In this paper, a multivariable direct adaptive controller using multiple models without minimum phase assumption is presented to improve the transient response when the parameters of the system jump abruptly. The controller is composed of multiple fixed controller models, a free-running adaptive con- troller model and a re-initialized adaptive controller model. The fixed controller models are derived from the corresponding fixed system models directly. The adaptive controller models adopt the direct adaptive algorithm to reduce the design calculation. At every instant, the optimal controller is chosen out according to the switching index. The interaction of the system is viewed as the measured distur- bance which is eliminated by the choice of the weighing polynomial matrix. The global convergence is obtained. Finally, several simulation examples in a wind tunnel experiment are given to show both effectiveness and practicality of the proposed method. The significance of the proposed method is that it is applicable to a non-minimum phase system, adopting direct adaptive algorithm to overcome the singularity problem during the matrix calculation and realizing decoupling control for a multivariable system.
For a stochastic non-minimum phase multivariable system,a multiple models direct adaptive controller is presented.It is composed of multiple fixed models with two adaptive models.The fixed models are used to cover the region where the system parameters jump and improve the transient response,while another two adaptive models are used to guarantee the stability.Utilizing generalized minimum variance design method,it adopts the stochastic system estimation algorithm with optimal controller design method to identify the controller parameter directly.Finally,the global convergence is given.The simulation proves the effectives of the controller proposed.
