AN ALGEBRAIC METHOD FOR POLE PLACEMENT IN MULTIVARIABLE SYSTEMS

This paper considers the pole placement in multivariable systems involving known delays by using dynamic controllers subject to multirate sampling. The controller parameterizations are calculated from algebraic equations which are solved by using the Kronecker product of matrices. It is pointed out that the sampling periods can be selected in a convenient way for the solvability of such equations under rather weak conditions provided that the continuous plant is spectrally controllable. Some overview about the use of nonuniform sampling is also given in order to improve the system's performance.

A matrix Padé-type-Routh model reduction method for multivariable linear systems

In the present paper, the formulae for matrix Padé-type approximation were improved. The mixed model reduction method of matrix Padé-type-Routh for the multivariable linear systems was presented. A well-known example was given to illustrate that the mixed method is efficient.

On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network

In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed.

Optimal decoupling control system using kernel method

A major difficulty in multivariable control design is the cross-coupling between inputs and outputs which obscures the effects of a specific controller on the overall behavior of the system. This paper considers the application of kernel method in decoupling multivariable output feedback controllers. Simulation results are presented to show the feasibility of the proposed technique.

A Systematic Design of Emulators for Multivariable Non Square and Nonlinear Systems

In this paper, multimodel and neural emulators are proposed for uncoupled multivariable nonlinear plants with unknown dynamics. The contributions of this paper are to extend the emulators to multivariable non square systems and to propose a systematic method to compute the multimodel synthesis parameters. The effectiveness of the proposed emulators is shown through two simulation examples. The obtained results are very satisfactory, they illustrate the performance of both emulators and show the advantages of the multimodel emulator relatively to the neural one.

Performance analysis of stochastic gradient algorithms under weak conditions

By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting （i.e., the data product moment matrix has a bounded condition number） and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persis- tent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.

Fault-Tolerant Model Predictive Control of a Fixed-Wing UAV with Actuator Fault Estimation

The vast majority of today's agtering ayateas poas operational constsinta and have multiple inputa and outputa.This classifen them an Mults-Input Multi-Orutput(MIMO)ayn tena.This pape devekops a novel obeerver-hased fault diagnoas schene with the capability d simultaneoua state and actuator fault estimation for Linear Time-In ariant(LTI)MIMO aystenaa,which is then integrated with Model Predictive Control(MPC)method for achie ving fault-tolerant control.The application within this study is chosen to be the longitudinal flight control o a fixad-wing Unmanmed Aerial Vetücle(UAV).The oberver-based method is dom hüned with two MPC schemas to detect and compansate randomly oeeurring actuator faults in real time.The faults are modeled asa Lans Of Efkctiveess(LOE).For the first(dfident)MPCmethod,a simpke remnniguration can be perkormed in the esent of faulta,as it is based on a abaolute Input foemmlation.Howeve,as the seeond(integrd-action)MPC is based on a incamen tal input formulation,rconfiguration is notrequired,sinee this algorithm has&degrc of implicst fault tokeranee.Numerfcal simulationa danstrate the afetivens of the panposed approach for both MPC sebemes.