control will ensure stability in closed-loop if and only if the dynamics to cancel are stable. What if those dynamics are unstable? Classical control strategies through inversion might solve the problem while making the closed loop system unstable. This thesis aims to introduce a solution for such a problem. The main idea behind our work is to stabilize the nonminimum phase system in continuous- time and undersampling using zero dynamics concept. The overall work in this thesis is divided into two parts. In Part I, we introduce a feedback control designs for the input-output stabilization and the Disturbance Decoupling problems of Single Input Single Output nonlinear systems. A case study is presented, to illustrate an engineering application of results. Part II illustrates the results obtained based on the Articial Intelligent Systems in power system machines. We note that even though the use of some of the AI techniques such as Fuzzy Logic and Neural Network does not require the computation of the model of the application, but it will still suer from some drawbacks especially regarding the implementation in practical applications. An alternative used approach is to use control techniques such as PID in the approximated linear model. This design is very well known to be used, but it does not take into account the non-linearity of the model. In fact, it seems that control design that is based on nonlinear control provide better performances