This paper addresses the optimization and stabilization problems of nonlinear systems subject to parameter uncertainties. The methodology is based on a fuzzy logic approach and an improved genetic algorithm (GA). The TSK fuzzy plant model is employed to describe the dynamics of the uncertain nonlinear plant. A fuzzy controller is then obtained to close the feedback loop. The stability conditions are derived. The feedback gains of the fuzzy controller and the solution for meeting the stability conditions are determined using the improved GA. In order to obtain the optimal fuzzy controller, the membership functions are further tuned by minimizing a defined fitness function using the improved GA. An application example on stabilizing a two-link robot arm will be given.
"Therefore, the aim of this stage is to move the nut into a specified angle and to assure a proper alignment between the nut and bolt avoiding all of the aforementioned errors. Fig. 2. The 4 stages of the bolt tightening process  . "
[Show abstract][Hide abstract] ABSTRACT: In the modern wind turbine industry, one of the core processes is the assembly of the bolt-nut connections of the hub, which requires tightening bolts and nuts to obtain well-distributed clamping force all over the hub. This force deals with nonlinear uncertainties due to the mechanical properties and it depends on the final torque and relative angular position of the bolt/nut connection. This paper handles the control problem of automated bolt tightening processes. To develop a controller, the process is divided into four stages, according to the mechanical characteristics of the bolt/nut connection: a fuzzy logic controller (FLC) with expert knowledge of tightening process and error detection capability is proposed. For each one of the four stages, an individual FLC is designed to address the highly nonlinearity of the system and the error scenarios related to that stage, to promptly prevent and avoid mechanical damage. The FLC is implemented and real time executed on an industrial PC and finally validated. Experimental results show the performance of the controller to reach precise torque and angle levels as well as desired clamping force. The capability of error detection is also validated.
IEEE Transactions on Control Systems Technology 01/2014; 23(1). DOI:10.1109/TCST.2014.2309854 · 2.47 Impact Factor
"These optimization methods can basically be put in two categories: derivative-based and derivative-free optimization methods. Genetic algorithm , particle swarm optimization (PSO) , singular value QR decomposition  and cell mapping  can be cited as some examples of derivative-free methods. On the other hand, examples of derivative-based optimization methods are gradient descent , simplex method , least square  and Extended Kalman Filter (EKF) . "
[Show abstract][Hide abstract] ABSTRACT: In this paper, the use of extended Kalman filter for the optimization of the parameters of type-2 fuzzy logic systems is proposed. The type-2 fuzzy logic system considered in this study benefits from a novel type-2 fuzzy membership function which has certain values on both ends of the support and the kernel, and uncertain values on other parts of the support. To have a comparison of the extended Kalman filter with other existing methods in the literature, particle swarm optimization and gradient descent-based methods are used. The proposed type-2 fuzzy neuro structure is tested on different noisy input-output data sets, and it is shown that extended Kalman filter has a better performance as compared to the gradient descent-based methods. Although the performance of the proposed method is comparable with the particle swarm optimization method, it is faster and more efficient than the particle swarm optimization method. Moreover, the simulation results show that the proposed novel type-2 fuzzy membership function with the extended Kalman filter has noise rejection property. Kalman filter is also used to train the parameters of type-2 fuzzy logic system in a feedback error learning scheme. Then, it is used to control a real-time laboratory setup ABS and satisfactory results are obtained.
"However, this approach requires an accurate timeinvariant fuzzy Takagi–Sugeno (TS) model of the nonlinear plant. Leung et al.  extend the approach to the case of known parameter uncertainties. Note that this combination of GA and Lyapunov theory focuses on stabilization in the presence of uncertainties, rather than on hardware-in-the-loop optimization. "
[Show abstract][Hide abstract] ABSTRACT: This paper proposes a hybrid approach for the design of adaptive fuzzy controllers (FCs) in which two learning algorithms with different characteristics are merged together to obtain an improved method. The approach combines a genetic algorithm (GA), devised to optimize all the configuration parameters of the FC, including the number of membership functions and rules, and a Lyapunov-based adaptation law performing a local tuning of the output singletons of the controller, and guaranteeing the stability of each new controller investigated by the GA. The effectiveness of the proposed method is confirmed using both numerical simulations on a known case study and experiments on a nonlinear hardware benchmark.
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