Nonlinear leader-following MASs control: a data-driven adaptive sliding mode approach with prescribed performance

Zhang, Yanan; Song, Jiacheng

doi:10.1007/s11071-022-07218-8

Cited by 9 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where βM (k) = 𝛽 * M − 𝛽 M (k). Let's combine the learning laws in ( 18) and ( 19) and set 𝜂 u = 𝜂 x = 𝜂 as (20), thus, we have…”

Section: Dynamic Data-equivalent Model: Ddemmentioning

confidence: 99%

“…By selecting the learning rate according to (20), it results in the convergence of weight parameters. Subsequently, let's recall the dynamics of the model error in (23) in conjunction with the one-step back of ( 24), yielding:…”

Section: Dynamic Data-equivalent Model: Ddemmentioning

confidence: 99%

“…16,17 However, DSMC schemes still exhibit certain limitations: (i) the chattering problem, stemming from high-frequency components in the control signal 18,19 ; (ii) performance dependence on the uncertainty characteristics of the controlled plant under non-matching conditions. 20,21 The foremost consideration in designing control systems, particularly in practical implementations, is to ensure robustness under constraints and uncertainties. 22,23 In switching-type reaching law-based Sliding Mode Control (SMC), the inclusion of the switching term enables the attainment of a switching quasi-sliding mode (QSM) motion in the controlled system.…”

Section: Introductionmentioning

confidence: 99%

“…Building upon this foundation and the motivations discussed earlier, discrete‐time sliding mode control (DSMC) schemes have been introduced to enhance the performance of controlled systems, notably BLDC, within the discrete‐time domain 16,17 . However, DSMC schemes still exhibit certain limitations: (i) the chattering problem, stemming from high‐frequency components in the control signal 18,19 ; (ii) performance dependence on the uncertainty characteristics of the controlled plant under non‐matching conditions 20,21 …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Treesatayapun

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article presents an adaptive controller tailored for discrete‐time systems, emphasizing robustness and addressing time‐varying constraints. An equivalent model is constructed solely from input‐output data collected from an unknown plant. Performance optimization involves determining an appropriate learning rate guided by theoretical analysis. A control law is derived from the equivalent model and a proposed auxiliary controller, with parameter settings guided by theoretical analysis to achieve robust convergence in closed‐loop performance. Comprehensive experimental and comparative assessments confirm the superior performance of both the equivalent model and the proposed controller, especially in brushless DC motor position control. Moreover, compared to similar schemes, the proposed controller generates fewer high‐frequency control effort components. The investigation validates the approach's robustness in real‐world conditions, including uncertainties, disturbances, and common time‐varying constraints in practical plant scenarios.

show abstract

“…where βM (k) = 𝛽 * M − 𝛽 M (k). Let's combine the learning laws in ( 18) and ( 19) and set 𝜂 u = 𝜂 x = 𝜂 as (20), thus, we have…”

Section: Dynamic Data-equivalent Model: Ddemmentioning

confidence: 99%

Section: Dynamic Data-equivalent Model: Ddemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Treesatayapun

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…The nonlinear control problem has been widely studied because many practice systems are usually described as nonlinear models [1][2][3], such as permanent magnet synchronous motors [4], induction motor systems [5,6], and four-tank systems [7]. To achieve a better control effect, a series of nonlinear control techniques, such as adaptive control and the backstepping technique, are developed [8][9][10][11][12]. An adaptive backstepping method, which comes from the combination of adaptive control and the backstepping technique, is an important technique to handle the parameters uncertainty of high-order nonlinear systems [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Asymptotic Control for a Class of Large-Scale High-Order Unknown Nonlinear Systems

Ju.

Song

2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

This paper studies the asymptotic control problem of a class of large-scale high-order nonlinear systems (LSHONSs), and an asymptotic fuzzy adaptive dynamic surface controller is developed. Unknown nonlinear terms are learned online by fuzzy logic systems (FLSs) such that the accurate nonlinear model is released in the controller design procedure, where the parameters of FLSs are updated by developing adaptive laws. To compensate for the “boundary error” caused by the dynamic surface control method where a linear filter is added in the backstepping procedure to handle the “explosion of complexity” problem, a nonlinear filter is proposed to eliminate the boundary layer error. Some simulations are given to demonstrate the effectiveness of the proposed algorithm.

show abstract

Event‐triggered model‐free adaptive SMC for nonlinear NCSs with deception attacks

Zhang,

Niu,

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This work presents an event‐triggered model‐free adaptive sliding mode control (MFASMC) strategy for nonlinear networked control systems (NCSs) without and with deception attacks. First, a time‐varying pseudo‐partial derivative is introduced to transform the nonlinear NCSs into a linearized model. In the secure environment without attacks, an event‐triggered MFASMC law is proposed based on the designed sliding surface. Then, considering that the transmitted signal is vulnerable to deception attacks, it assumes that the attacker injects false data into the output signal in a probabilistic manner. Under this case, the above event‐triggered MFASMC scheme is implemented by means of attacked output signals. And the network burden can be reduced while ensuring the reachability of the specified sliding surface and the output tracking performance. The key feature of the proposed MFASMC algorithm is that secure tracking control can be achieved using only input/output data without relying on any model information. Finally, the proposed control strategy is verified via the steam‐water heat exchanger.

show abstract

Nonlinear leader-following MASs control: a data-driven adaptive sliding mode approach with prescribed performance

Cited by 9 publications

References 42 publications

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Dynamic data model and robust controller with high‐frequency component reduction for unknown time‐varying discrete‐time systems

Fuzzy Adaptive Asymptotic Control for a Class of Large-Scale High-Order Unknown Nonlinear Systems

Event‐triggered model‐free adaptive SMC for nonlinear NCSs with deception attacks

Contact Info

Product

Resources

About