Input-to-state stability methods for nonlinear and network systems

Ubiquitous uncertainties exist in control systems. The concept of input-to-state stability (ISS) is one way to describe how uncertainties influence systems stability. The main purpose of this thesis is to develop some new ISS-based tools to design feedback control laws that are capable of counteracting specific uncertainties in nonlinear and network systems. In Part II, ISS cyclic-small-gain theorems are developed as a basic criterion to analyze the stability and robustness of nonlinear and network systems composed of interacting ISS subsystems. Part III presents a dissipativity-based switching adaptive control methodology, with which a control system could counteract uncertainties by appropriately switching the controller parameters. Based on the cyclic-small-gain theorem, the switching adaptive control law can be implemented in a decentralized manner for uncertain network systems. In Part IV, we propose cyclic-small-gain based tools for robust control design of nonlinear systems with disturbed measurements. With the proposed design tools, the controller can drive the control error of a nonlinear system to the level of the measurement disturbance. The efforts in Part V are devoted to quantized control of nonlinear systems. We will consider different quantizers and introduce new quantized control structures for nonlinear systems