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Conventional and Intelligent Control
Published in Clarence W. de Silva, Intelligent Control, 2018
An adaptive control system is a feedback control system in which the values of some or all of the controller parameters are modified (adapted) during the system operation (in real time) on the basis of some performance measure, when the response (output) requirements are not satisfied. The techniques of adaptive control are numerous because many criteria can be employed for modifying the parameter values of a controller (Zhou and de Silva, 1995). According to the above definition, self-tuning control (see, for example, Clarke and Gawthrop, 1985) falls into the same category. In fact, the terms “adaptive control” and “self-tuning control” have been used interchangeably in the technical literature. Performance criteria used in self-tuning control may range from time-response or frequency-response specifications, parameters of “ideal” models, desired locations of poles and zeros, and cost functions. Generally, however, in self-tuning control of a system some form of parameter estimation or identification is performed on-line using input-output measurements from the system, and the controller parameters are modified using these estimated parameter values. A majority of the self-tuning controllers developed in the literature is based on the assumption that the plant (process) is linear and time invariant. This assumption does not generally hold true for complex industrial processes. For this reason we shall restrict our discussion to an adaptive controller that has been developed for nonlinear and coupled plants.
Recent Trends in Adaptive Control Applications
Published in V. V. Chalam, Adaptive Control Systems, 2017
Adaptive control makes possible the implementation of control systems, even though the parameters of the process are unknown, and provides for automatic adjustment to step changes in operating conditions or slow continuous variations. The DO dynamics are represented by a bilinear model to which we look for both parameter estimation and control. A least squares parameter estimator is combined with a minimum-variance control algorithm to obtain an adaptive controller. This approach allows us to identify the mass transfer coefficient and the oxygen uptake rate, which provide useful information concerning the state of the activated sludge process. Robustness in the presence of bounded error terms and global convergence are the features of this class of adaptive control algorithm [143].
Distributed Control Systems
Published in Richard L. Shell, Ernest L. Hall, Handbook of Industrial Automation, 2000
In modern control theory, the term self-tuning control [30] has been coined as alternative to adaptive control. In a self-tuning system control parameters are, based on measurements of system input and output, automatically tuned to result into a sustained optimal control. The tuning itself can be affected by the use of measurement results to: Estimate actual values of system parameters and, in the sequence, to calculate the corresponding optimal values of control parameters, or toDirectly calculate the optimal values of control parameters.
A novel LMI-based robust adaptive model predictive control for DFIG-based wind energy conversion system
Published in Systems Science & Control Engineering, 2019
Hongkai Zhang, Jie Hao, Chao Wu, Yang Li, Alireza Sahab
Among many other approaches, adaptive control and robust model predictive control are the two popular control strategies that researchers have extensively employed while dealing with uncertainty and disturbance (Imani, Fazeli, Malekizade, & Hosseinzadeh, 2019; Imani, Jahed-Motlagh, Salahshoor, Ramezani, & Moarefianpur, 2017; Imani, Jahed-Motlagh, Salahshoor, Ramezani, & Moarefianpur, 2018; Imani, Malekizade, Asadi Bagal, & Hosseinzadeh, 2018; Taleb Ziabari, Jahed-Motlagh, Salahshoor, Ramezani, & Moarefianpur, 2017). Adaptive Control covers a set of techniques which provide a systematic approach for automatic adjustment of controllers in real time, in order to achieve or to maintain a desired level of control system performance when the parameters of the plant dynamic model are unknown and/or change in time (Landau, Lozano, M'Saad, & Karimi, 2011). Whereas, robust model predictive control aims at handling the uncertainties of the system within a priori defined bound and there are several ways for designing robust MPC controllers in disturbed nonlinear systems that can be divided to nominal stabilizing MPC algorithms, as they were done in (Grimm, Messina, Tuna, & Teel, 2003; Limon, Alamo, & Camacho, 2002; Magni, De Nicolao, & Scattolini, 1998; Scokaert, Rawlings, & Meadows, 1997) and MPC problem formulation via open-loop worst case scenarios (Chisci, Rossiter, & Zappa, 2001; Limon, Alamo, & Camacho, 2002; Richards & How, 2006).
Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art
Published in Advanced Robotics, 2019
There have been various applications of adaptive control algorithms based on neural networks (NNs) for autonomous flight in UAVs. The increasing interest in adaptive control stems from its ability to handle changes in system dynamics and to mitigate uncertainty. Based on the intelligent behavior of the human brain, NN consists of many simple, connected processors called neurons, each producing a sequence of real-valued activations [56–59]. This yields the great advantages in high-dimensional data representation and processing, therefore, NN has been widely used to solve complicated adaptive control problems of UAVs for autonomous flight [43].
Speed sensorless control of a bearingless induction motor based on fuzzy PI fractional MRAS scheme
Published in International Journal of Green Energy, 2022
Ting Xu, Zebin Yang, Xiaodong Sun, Jingjing Jia
Adaptive control is the ability to maintain the desired state of the system automatically and continuously in the case of unpredictable changes. At present, there are two kinds of mature adaptive control, model reference adaptive control and self-tuning control. The model reference adaptive system has become one of the most common methods to ensure the accurate estimation of rotor speed and rotor flux linkage due to its simple design, fast convergence speed and small computation.