Disturbance Observer-Based Predictive Functional Critical Control of a Table Drive System
This paper addresses a control system design for a table drive system based on the disturbance observer (DOB)-based predictive functional critical control (PFCC). To empower the previously developed DOB-based PFC to handle constraints on controlled outputs, we propose to take a critical control approach. To this end, we derive the transfer function representation of the PFC controller and yield a detailed design procedure. The effectiveness of the proposed method is confirmed through an experimental evaluation.
Computation of peak output for inputs restricted in L2 and L∞ norms using finite difference schemes and convex optimization
Control systems designed by the principle of matching gives rise to problems of evaluating the peak output. This paper proposes a practical method for computing the peak output of linear time-invariant and non-anticipative systems for a class of possible sets that are characterized with many bounding conditions on the two- and/or the infinity-norms of the inputs and their derivatives. The original infinite-dimensional convex optimization problem is approximated as a large-scale convex programme defined in a Euclidean space, which are associated with sparse matrices and thus can be solved efficiently in practice. The numerical results show that the method performs satisfactorily, and that using a possible set with many bounding conditions can help to reduce the design conservatism and thereby yield a better match.
Input spaces and output performance
The performance of an output is defined as the least upper bound of the absolute value of that output, as time ranges over the real numbers and the input ranges over an appropriate function space. The paper extends previous results by introducing useful input spaces. A simple relation is found between performance, the step response and a characteristic of the input space. Useful necessary and sufficient conditions for finite performance are derived. The new results are extended to multivariable systems. The appropriateness of various conventional performance indices is assessed by considering the appropriateness of their corresponding input spaces.