An improved model-free adaptive control for nonlinear systems: An LMI approach

Jin, Xiao‐Zheng; Ma, Yong‐Sheng; Che, Wei‐Wei

doi:10.1016/j.amc.2023.127910

Cited by 7 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, model-free control is gradually receiving much attention recently due to its modelindependent characteristics [18,19]. In the recent years, several model-free control methods that only rely on input and output system data have been developed, such as active disturbance rejection control [20,21], dynamiclinearization-based model free adaptive control [22,23], iterative learning control [24,25], etc. Moreover, on the basis of ultra-local model (ULM) principle, some enhanced and effective model-free controllers have been proposed [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Tian

et al. 2023

Preprint

View full text Add to dashboard Cite

In this paper, a model-free fractional-order adaptive prescribed performance controller (MFF-APPC) is designed for mechatronic systems in presence of uncertainties, external disturbances and actuator failure. The MFF-APPC is composed of ultra-local model (ULM)-based time-delay estimation (TDE), Proportional-Differential (PD) controller and fractional-order adaptive prescribed performance compensator (FAPPC). The ULM is utilized to approximate the complex mechatronic system in an extra-short sliding time window and TDE is employed to observe and eliminate the lumped disturbance. Then, PD controller is designed to stabilize the closed-loop system. Correspondingly, the TDE-based PD (TDE-PD) controller is constructed. However, there always exists estimation error under TDE technique and the tracking performance cannot be guaranteed. Hence, the FAPPC is designed and introduced into TDE-PD controller to eliminate the effect of estimation error on control performance and constrain the tracking error with prescribed convergence time and precision. Moreover, an adaptive parameter is designed to approximate the upper bound of estimation error in TDE and thus reduces the unwanted chattering on the switching manifold. Correspondingly, the MFF-APPC is constructed. Furthermore, the stability of closed-loop system under the proposed MFF-APPC is analyzed by using Lyapunov theorem. To validate the proposed method, the numerical simulation on 2-DOF robotic manipulator and co-simulation on virtual prototype of 7-DOF iReHave upper limb exoskeleton are completed. Through the compared simulation results with supervising switching technique based adaptive model-free controller (SST-AMFC) and robust fault tolerant controller (RFTC), the proposed strategy shows better trajectory tracking with prescribed performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Tian

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Salighe,

Söffker

2024

Proc Appl Math and Mech

View full text Add to dashboard Cite

This paper discusses a strategy for optimizing the complexity of time‐varying data models as used in model‐free adaptive control (MFAC). Here the dynamic linearization in compact form (CFDL), partial form (PFDL), and full form (FFDL) are considered as data models used to describe input/output (I/O) data sets. These data models are built only for control purpose and can have various degrees of complexity depending on the size of the considered time‐window as well as the underlying algorithms. The methodology is to compare the performance of the data models according to an evaluation criterion, to analyze the order of different data models based on bias‐variance trade‐off, and to select the best‐performing model. The complexity of the selected model is compared with the reduced linear time‐invariant (LTI) model obtained by applying a combination of the eigensystem realization algorithm (ERA) and observer/Kalman filter identification (OKID) on the same I/O dataset. The I/O data are obtained from applying the MFAC controllers on a nonlinear three‐tank system (3TS) to track various desired references. The results indicate that the model complexity optimization can also be used for selecting an appropriate MFAC data model with optimal order to reduce the complexity and computational burden of the MFAC control algorithms.

show abstract

Data‐driven prescribed performance platooning sliding mode control under DoS attacks

Zhang,

Che

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

SummaryIn this paper, a prescribed performance model‐free adaptive platooning sliding mode control (PP‐MFAP‐SMC) problem for the nonlinear vehicular platooning systems (VPSs) under denial‐of‐service (DoS) attacks is studied. Firstly, the partial form dynamic linearization (PFDL) technique is employed to convert the nonlinear VPSs into an equivalent linear data model, in which the nonlinear features of the VPSs are compressed into an unknown time‐varying pseudo gradient (PG) vector. Then, an observer is devised to acquire the estimation value of the unknown time‐varying PG vector. To lower the complication of the design, the constrained tracking error is converted into the unconstrained one. Based on which, the sliding mode control (SMC) strategy is proposed to enhance the robustness of the VPSs. Further, a PP‐MFAP‐SMC algorithm with an attack compensation mechanism is developed to ensure that the vehicular tracking errors of the position and velocity can converge to the predefined regions, respectively. Eventually, the effectiveness of the developed algorithm is demonstrated by an actual VPS with the comparisons.

show abstract

An improved model-free adaptive control for nonlinear systems: An LMI approach

Cited by 7 publications

References 25 publications

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Data‐driven prescribed performance platooning sliding mode control under DoS attacks

Contact Info

Product

Resources

About