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Force-System Resultants and Equilibrium
Published in Richard C. Dorf, The Engineering Handbook, 2018
In the last 20 years nonlinear control has reached the level of a mature discipline, both in its theoretical developments and in engineering applications. Examples of successful application of modern nonlinear control theory are widespread, and range from aerospace to robotics, and from electrical and mechanical to biomedical engineering. The advantage of nonlinear control systems design versus more conventional (i.e., linear) design methodologies lies in the fact that the fundamentally nonlinear nature of the plant to be controlled is taken directly into account (and, sometimes, exploited) rather than neglected or ignored. The price to pay, however, is the lack of a general design methodology that is applicable to all nonlinear systems, and a substantial complexity of the mathematical tools required for the analysis and the synthesis of nonlinear control systems. The first drawback is intrinsic to the nature of the problem, and it is alleviated by the fact that most nonlinear design techniques indeed apply to entire classes of nonlinear systems sharing a common structure and properties. The second requires the modern control engineer to acquire the mathematical tool necessary to master the discipline, and meet the challenges for more demanding applications.
Introduction
Published in Mohamed Gad-el-Hak, MEMS, 2005
Aside from the developments of robust optimal control briefly outlined in the previous section, the area of most recent development in control theory has been nonlinear control. Nonlinear control does not ignore nonlinear effects via linearization, the nonlinearities in the control system are either expressly recognized or are even exploited for control purposes. Much, but not all, development in nonlinear control uses tools from differential geometry. While the control techniques will be outlined here, the basics of differential geometry will not, and the interested reader is referred to Abraham et al. (1988), Boothby (1986), Isidori (1996), and Nijmeijer and van der Schaft (1990) for details.
Adaptive anti-slip regulation method for distributed drive electric vehicle
Published in Johannes Edelmann, Manfred Plöchl, Peter E. Pfeffer, Advanced Vehicle Control AVEC’16, 2017
K. Sun, Z. Yu, L. Xiong, R. Zhang
The sliding mode control has been an effective solution to nonlinear control problem due to its robustness to model uncertainty and system disturbance. And its drawback of chattering in control input can be suppressed by employing continuous boundary layer near the sliding face. Enlightened by a kind of conditional integrator design (Kay & Khalil, 2002), we propose the SMC with conditional integrator (9) as ASR controller. Within the boundary layer, it has the advantage of low feedback gain of conditional PI controller. At the same time, it will prevent the integral part from being too large to deteriorate system’s dynamic response by suppressing overshoot.
Application of fuzzy control in the evaporation stage of a first- and second-generation sugarcane ethanol biorefinery
Published in Chemical Engineering Communications, 2023
E. Y. Emori, M. A. S. S. Ravagnani, C. B. B. Costa
FL is a strategy for thinking that looks like human thinking. FL controllers are a manifestation of how to put an experienced human operator’s intuitive and empirical expertise about a given system into a mathematically defined representation. The foundation of this strategy is a collection of IF-THEN rules controlling the input–output relationship of a system. To represent these rules in language terms, the fuzzy set theory is frequently used. FL controllers are particularly robust instruments for implementation because they incorporate expert knowledge. A fuzzy controller uses non-linear mapping to transfer the state space of the system into control space. As a result, a fuzzy controller output is a non-linear control fuzzy surface that mimics expert knowledge. The linguistic value (e.g., high, low, and medium) attributed to a particular variable is changed into a fuzzy set by the detailing of an evaluation of membership numbers somewhere in the range between 0 and 1. To characterize the level of the membership, two different kinds of stretches have to be picked, for example, an interval of usual values and another one with more specific values (Dohnal and Walthew, 1995; Ghasem, 2006; Hussain et al., 2014).
PVTOL control using feedback linearisation with dynamic extension
Published in International Journal of Control, 2021
Jossué Cariño Escobar, Rogelio Lozano, Moisés Bonilla Estrada
The method proposed in this work is called feedback linearisation. This is a nonlinear control technique that allows the system to be transformed into a linear system using a feedback function. The advantage of this technique is that it is not based on approximations like the previous cases, thus, the possibility exists that global stability can be ensured for this method. Even if the system is locally stable, it usually yields a better attraction zone than the one obtained with linear approximations. However, it may have some drawbacks, one of them being that the exact knowledge of the state is crucial in order for the feedback function to successfully linearise the system. In the case of PVTOLs, there exist observers that can measure accurately the attitude of the vehicle, which makes it possible to mitigate this drawback. Another drawback that is the main focus of this work is that the transformation of the system often presents singularities in certain vehicle states.
Recent Developments in Dynamic Modeling, Control and Applications of Neural Networks
Published in Cybernetics and Systems, 2020
Sitian Qin, Shenshen Gu, Nian Zhang
Recent advances and emerging approaches in neural model and learning (i.e. deep neural network) have led to an unparalleled surge of interest in the topic of neural networks. Neural networks provide an intelligent approach for solving complex problems that might otherwise not have a tractable solution. Neural networks have emerged as a powerful tool by providing outstanding performance that allow a wide variety of unprecedented applications in associative memory, function approximation, learning system, nonlinear system modeling and control. Neural networks themselves are typically nonlinear, and many different kinds of neural network models have recently been proposed for solving emerging problems. In addition, research on dynamics of neural networks and neural networks based control in nonlinear control system have also grown tremendously. This special issue presents the latest theoretical and technical advancements, and novel applications of neural networks.