Global adaptive neural network control for a class of uncertain non-linear systems

A bio-system inspired nonline ar intelligent controller with application to bio-reactor system

“…Eqs. (6) and (17) to identify the corresponding amplitude factor K, time constant T, and time-delay τ. The sampled response data and the identified model parameters are shown in Table 3, where T r is the temperature of reactor.…”

“…For long time the bio-system and human information system have inspired us with many ideas to solve some difficult engineering problems [1][2][3]. In order to obtain high product quality and control precision, some nonlinear control techniques, such as neural networks control [4][5][6][7], fuzzy control [8][9][10][11], expert control [12][13][14][15] and other advanced control algorithms [16,17], have been developed. It can also be found that more and more advanced or intelligent control algorithms inspired from the bio-information systems [18] will be developed in the future.…”

Robust adaptive feedback linearization control for a class of MIMO uncertain nonlinear systems

“…In the ANNC, neural networks are primarily used as on-line approximators for the unknown nonlinearities due to their inherent approximation capabilities. The adaptive control methods were proposed by using NNs for uncertain nonlinear MIMO systems [5,6]. In their study, the adaptive control scheme based on radial basis function neural networks (RBFNNs) was presented for a class of the uncertain nonlinear MIMO systems.…”

Robust Adaptive Control for a Class of Nonlinear Systems Using the Backstepping Method

“…In recent decades, a large number of papers have studied the problem of robust adaptive control of nonlinear systems (see, e.g., [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] and references therein). In [1], a new adaptive law based on an optimal control formulation for the minimization of the 2 L norm of the tracking bounded error is considered.…”