China
Africa
Explore chapters and articles related to this topic
Integrated Missile Design
Published in Rafael T. Yanushevsky, Modern Missile Guidance, 2019
Classical control theory is based on the feedback principle. The optimal control theory supplies us with an optimal control law as a function of time. Only for a special class of optimal problems, the optimal solution can be presented as a function of the system state vector, i.e., present controller equations similar to the closed-loop system examined in classical control theory.
Intelligent machining: a review of trends, achievements and current progress
Published in International Journal of Computer Integrated Manufacturing, 2021
M. Imad, C. Hopkins, A. Hosseini, N.Z. Yussefian, H.A. Kishawy
Damage is unacceptable when precise tolerances are required. Thin-walled aerospace parts require tight tolerances, which is why Li et al (Li et al. 2018) proposed a method that is able to predict dimensional surface form errors caused by deflection of the cutting tool and the workpiece. In their work, thin-walled parts are flank milled on a five-axes milling machine. To properly predict form errors, the experimentally validated method included updating the blade’s stiffness matrix and the deflection that was caused by tool engagement with the workpiece. Industrial settings require real time controllers due to their dynamic nature. The requirement for real-time responses led to the work of Moreira et al. (2019). They proposed an integrated multi-variable controller that had the ability to execute orders in real time, unlike traditional controllers. The controller was a combination of the following approaches: NF, fuzzy logic, and classical control theory. During a milling case of EN24T steel alloy, the proposed controller was validated and proved its reliability, by comparing it against a traditional controller in terms of surface roughness. The integrated multi-variable controller displayed better results than the traditional controller, by minimizing the surface roughness error value by 96% (3.6 µm to 0.12 µm). This minimization allowed for achieving a final surface quality within the acceptable quality tolerances of 0.2%-4%. Samples of recently implemented techniques in the field of intelligent process control are presented in Table 6.
Periodic sampling: maximising the sampling period
Published in International Journal of Control, 2020
It goes without saying that, between samples, a sampled-data control system operates without feedback. As classical control theory reminds us, the lack of feedback may increase operating errors. For a particular controlled system, the magnitude of such operating errors depends on two main factors: (i) the length of the time span between samples, namely, the length of the sampling period; and (ii) the nature of the input signal the controlled system receives between samples, namely, the nature of the controller controlling the system. The present paper concentrates on the existence, the design, and the implementation of robust optimal controllers that make it possible to utilise the maximal sampling period, without violating specified bounds on operating errors and other specifications. We show in Section 3 that such optimal controllers do exist for a broad family of nonlinear input-affine systems. The main requirement for the existence of such optimal controllers is a certain controllability property the controlled system must possess.
Successive Linear Programming to Improve Small-Signal Stability of Power Systems with Doubly-Fed Induction Generators
Published in Electric Power Components and Systems, 2019
Shenghu Li, Jiejie Huang, Hao Zhang, Zhuopeng Li
One is using the classical control theory. Based on the modal analysis and the transfer function, Pagola et al. use the eigen-sensitivity to improve the oscillatory stability of multimachine system, and decide the feedback placements [18]. Two state feedback controllers, that is, desensitized 4-loop regulator (DFLR) and extended DFLR, are proposed by Elices et al. to damp the local and the wide-area low-frequency oscillations (LFOs) [19]. The controllers are formed of the automatic voltage regulator and power system stabilizer designed by the eigenvalue sensitivity and frequency response. Ma et al. use the output feedback and region pole assignment to design the excitation controller to damp the LFOs, coordinate multiple controllers, and avoid damping decrease of other modes [20]. But for large power systems, unless using the dynamic reduction techniques, it is difficult to quantify the controllability Gramian and place the eigenvalues.