Alternating Positive and Negative Feedback Control Model Based on Catastrophe Theories

Abstract

In automatic control systems, negative feedback control has the advantage of maintaining a steady state, while positive feedback control can enhance some activities of the control system. How to design a controller with both control modes is an interesting and challenging problem. Motivated by it, on the basis idea of catastrophe theories, taking positive feedback and negative feedback as two different states of the system, an adaptive alternating positive and negative feedback (APNF) control model with the advantages of two states is proposed. By adaptively adjusting the relevant parameters of the constructed symmetric catastrophe function and the learning rule based on error and forward weight, the two states can be switched in the form of catastrophe. Through the Lyapunov stability theory, the convergence of the proposed adaptive APNF control model is proven, which indicates that system convergence can be guaranteed by selecting appropriate parameters. Moreover, we present theoretical proof that the negative feedback system with negative parameters can be equivalent to the positive feedback system with positive parameters. Finally, the results of the simulation example show that APNF control has satisfactory performance in response speed and overshoot.

ICRICS: Iterative Compensation Recovery for Image Compressive Sensing

“…Currently, to the best of our knowledge, image compressive sensing adopts an open-loop architecture with separated measurement and recovery units. Closed-loop architecture is broadly applied in automatic control systems to gain better dynamic and static performance, such as response time and steady-state error [6]. This paper attempts to enhance the recovery quality of the existing image compressive sensing approaches by building a closed-loop system.…”