Sliding Mode Differentiator Based Tracking Control of Uncertain Nonlinear Systems with Application to Hypersonic Flight

This paper addresses local stabilization of a semilinear parabolic system by boundary control. Under a certain assumption on the nonlinearity of the parabolic equation, a linear boundary feedback control law is applied to control the semilinear system. Based on the operator theories and relations of different norms, locally exponential stabilization of the closed loop system is established. Simulations support the established stability result.

Section: Conclusion and Commentsmentioning

confidence: 99%

Local stabilization of semilinear parabolic system by boundary control

Xie

2019

“…The above observer techniques require knowledge about the model, the input, and the noise in cases where the Kalman filter guarantees a good performance. In contrast, differentiators or observers based on a sliding mode approach [17], even those applied in flying systems as in [18] or in [19], provide accuracy with reduced information of the model and the model parameters and do not depend on the control input. Nonetheless, a drawback relies on the sensibility to noise.…”

Section: Introductionmentioning

confidence: 99%

Continuous and smooth differentiator based on adaptive sliding mode control for a quad‐rotor MAV

Castañeda

Rodríguez

Gordillo

2019

This paper addresses the design of flight control for a micro air vehicle type quad‐rotor, which assumes partial measurements of outputs and uncertainties/perturbations affecting the aircraft performance. Under such conditions, the approach is a continuous and smooth differentiator based on an adaptive sliding mode controller. The differentiator output is a sufficiently smooth and accurate estimation of the full state even in the presence of noise, such that it allows calculation of the control law. The adaptive sliding control ensures that the quad‐rotor tracks desired trajectories subject to external disturbances, where its adaptive gains use a minimal gain large enough to remain on the sliding mode, but increases their value to reject perturbations as soon as they appear. Furthermore, a closed loop analysis of the controller observer scheme and numerical simulations shows the feasibility and advantages of the proposed method.

“…In the above studies, the MFAC algorithms were developed on the premise of an ideal operating environment without disturbance affection. It should be pointed out that disturbance input is often encountered in many practical systems, which affects the system control performance and even leads to the instability of the controlled systems, see [17]–[18] and some related references therein. Therefore, it is significant and urgent work to research these kind of nonlinear systems with disturbance input.…”

Section: Introductionmentioning

confidence: 99%

Disturbance compensation based model‐free adaptive tracking control for nonlinear systems with unknown disturbance

Wang

Pang

2019

This paper considers a novel model‐free adaptive tracking control for nonlinear perturbed systems. A general dynamic linearization model with disturbance input is first developed for nonlinear systems based on the pseudo‐partial derivatives (PPDs) method and input/output (I/O) data. Due to the unknown disturbance input, an online disturbance estimation algorithm is proposed based on optimization by minimizing a cost function. Furthermore, an adaptive tracking control strategy with disturbance compensation is developed, such that the tracking error of nonlinear systems is convergent and closed‐loop systems are strongly robust. Finally, a comparing simulation is provided to demonstrate the effectiveness of the proposed approach.