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

Zhao, Shi-tie; Gao, Xianwen; Che, Chang-jie

doi:10.1109/ccdc.2015.7162179

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [23], a robust adaptive feedback linearization control has been proposed for a class of uncertain nonlinear MIMO systems. It gives promise of good tracking performance and strong robustness, but this approach is also confined only to squared MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

Direct Adaptive Neural Control Design for a Class of Nonlinear Multi Input Multi Output Systems

2019

View full text Add to dashboard Cite

This paper proposes a direct adaptive neural control law for a class of affine nonlinear multi-input-multi-output (MIMO) systems of the formẋ = f (x) + G(x)u using feedback linearization when both f (x) and G(x) are to be estimated. It is challenging to estimate f (x), a vector, and G(x), a matrix, to be used in synthesizing the control law, simultaneously, because of the dimensional inconsistency arising with the available neural structures, which do not have multiple layers of outputs. This problem is addressed in this paper by exploiting the power of matrix vectorization and reshaping techniques using the Kronecker product. The strategy may be visualized as equivalent to the neural structure consisting of multiple layers of outputs that result from the appropriate manipulation of matrices corresponding to the proposed estimations. The weight update laws, for both the radial basis function neural networks that estimate both f (x) and G(x), are derived such that the proposed control law achieves the twin objective of the derived tracking performance as well as closed-loop system stability in the sense of Lyapunov. The ratios α and β are proposed in line with the widely used concept of Rayleigh's quotient adopted in structural dynamics to evaluate the natural frequency of a system. The simulation results obtained from the use of a twin rotor MIMO system are presented here to demonstrate the feasibility and effectiveness of the proposed control law. The superiority of this approach lies in the development of suitable control law for a MIMO system in the absence of knowledge about the nonlinearities.

show abstract

Section: Introductionmentioning

confidence: 99%

Direct Adaptive Neural Control Design for a Class of Nonlinear Multi Input Multi Output Systems

2019

View full text Add to dashboard Cite

show abstract

“…The principles of pole placement control for linear systems were utilized to design adaptive controls for feedback linearizable systems in the late 1980s and early 1990s. Recent studies that attempt to eliminate non-linear effects via control design are diverse (Chang et al, 2013; Chhabra and Emami, 2016; Mohammed et al, 2012; Yuan et al, 2016; Zhao et al, 2015), and several studies exist for the stabilization and control of hard non-linearities and chaotic systems using robust control techniques (Cao and Chen, 2015; Chen et al, 2012, 2013a). Computational methods with a heuristic nature rather than artificial intelligence-based approaches have also been utilized in recent years for their simple design (Chen et al, 2013b), and combinations of these approaches are available in literature (Sophianos and Boutalis, 2016; Wu and Li, 2016).…”

Section: Introductionmentioning

confidence: 99%

Adaptive approximate input–output linearizing control with applications to ball and beam mechanism

Kara

Eker

2016

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

This paper presents the design and implementation of adaptive control with approximate input–output linearization for underactuated open-loop unstable non-linear mechanical systems. Control of a ball and beam (BB) mechanism is selected as a benchmark problem for testing the designed control. The method of input–output linearization is reviewed and an adaptive input–output linearizing control design procedure is given. An approximate BB model is developed using Euler–Lagrange equations, and input–output linearization-based adaptive tracking control is designed for the system. The model is parameterized with respect to ball mass for adaptive tracking, and the proposed control structure is tested via computer simulations and experiments. The results present the tracking performance of designed control for various ball masses, and reveal the proposed method’s capability to cover ball mass variations over non-adaptive control. The proposed control exhibits improved error performance in the presence of parametric variations in the plant. Results of the BB control case reveal successful control of underactuated non-linear mechanisms when a system parameter is unknown or time varying.

show abstract

Sufficient LMI conditions and Lyapunov redesign for the robust stability of a class of feedback linearized dynamical systems

Azizi

2017

ISA Transactions

View full text Add to dashboard Cite

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

Cited by 3 publications

References 21 publications

Direct Adaptive Neural Control Design for a Class of Nonlinear Multi Input Multi Output Systems

Direct Adaptive Neural Control Design for a Class of Nonlinear Multi Input Multi Output Systems

Adaptive approximate input–output linearizing control with applications to ball and beam mechanism

Sufficient LMI conditions and Lyapunov redesign for the robust stability of a class of feedback linearized dynamical systems

Contact Info

Product

Resources

About