This thesis investigates the frequency weighted balanced model reduction problem for linear time invariant systems. Both continuous and discrete time systems are considered, in one and two-dimensions. First the frequency weighted balanced model reduction problem is formulated, then a novel frequency weighted, balanced, model reduction method for continuous time systems is proposed. This method is based on the retention of frequency weighted Hankel singular values of the original system, and yields stable reduced order models even when two sided weightings are employed. An alternative frequency weighted balanced model reduction technique (applicable for controller reduction applications) is then developed. This is based on a parametrized combination of the frequency weighted partial fraction expansion technique with balanced truncation and the singular perturbation approximation techniques. This method yields stable models even when two sided weightings are employed. An a priori error bound for the model reduction method is derived. Lower frequency response errors and error bounds are obtained using free parameters and equivalent anti-stable weightings. Based on the same idea, a novel parameterized frequency weighted optimal Hankel norm model reduction method with a tighter a priori error bound is proposed. The proposed methods are extended to include discrete time systems. A frequency interval Gramians based stability preserving model reduction scheme with error bounds is also presented. In this case, frequency weights are not explicitly predefined. Discrete time system related results are also included. Several frequency weighted model reduction results for two-dimensional (2-D) systems are also proposed. The advantages of these schemes include error bounds, guaranteed stability and applicability to general stable (non-separable denominator) weighting functions. Finally, a novel 2-D identification based frequency weighted model reduction scheme is outlined. Numerically robust algorithms based on square root and balancing free techniques are proposed for frequency weighted balanced truncation techniques. Several practical examples are included to illustrate the effectiveness of the algorithms.

This Thesis is concerned with the approximation of high order controllers or the controller reduction problem. We firstly consider approximating high-order controllers by low order controllers based on the closed-loop system approximation. By approximating the closed-loop system transfer function, we derive a new parametrized double-sided frequency weighted model reduction problem. The formulas for the input and output weights are derived using three closed-loop system configurations: (i) by placing a controller in cascade with the plant, (ii) by placing a controller in the feedback path, and (iii) by using the linear fractional transformation (LFT) representation. One of the weights will be a function of a free parameter which can be varied in the resultant frequency weighted model reduction problem. We show that by using standard frequency weighted model reduction techniques, the approximation error can be easily reduced by varying the free parameter to give more accurate low order controllers. A method for choosing the free parameter to get optimal results is being suggested. A number of practical examples are used to show the effectiveness of the proposed controller reduction method. We have then considered the relationships between the closed-loop system con gurations which can be expressed using a classical control block diagram or a modern control block diagram (LFT). Formulas are derived to convert a closed-loop system represented by a classical control block diagram to a closed-loop system represented by a modern control block diagram and vice versa.