Global Practical Tracking for Nonlinear Systems With More Unknowns via Adaptive Output‐Feedback

This paper presents an adaptive robust motion control algorithm for linear induction motor (LIM) with electromagnetic nonlinearity compensation to suppress nonlinear magnetic thrust reduction due to the end effect. The electromagnetic compensation adaptive robust control (ECARC) algorithm is developed to deal with the parametric uncertainties and uncertain nonlinearities that are widely present in LIM. This algorithm employs a discontinuous projection‐type method to estimate the system parameters. The disturbance is bounded by a critical value, which guarantees that the system is exponentially stable. However, due to the magnetic field distortion caused by the opening of primary ends, the relationship between the driving current and the resultant motor thrust is nonlinear. Consequently, electromagnetic thrust and flux are calculated and used for feedback compensation. Different from the traditional vector control and direct torque control, the control algorithm employs electromagnetic thrust and magnetic flux to compensate torque current and magnetic field. Simulation results and comparative experimental results show that current control algorithm is accurate and efficient.

“…Applying equations (11)(12)(13)(14) into equations (1-4) and assuming that qy =̇q y = 0, u dy = u qy = 0, the following equations can be obtained:…”

Section: Lim Model Considering End Effectmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Robust Motion Control for Linear Induction Motor with Electromagnetic Nonlinearity Compensation

Liu

Peng

et al. 2018

“…We now give appropriate estimations to the last two terms on the right-hand side of (9). Using the technique of completing squares yields…”

Section: Output-feedback Controller Designmentioning

confidence: 99%

“…Remarkably, system (1) allows three types of uncertainties: unknown control directions, parametric uncertainties and unknown input bias, and hence is more essential than those in the existing related literature (see e.g., [3][4][5][6][7][8][9]). Specifically, (i) the signs of g i 's, the control coefficients of system (1), are unknown, besides the unknown amplitudes of g i 's.…”

Section: Introduction and Problem Formulationmentioning

confidence: 99%

Adaptive Output‐Feedback Control of Nonlinear Systems with Multiple Uncertainties

Huang

Liu

2017

Self Cite

This paper addresses the global stabilization via adaptive output-feedback for a class of uncertain nonlinear systems. Remarkably, the systems under investigation are with multiple uncertainties: unknown control directions, unknown growth rates and unknown input bias, and can be used to describe more physical plants. Multiple uncertainties, which usually cannot be compensated by a sole compensation technique, may give rise to big technical difficulty for controller design. To overcome such difficulty and to achieve the global stabilization, a new adaptive output-feedback scheme is proposed in this paper, by flexibly combining Nussbaum-type function, tuning function technique and extended state observer. It is shown that, under the designed controller, the system states globally converge to zero. A simulation example on non-zero set-point regulation is given to demonstrate the effectiveness of the theoretical results.

“…Hence the design of tracking control systems for parallel mechanism has attracted considerable attention. In the literature, several control approaches have been proposed to deal with uncertainties such as adaptive control [2,3], sliding mode control (SMC) [4][5][6][7], finite-time control [8][9][10], fuzzy systems [11,12], neural networks [13,14], and so on. To handle systems with uncertainties, SMC is widely used in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Adaptive finite‐time neural network control for redundant parallel manipulators

Nguyen

Wang

et al. 2019

This paper reports our study of an adaptive finite-time neural network control scheme for redundant parallel manipulators. The proposed controller is based on fully tuned radial basis function neural networks (RBFNs), nonsingular fast terminal sliding mode control (NFTSMC), and nonlinearities in output feedback. RBFNs with a fully online update on the output weights and both the centers and variances of Gaussian functions are used to estimate system uncertainties and disturbances. The proposed approach possesses several advantages over other existing approaches like robustness, rapid response, nonsingularity, higher precision, finite-time convergence, and better tracking control performance. The stability of the parallel manipulators is ensured by the Lyapunov theory. Finally, simulation results validate the effectiveness of the proposed methodology.