摘要：AbstractThis paper tackles the problem of identifying linear parameter-varying (LPV) systems by combining data originating from global and local identification experiments into a nonlinear leastsquares problem. One extreme of the approach results in a model optimal with respect to the system behavior under varying scheduling parameter conditions, while the other gives a model being a good approximation of system behavior for fixed scheduling parameter. When measurements from global and local experiments are available, a compromise between the two objectives is achieved. Numerical and experimental validations, accompanied by comparisons with existing LPV identification methods show the potential of the developed approach.
