Finite-time tracking control for strict-feedback nonlinear systems with full state constraints

Wang, Chunxiao; Wu, Yuqiang

doi:10.1080/00207179.2017.1397290

Cited by 59 publications

(28 citation statements)

References 22 publications

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“…According to (25), (39), and (52), one can easily conclude that ė x , ė y , and ė z are also bounded. Thus,…”

Section: Proposed Drug Dosage Regimensmentioning

confidence: 88%

“…Substitution of the control law (38) into the error dynamics ė x = x˙− ẋ and using (38) obtains (39) in which θ g = θ g − θ^g. Consider the following function:…”

Section: Proposed Drug Dosage Regimensmentioning

confidence: 99%

“…Thus, it is more flexible in designing controllers for high-order non-linear system models [32]. This control approach has been applied to various non-linear important systems such as missile systems [33][34][35], power systems [36], vehicle systems [37,38], pure and strictfeedback non-linear systems [39][40][41], and many other systems. This paper applies back-stepping design procedure to propose a controller for cancer treatment.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive back‐stepping cancer control using Legendre polynomials

Khorashadizadeh

Kalat

2020

IET Systems Biology

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Here, a model-free controller for cancer treatment is presented. The treatment objective is to find a proper drug dosage that can reduce the population of tumour cells. Recently, some solutions have been proposed according to the control theory. In these approaches, based on the mathematical description of the number of effector cells, tumour cells, and concentration of the interleukin-2 (IL-2), a non-linear controller is designed. Here, based on the back-stepping design procedure and function approximation property of Legendre polynomials, a novel controller for MIMO cancer immunotherapy is presented. In fact, Legendre polynomials play the role of uncertainty estimation and compensation. In comparison with other uncertainty estimators such as neural networks, Legendre polynomials have simpler structure. Thus, the contribution of this study is simplifying the design procedure and reducing the controller computational load in comparison with Neuro-Fuzzy controllers. The resulting closed-loop system is capable of overcoming various uncertainties. Simulation results verify the efficiency of the proposed method in the fast reduction of tumour cells. Moreover, a comparison between the performance of Legendre polynomials and a radial basis functions neural network (RBFN) is presented.

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“…According to (25), (39), and (52), one can easily conclude that ė x , ė y , and ė z are also bounded. Thus,…”

Section: Proposed Drug Dosage Regimensmentioning

confidence: 88%

“…Substitution of the control law (38) into the error dynamics ė x = x˙− ẋ and using (38) obtains (39) in which θ g = θ g − θ^g. Consider the following function:…”

Section: Proposed Drug Dosage Regimensmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive back‐stepping cancer control using Legendre polynomials

Khorashadizadeh

Kalat

2020

IET Systems Biology

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“…With the development of BLF-based control, various types of BLF have been reported in the literature. For instance, the log-type BLF (lBLF) (Liu and Tong, 2016; Qiu et al, 2015; Tee and Ge, 2009; Tee et al, 2009b; Wang et al, 2016, 2017), the integral-type BLF (Li et al, 2016; Tee and Ge, 2012) and the tan -type BLF (tBLF) (Jin, 2015, 2016; Wang and Wu, 2017). Compared with the conventional lBLF and integral-type BLF, tBLF has the advantage that it imposes a unified form on the Lyapunov function for both systems with and without constraints.…”

Section: Introductionmentioning

confidence: 99%

BLF-based asymptotic tracking control for a class of time-varying full state constrained nonlinear systems

Wang

2019

Transactions of the Institute of Measurement and Control

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This paper presents an adaptive control based on a tan-type barrier Lyapunov function for time-varying state constrained strict-feedback nonlinear systems to achieve asymptotic output tracking. The design procedure is constructive and yields a continuous actual control law, which guarantees that asymptotic output tracking is achieved. Furthermore, we show that the time-varying full state constraints are not violated for all time and that all closed-loop signals are bounded provided that some conditions on the initial states and control parameters are satisfied. Three simulation examples are provided to illustrate the performance of the proposed control scheme. One is a comparison study simulation, another is a practical single-link robot system and the last is a three-order nonlinear system.

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“…It is in this way that the constraint conditions can be guaranteed. There are various type of BLF in the existing literature works, such as the log‐type BLF (lBLF) [25, 27, 33], the integral‐type BLF (iBLF) [26, 32], and the tan‐type BLF (tBLF) [28, 31]. Compared with the conventional lBLF and iBLF, the tBLF has obvious predominance in constraint analysis.…”

Section: Introductionmentioning

confidence: 99%

Finite-time tracking control for strict-feedback nonlinear systems with full state constraints

Cited by 59 publications

References 22 publications

Adaptive back‐stepping cancer control using Legendre polynomials

Adaptive back‐stepping cancer control using Legendre polynomials

BLF-based asymptotic tracking control for a class of time-varying full state constrained nonlinear systems

Adaptive finite‐time tracking control for time‐varying output constrained non‐linear systems with unmatched uncertainties

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