An inverse optimal adaptive control design method for nonlinear strict-feedback systems with unknown parameters is presented. Generally, it is known that the optimal control problems can be reduced to the Hamilton-Jacobi-Bellman (HJB) equations, but to solve their equations is extremely difficult. For this reason, an adaptive controller minimizing some meaningful cost functionals without solving the HJB equation is designed. Such design methods are called inverse optimal design and have been researched recently. In these designs, a cost functional is decided by controller design, but its functional may not be desirable. Our goal is to design an adaptive controller so as to make a desirable cost functional smaller. In order to acheive the goal, we construct a controller by following steps, that is, 1) An adaptive inverse optimal controller is constructed by pointwise min-norm design, 2) Parameters of the designed controller is tuned. Illustrative examples show that although the constructed controllers may not be optimal, they make desirable cost functional smaller and have favorable stability property.
This paper proposes a useful expression of summational type state equation conformable to physical and/or engineering actualities. The summational type state equation can solve the following two essential problems in mathematical expressions of the existing state equations : One of them is a physical problem of discontinuity in mathematical expressions for different orders of the existing state equation related to the number of inevitable many parasitic energy elements which always exist in an actual system. The other one is an engineering problem of disunification in mathematical expressions in which continuous-time and discrete-time systems are not described with consistency. First, this paper clarifies and explains the above problems to be solved with a simple example. Next, this paper defines the summational type state equation for the problems and analyzes, for example, basic properties, stability condition and Lyapunov inequality by using the defined state equation. Finally, the effectiveness of the summational type state equation is shown by numerical examples on the stability analysis. From these results, this paper points out that the proposed state equation can overcome difficulties of the above problems and get benefits from the sophisticated results of modern control theory.
This paper discusses the integrated design strategy of CAS (Control Augmentation System) for vehicle handling quality improvement. This paper points out the similarity of the problem setting between CAS design and structure/control integrated design. As expected from the above similarity, it is observed that the phase crossover bandwidth of vehicle dynamics has crucial effect to the tracking performance for pilot control input. Furthermore, a finite bandwidth phase shaping with frequency-dependent weighting functions is proposed. Namely, the phase shaping in control bandwidth is achieved with no phase constraint in the higher frequency range. By the proposed technique, a CAS which achieves the phase shaping in the specified control bandwidth is easily designed via LMI (Linear Matrix Inequality) approach.
This paper studies a method for quality inspection in heat insulation. Based on the Mahalanobis-Taguchi system, this method performs quality judgement from measurement data of heat transmission in the heating surface of a product. An experiment shows that the method is sufficiently fast and accurate.
In this paper, we consider a subspace system identification problem for linear stochastic systems subjected to observation outliers. First, the Least-Trimmed-Squares (LTS) technique due to Bai  is extended to Multiple-Input Multiple-Output (MIMO) regression model. Then, we identify the outliers in the output process of the MIMO state space model by using the LTS technique, and replace them by the median to obtain a preprocessed input-output data without outliers. We apply the Orthogonal (ORT) decomposition method to the preprocessed input-output data to get state space models. Numerical examples demonstrate the effectiveness of the proposed method.