This paper presents the design strategies used to develop a supervision scheme for the attitude control system of an earth satellite model. The main point regards the handling of faults affecting the satellite engines, i.e. how to detect and isolate faults, and how to prevent propagation into failures with potential mission loss as a consequence. Thus, this work develops a comprehensive scheme for fault detection, isolation and recovery of the engines for a spacecraft attitude control, based on the earth satellite model. As the present study focuses on an earth satellite model, robustness is achieved by exploiting almost known disturbance models and an explicit disturbance de-coupling method. The achieved results demonstrate how the proposed methodology can constitute a successful approach for real application in future spacecraft.
A Capsubot, which consists of three parts, the inner body, the capsule shell, and the driving mechanism, is a micro capsule robot with no legs and no wheels, and is driven by the interactive propulsion between the inner body and the capsule shell. The desired locomotion of the Capsubot is generated by making the inner body track a designed trajectory repeatedly. Due to the nature of repetitive motion, an iterative learning control scheme is proposed to improve the tracking performance of the inner body, in order to achieve the desired locomotion of the Capsubot. Extensive simulation studies are conducted to demonstrate the effectiveness of the scheme.
Subspace-based model-free predictive control algorithms directly estimate the relevant components of a predictive controller. Due to disturbances and noise in the measured data, the estimation results were often poor, which limited the applications of subspacebased model-free predictive controllers. By assuming a priori knowledge of the disturbance characteristics, this paper proposes a subspace-based model-free predictive control algorithm that utilizes the noise model for the estimation of the predictive control gain matrices. Simulation results show improved control results.
Most conventional control algorithms cause numerical problems where data is collected at sampling rates that are substantially higher than the dynamics of the equivalent continuous-time operation that is being implemented. This is of relevant interest in applications of digital control, in which high sample rates are routinely dictated by the system stability requirements rather than the signal processing needs. Digital control systems exhibit bandwidth limitations enforced by their closed-loop frequency requirements that demand very high sample rates. Considerable recent progress in reducing sample frequency requirements has been made through the use of non-uniform sampling schemes, so called alias-free signal processing. The approach prompts the simplification of complex systems and consequently enhances the numerical conditioning of the implementation algorithms that otherwise would require very high uniform sample rates. However, the control communities have not yet investigated the use of intentional non-uniform sampling. The purpose of this article is to address some algorithmic issues when using such regimes for digital control.
This paper describes the design of a multivariable H∞ loopshaping controller for water level regulation on the Haughton Main Channel, Queensland Australia. A one-degree-of-freedom controller structure is applied to three successive pools. Analysis of robust performance is presented in terms of the structured singular value. Closed-loop performance is also evaluated through simulations with high fidelity models of the channel. The controller shows better performance than decentralized PI control, and attains similar performance to a multivariable LQ controller. The H∞ loop-shaping controller is, however, much easier to tune than an LQ controller. This becomes important when the number of pools to be controlled is increased. As such, it is an appealing design paradigm when high performance control of an irrigation channel is required.
Model reduction is an area of fundamental importance in many modeling and control applications. In this paper we analyze the use of parallel computing in model reduction methods based on balanced truncation of large-scale dense systems. The methods require the computation of the Gramians of a linear-time invariant system. Using a sign function-based solver for computing full-rank factors of the Gramians yields some favorable computational aspects in the subsequent computation of the reduced-order model, particularly for non-minimal systems. As sign function-based computations only require efficient implementations of basic linear algebra operations readily available, e.g., in the BLAS, LAPACK, and ScaLAPACK, good performance of the resulting algorithms on parallel computers is to be expected. Our experimental results on a PC cluster show the performance and scalability of the parallel implementation.
Two recent approaches 4, 14 in subspace identification problems require the computation of the R factor of the QR factorization of a blockHankel matrix H,which, in general has a huge number of rows. Since the data are perturbed by noise, the involved matrix H is, in general, full rank. It is well known that, from a theoretical point of view, the R, factorization of is equivalent to the Cholesky factor of the correlation , apart from a multiplication by a sign ma- trix. In 12 a fast Cholesky factorization of the correla- tion matrix, exploiting the blockHankel structure of is described. In this paper we consider a fast algorithm to compute the factor based on the generalized Schur algorithm. The proposed algorithm allows to handle the rankdeficient case.
