No abstract

The control design for many practical applications involves a need to specifically treat parameter uncertainty and disturbances. As shown in previous work of the authors, techniques from interval analysis can be used for this purpose in an efficient way. The two options that have been considered so far are the use of interval analysis in either a framework for modelpredictive control or in a framework for variable-structure sliding mode design. In the latter case, interval techniques can be used efficiently for a robust stabilization of continuous-time dynamic systems despite bounded uncertainty. To avoid unnecessarily conservative control strategies, it has to be shown in real time that the closed-loop control system is guaranteed to remain asymptotically stable despite bounded error variables. This online stability proof is performed on the basis of suitable candidates for Lyapunov functions, while functionalities for interval analysis are provided by C++ software libraries. Required partial derivatives, for transformations of state equations into suitable canonical forms and for the estimation of a finite number of time derivatives of the controlled variables, are computed efficiently by algorithmic differentiation. This paper presents an overview of intervalbased variable-structure control approaches for the thermal behavior of solid oxide fuel cells. These approaches comprise trajectory tracking during non-stationary heating phases as well as disturbance compensation at high-temperature operating points. Finally, they rigorously account for state and actuator constraints.

No abstract

The estimation of non-measurable state variables as well as the reliable identification of unknown system parameters are important prerequisites for the design and implementation of controllers for nonlinear dynamic systems. However, these tasks are often impeded by the nonlinearity of dynamic system models as soon as observer techniques are sought for, which can be used for large operating ranges. Moreover, parameters and measured data are typically only known within given tolerance bounds. Such uncertainty makes the proof of the asymptotic stability of the error dynamics of classical state observers quite difficult. Therefore, a novel interval-based sliding mode observer providing point-valued estimates is presented in this paper which is designed in such a way that asymptotic stability can be guaranteed by means of an online evaluation of a suitable Lyapunov function. Furthermore, an efficient strategy for the adaptation of the switching amplitude of the observer's variable structure part is presented to reduce the amplification of measurement noise as far as possible if time-varying state variables and time-invariant system parameters are estimated simultaneously. An illustrative example, describing the longitudinal dynamics of a vehicle, is presented to highlight the practical applicability of the observer.

No abstract