Showing papers on "MIMO published in 1990"

Feedforward/feedback control of multivariable nonlinear processes

Abstract: This paper concerns general MIMO nonlinear processes, whose dynamic behavior is described by a standard state-space model of arbitrary order, including measurable disturbances. The concept of relative order of an output with respect to an input, extended to include disturbance as well as manipulated inputs, is generalized in a MIMO context and it is used to obtain a characterization of the dynamic interactions among the input and the output variables. A synthesis formula is calculated for a feedforward/state feedback control law that completely eliminates the effect of the measurable disturbances on the process outputs and induces a linear behavior in the closed-loop system between the outputs and a set of reference inputs. The input/output stability and the degree of coupling in the closed-loop system are determined by appropriate choice of adjustable parameters. A MIMO linear controller with integral action completes the feedforward/feedback control structure. The developed control methodology is applied to a continuous polymerization reactor and its performance is evaluated through simulations.

Control channel structure for TDMA mobile radio systems

Abstract: A control channel structure for time-division multiple access (TDMA) mobile radio systems that must offer high system capacity and ISDN services is described. This structure overcomes the difficulties in random access efficiency and the application of a layered protocol. A functional channel definition and layered signaling frame format are described. Functional channel mapping on physical channels is then described. Several methods for the frequency assignment of control channels are compared from the points of radio channel efficiency and functional ability. The optimum method is determined. Mapping information is broadcast to realize the control channel structure needed. An improved random access scheme for the common control channel is proposed. This scheme obtains good throughput performance, and realizes the application of data link layer protocols. Thus, efficient and flexible signaling for radio link control and data services is achieved. >

Periodically time-varying two-dimensional systems

Abstract: A two-dimensional (2-D) single-input/single-output (SISO) system represented by a recursive linear difference equation with periodically varying coefficients is considered. An equivalent multiple-input/multiple-output (MIMO) time-invariant realisation is then derived, whose structure is suitable for VLSI realisation.

Design of G-interactor and its application to direct multivariable adaptive control

Abstract: In the design of multi-input multi-output (MIMO) adaptive control systems an interactor plays an important role. The derivation of the interactor requires system parameter values unless the interactor is in diagonal form. This is a considerable restriction when the interactor is applied to the adaptive control system design. In MIMO adaptive control many parameters must be estimated to achieve good tracking. Over-parametrization causes many problems, e.g. bad tracking performance and long calculation time of control input. A design of interactor, called a G-interactor, for unknown MIMO plants is given. It is applied to MIMO adaptive control systems. The G-interactor requires minimum information on the plant, such as the relative degree of each element of the plant transfer matrix. For this design a canonical form, called the G-canonical form, of a matrix A is investigated. The canonical form is also used to eliminate redundant parameters of MIMO adaptive control systems. Numerical examples show the effect...

Multi-input/multi-output sensitivity-points tuning

Abstract: An extension of sensitivity point tuning concepts to the self-tuning of multi-input/multi-output (MIMO) systems is presented, and implementation issues are discussed. The means of generating the sensitivities needed for tuning are developed in a matrix transfer function form. A block diagram interpretation is presented to illustrate the conceptual clarity of using MIMO sensitivity points. A self-tuning example is included to demonstrate the tuning characteristics of the technique. >

Modeling and identification of human musculoskeletal walking system

Abstract: Several methods are tested to identify the human musculoskeletal system both as a linear and nonlinear system. For the linear system approach, a MIMO (multiinput, multioutput) ARX (autoregressive with exogeneous inputs) model is first tested to get a rough estimation of the system structure and parameters. A general linear input-output MIMO model is then developed, and parameters are estimated by means of the prediction error identification method. Since the complex human musculoskeletal system is almost certainly a nonlinear system, nonlinear system identification is applied and polynomials are used to approximate the nonlinear system functions. For such a MIMO nonlinear system, the parameters to be estimated will number in the thousands or even millions, depending on the polynomial degrees used and the maximum orders of delays. To overcome such numerical difficulties, a forward-regression orthogonal method is used to select only the most significant terms and estimate the corresponding parameters. >

Towards a link between the factorization approach and MIMO robust adaptive control

Abstract: Based on a doubly coprime factorization theorem (Nett et al. 1984), an adaptive controller for discrete-time MIMO systems consisting of a not necessarily minimum-phase modelled part and fraction-type unmodelled dynamics, which are not necessarily stable, non-linear, and time-varying, is developed. Then, by virtue of a stability result, which is itself of interest, it is shown that the adaptive closed-loop system is BIBO stable even in the presence of small but not necessarily stable unmodelled dynamics and arbitrarily bounded initial conditions. Consequently, an initial but important step for applying the factorization approach to MIMO robust adaptive control is completely established.

Unification of some MIMO adaptive control algorithms and global convergence analysis

Abstract: This paper presents the general direct adaptive scheme which can control linear schastic multivariable systems with arbitrary interactor matrix For this scheme some MIMO adaptive control structures derived by specific approaches are special cases A proof of global convergence of this adaptive scheme using a modified least squares algorithms is also given

Implementation of SISO and MIMO Predictive Control for a Chemical Plant

Abstract: Adaptive control strategies have become an area of increasing interest with several implementations being reported on recent years. Among these strategies a very new one is the generalized predictive control. In this paper the predictive algorithm is evaluated for controlling a high-purity distillation column for load disturbance rejection and setpoint tracking, utilizing both SISO and MIMO configurations. The general results show that, although the process is a very complex one, these algorithms are specially robust and perform very well.

Robust Design of MIMO Feedback Systems Having an Uncertain Nonlinear MIMO Plant

Abstract: An n×n nonlinear multiple-input multiple-output system W is presented, known only to belong to a set (W). W is embedded in a two-degree-of-freedom feedback structure. (r) is a set of possible command inputs, where for each r (r) there is given a set of acceptable outputs {Ar}. The two degree of freedom have to be chosen so that in response to each command input r, the plant output y {Ar} for all W {W}. A design technique for solving this problem is presented which is based on the Horowitz design method for MIMO linear time invariant feedback systems. Sufficient conditions under which a solution is guaranteed are given. A design example is included.

MIMO servo design by the split control method

Abstract: We revisit the split control methodology for MIMO servo design with output feedback for linear systems subject to unknown disturbances, introduced by Cro Granite and Hedrick (1988). This method dec...

Model matching for multiple-input multiple-output nonlinear systems by sliding mode

Abstract: The MIMO (multiple-input multiple-output) nonlinear model matching problem is considered The zero-dynamics algorithm and a sliding mode technique are combined resulting in a robust control structure Asymptotic model matching can be achieved in the presence of modeling errors and disturbances >

Synthesis of MIMO system structures with low frequency-weighting sensitivity

Abstract: The synthesis of multi-input multi-output (MIMO) linear discrete-time systems with low sensitivity is discussed. The system structure is synthesized so as to minimize a performance index defined by sensitivities with frequency weighting functions under an l/sub 2/ scaling constraint on the state variables and under no scaling constraint, respectively. It is shown that, as a result, the system structure which minimizes the performance index does not coincide with the realization being balanced in the wide sense. >