Reduced-Order Filters-Based Adaptive Backstepping Control for Perturbed Nonlinear Systems

This study proposes a high gain observer-based filtered backstepping control scheme for nonlinear systems with unknown dynamics and dead-zone constraint. Majority of the previously papers describe dead-zone by the conventional certain models, assume that all state variables are available and invoke the backstepping or conventional dynamic surface control (CDSC) approaches for controller design. This work describes the dead-zone by the fuzzy model, invokes radial basis function neural network (RBFNN) to model the unknown functions and proposes a RBFNN-based high gain observer to estimate unmeasurable states. To avoid the complexity explosion in the backstepping approach and eliminate the sensitivity to the time-constant of the first-order filters in the CDSC, a nonlinear tracking differentiator (NTD) with finite time convergence property is used instead of the first-order filters in the CDSC to obtain derivative of the virtual inputs. Moreover, the error compensation mechanism and auxiliary signal are proposed to eliminate the influence of the filtering error and input nonlinearity, respectively. Despite the fuzzy dead-zone, the proposed scheme makes the closed-loop signals uniformly ultimately bounded. In comparison with the existing results, (i) some assumptions like certain model of dead-zone and full state measurement are removed, (ii) "explosion of complexity" and sensitivity to the time constant of the first-order filters are eliminated, (iii) effect of the filtering error and input nonlinearity are compensated, (iv) number of adjustable parameters and online computational burden are decreased effectively. Simulation results on the two well-known examples verify the effectiveness and applicability of the proposed new design technique.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive neural network observer‐based filtered backstepping control for nonlinear systems with fuzzy dead‐zone and uncertainty

Shahriari-kahkeshi

Jahangiri‐heidari

Shi

2023

“…Under the systematic framework of backstepping technique, utilizing the approximator‐based FLSs or NNs with adaptive control approaches have developed rapidly, which can estimate the unknown parameters and unknown functions. Up to now, plenty of valuable results have been obtained in References 13‐17. In References 18 and 19, the approximation property of the FLSs was applied to deal with the tracking control problems for strict‐feedback nonlinear systems and uncertain switched nonlinear systems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural finite‐time output‐feedback tracking control for nonlinear stochastic nonstrict‐feedback systems with input saturation

Wang

Shen

2022

Summary In this paper, an observer‐based adaptive neural output‐feedback control scheme is developed for a class of nonlinear stochastic nonstrict‐feedback systems with input saturation in finite‐time interval. The mean value theorem and the property of the smooth function are applied to cope with the difficulties caused by the existence of input saturation. According to the universal approximation capability of the radial basis function neural network, it will be utilized to compensate the unknown nonlinear functions. Based on the state observer, the finite‐time Lyapunov stability theorem, we propose an adaptive neural output‐feedback control scheme for nonlinear stochastic systems in nonstrict‐feedback form. The developed controller guarantees that the system output signal can track the given reference signal trajectory, and all closed‐loop signals are semi‐globally finite‐time stability in probability. The observer errors and the tracking error can converge to a small neighborhood of the origin. Finally, simulation results demonstrate the effectiveness of the developed control scheme.

“…At present, the main disturbance rejection control methods contain disturbance observer-based control 1 and disturbance compensation control. [2][3][4] Moreover, adaptive control is also utilized in rejecting disturbances, [5][6][7][8][9] such as the presented robust adaptive control scheme minimizing the influence of periodic disturbance. 10 Model reference adaptive control (MRAC), as an important adaptive control method, is often applied to address the uncertainties of systems, [11][12][13][14] and it can also be used to solve disturbance rejection problems.…”

Section: Introductionmentioning

confidence: 99%

Discrete time partial‐state feedback model reference disturbance rejection control

Sang

Zhang

2022

Self Cite

This article develops a disturbance rejection control strategy using partial-state feedback model reference adaptive control for discrete-time uncertain systems with unknown input disturbances. Adaptive control schemes are proposed for constant disturbances, sinusoidal disturbances, and generic bounded disturbances. The partial-state feedback control is designed based on measurable signals and the parameterization method to counteract the effect of disturbances.The plant-model output matching condition is established. The developed control law is more flexible for applications and has a more concise structure than output feedback control. Additionally, closed-loop stability and asymptotic output tracking are derived. The effectiveness and the feasibility of partial-state feedback disturbance rejection control are verified by simulation results.