This brief develops an innovative robust iterative learning control law using the repetitive process setting. The new design is experimentally validated through a comprehensive set of experiments highlighting the capabilities for the position tracking control of a permanent magnet synchronous motor subject to load disturbances in the presence of uncertainties in selected parameters.

This paper is concerned with recursively estimating the internal state of a nonlinear dynamic system by processing noisy measurements and the known system input. In the case of continuous states, an exact analytic representation of the probability density characterizing the estimate is generally too complex for recursive estimation or even impossible to obtain. Hence, it is replaced by a convenient type of approximate density characterized by a finite set of parameters. Of course, parameters are desired that systematically minimize a given measure of deviation between the (often unknown) exact density and its approximation, which in general leads to a complicated optimization problem. Here, a new framework for state estimation based on progressive processing is proposed. Rather than trying to solve the original problem, it is exactly converted into a corresponding system of explicit ordinary first-order differential equations. Solving this system over a finite "time" interval yields the desired optimal density parameters.

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 model predictive 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, whereas 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 efficiently computed by algorithmic differentiation. This paper presents an overview of interval-based variable-structure control approaches for the thermal behavior of solid oxide fuel cells. These approaches consist of trajectory tracking during nonstationary heating phases and disturbance compensation at high-temperature operating points. Finally, they rigorously account for state and actuator constraints.

Validated integration of ordinary differential equations with uncertain initial conditions and uncertain parameters is important for many practical applications. If guaranteed bounds for the uncertainties are known, interval methods can be applied to obtain validated enclosures of all states. However, validated computations are often affected by overestimation, which, in naive implementations, might even lead to meaningless results. Parallelepiped and QR preconditioning of the state equations, Taylor model arithmetic, as well as simulation techniques employing splitting and merging routines are a few existing approaches for reduction of overestimation. In this paper, the recently developed validated solver ValEncIA-IVP and several methods implemented there for reduction of overestimation are described. Furthermore, a detailed comparison of this solver with COSY VI and VNODE, two of the most well- known validated ODE solvers, is presented. Simulation results for simplified system models in mechanical and bio- process engineering show specific properties, advantages, and limitations of each tool.

In this paper, control-oriented modeling approaches are presented for distributed parameter systems. These systems, which are in the focus of this contribution, are assumed to be described by suitable partial differential equations. They arise naturally during the modeling of dynamic heat transfer processes. The presented approaches aim at developing finite-dimensional system descriptions for the design of various open-loop, closed-loop, and optimal control strategies as well as state, disturbance, and parameter estimation techniques. Here, the modeling is based on the method of integrodifferential relations, which can be employed to determine accurate, finite-dimensional sets of state equations by using projection techniques. These lead to a finite element representation of the distributed parameter system. Where applicable, these finite element models are combined with finite volume representations to describe storage variables that are—with good accuracy—homogeneous over sufficiently large space domains. The advantage of this combination is keeping the computational complexity as low as possible. Under these prerequisites, real-time applicable control algorithms are derived and validated via simulation and experiment for a laboratory-scale heat transfer system at the Chair of Mechatronics at the University of Rostock. This benchmark system consists of a metallic rod that is equipped with a finite number of Peltier elements which are used either as distributed control inputs, allowing active cooling and heating, or as spatially distributed disturbance inputs.