Experimentally validated continuous-time repetitive control of non-minimum phase plants with a prescribed degree of stability

This paper considers the application of continuous-time repetitive control to non-minimum phase plants in a continuous-time model predictive control setting. In particular, it is shown how some critical performance problems associated with repetitive control of such plants can be avoided by use of predictive control with a prescribed degree of stability. The results developed are first illustrated by simulation studies and then through experimental tests on a non-minimum phase electro-mechanical system

Switched Linear Model Predictive Controllers for Periodic Exogenous Signals

This paper develops linear switched controllers for periodic exogenous signals using the framework of a continuous-time model predictive control. In this framework, the control signal is generated by an algorithm that uses receding horizon control principle with an on-line optimization scheme that permits inclusion of operational constraints. Unlike traditional repetitive controllers, applying this method in the form of switched linear controllers ensures rumpless transfer from one controller to another. Simulation studies are included to demonstrate the efficacy of the design with or without hard constraints

H∞ and guaranteed cost control of discrete linear repetitive processes

AbstractRepetitive processes are a distinct class of 2D systems (i.e. information propagation in two independent directions) of both systems theoretic and applications interest. In general, they cannot be controlled by direct extension of existing techniques from either standard (termed 1D here) or 2D systems theory. Here first we give major new results on the design of control laws using an H∞ setting and including the possibility of uncertainty in the process model. Then we give the first ever results on guaranteed cost control, i.e. including a performance criterion in the design. The designs in both cases can be computed using linear matrix inequalities. These results are for so-called discrete linear repetitive processes which arise in applications areas such as iterative learning control

Adaptive stabilization with exponential decay

Adaptive high gain stabilizers for classes of linear time-invariant state space systems are presented. The classes cover multi-input - multi-output systems where the state dimension is not known. Only standard assumptions such as minimum phase and known respectively unknown sign are required. The main result is, that the adaptive control laws of Byrnes and Willems can be modified to produce a gurantee of exponentially decaying states

Exponential Stabilization using Piecewise Constant Gain Adaptation

An adaptive stabilizer for a class of single-input-single-output (SISO) minimum phase systems with known sign is introduced. It extends the work of J.C. Willems and C.I. Byrnes (1984) but uses a threshold-based piecewise constant gain adaptation. This provides an exponential decaying state, and the set of thresholds which stabilize the terminal system exponentially is dense