Adaptive Fuzzy-Wavelet Neural Network identification core for reinforced control of general arbitrarily switched nonlinear Multi Input-Multi Output Dynamic Systems

Homaeinezhad, Mohammad Reza; Yaqubi, Sadeq

doi:10.1016/j.asoc.2020.106265

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…One can satisfy (i) by developing Lyapunov-based conventional controller, where (ii) is completely depended on the convergence condition of UIOEFIR estimation studied in Section II. Lastly, (iii) can be ensured by introducing a reset map and without the loss of generality, it can be assumed linear when the system has low natural frequency and the switching time is sufficiently small [38,39]. Now, when a fault d j,i k appears in i th sensor at time step k, u * (k) does not rely on e a anymore but e j (k) and hence, the effect of d j,i k diminished.…”

Section: Applying Uioefir In Sensor Fault-tolerant Tracking Controlmentioning

confidence: 99%

An Unknown Input Observer–EFIR Combined Estimator for Electrohydraulic Actuator in Sensor Fault-Tolerant Control Application

Nahian

Dinh

Dao

et al. 2020

IEEE/ASME Trans. Mechatron.

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Section: Applying Uioefir In Sensor Fault-tolerant Tracking Controlmentioning

confidence: 99%

An Unknown Input Observer–EFIR Combined Estimator for Electrohydraulic Actuator in Sensor Fault-Tolerant Control Application

Nahian

Dinh

Dao

et al. 2020

IEEE/ASME Trans. Mechatron.

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“…The control scheme presented in this paper is constructed based on discrete sliding mode control approach utilizing equivalent rigid model of the real system. However, due to considerable negative effects of unstructured uncertainties 27,28 in controller performance which is logically predictable in continuum mechanic systems, the control algorithm is modified through multiple levels. These modifications are basically conceived to maintain a trade‐off between closed‐loop stability and tracking performance such that when the system is getting close to stability margins, tracking performance is compromised to assist system stabilization.…”

Section: Control System Designmentioning

confidence: 99%

Controlling uncertain nonlinear structural vibrations of moving continuum system by embedding a vibration monitoring unit to feedback algorithm

Homaeinezhad

FotoohiNia

2020

Struct Control Health Monit

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This paper proposes a practical solution to control a problem of a class of continuum mechanics systems in which spatial equations corresponding to vibrational motions are not resolvable through conventional analytical approaches. Control scheme is constructed based on discrete sliding mode technique regarding a rigid model of a system such that control inputs are selected between Lyapunov stability bounds to manipulate the system in tracking reference values. The stability bounds and control input signal proximity to either bounds are updated in every simulation step in accordance to system vibration level using a practical acceleration synchronization method aside from evolution of rigid model states. To this end, the severity of vibrations is analyzed online using a limited number of acceleration sensors. These sensors are installed on critical nodes that are generally characterized with high level of vibrations, and the synchronization process is employed through comparative analysis with other nodes of which exhibit more rigidity within the structure. In order to highlight controller performance, virtual plant is assumed to be constructed from viscoelastic materials (VEMs) featuring timevarying elasticity and viscosity, and Prony series parameters are involved in modeling VEMs in ANSYS ® mechanical APDL student edition. Eventually, controller capability in stabilization of closed-loop system and tracking reference values are evaluated in finite element analysis transient environment, and simulation results are compared with those of existing methods.

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“…Assumption 6. For the closed-loop system with bounded uncertainty effects expressed in Assumption 1 and constraints indicated by Definition 5, and the set s & R m x is found which is robust positively invariant (RPI) and satisfies terminal cost conditions (13) and (14).…”

Section: Remarkmentioning

confidence: 99%

“…Naturally, timedelay effects are difficult to ascertain particularly in high-speed mechatronical systems (where time-delay effects arising from the use of various communication devices and algorithms often cannot be precisely measured) 5,6 which render the assumption of knowable time-delay effects often considered in control studies problematic. [5][6][7][8] This problem is further intensified in multi-input multi-output (MIMO) dynamical systems given the interactions between various subsystems [9][10][11][12][13] and also when considering the combination with sector-bounded modeling uncertainty in the dynamical model. 14,15 Recent publications such as the work of Naghavi et al 16 have addressed the issue of time-delay uncertainty in MPC using adaptive estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimally designed Lyapunov–Krasovskii terminal costs for robust stable–feasible model predictive control of uncertain time-delay nonlinear dynamical systems

Yaqubi

Homaeinezhad

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article details a new Model Predictive Control algorithm ensuring robust stability and control feasibility for uncertain nonlinear multi-input multi-output dynamical systems considering uncertain time-delay effects. The proposed control algorithm is based on construction of a Lyapunov–Krasovskii functional as terminal cost. Incorporation of this terminal cost into the Model Predictive Control optimization problem and calculation of the associated admissible set result in robust feasibility and robust stability of closed-loop system in presence of uncertain time-delay effects and bounded disturbance signals. The Lyapunov–Krasovskii functional term is constructed with respect to predicted sliding functions over the prediction horizon and considers the effects of dynamical variations over the prediction horizon in generation of control inputs. As dynamical variations are investigated in a sample-to-sample basis, feasible sliding regions are updated at each sample as well. Finally, based on expression of sliding functions as a combination of dynamical variations and input-based terms, required control inputs are calculated in the admissible bound by the optimization algorithm. Construction of control scheme on this basis permits straightforward calculation of robust stability and feasibility conditions for a general class of uncertain nonlinear system in finite prediction horizon whereas in the previous works, often-restrictive conditions were considered for the investigated dynamical systems. Numerical illustrations indicate precision and efficiency of control algorithm and improved stability and convergence rate for multivariable nonlinear dynamical systems considering uncertain time-delay effects. Finally, hardware-in-the-loop implementation indicates applicability of the proposed scheme in real-time control applications particularly in case appropriate compromises between optimality and calculation speed are considered.

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Adaptive Fuzzy-Wavelet Neural Network identification core for reinforced control of general arbitrarily switched nonlinear Multi Input-Multi Output Dynamic Systems

Cited by 17 publications

References 39 publications

An Unknown Input Observer–EFIR Combined Estimator for Electrohydraulic Actuator in Sensor Fault-Tolerant Control Application

An Unknown Input Observer–EFIR Combined Estimator for Electrohydraulic Actuator in Sensor Fault-Tolerant Control Application

Controlling uncertain nonlinear structural vibrations of moving continuum system by embedding a vibration monitoring unit to feedback algorithm

Optimally designed Lyapunov–Krasovskii terminal costs for robust stable–feasible model predictive control of uncertain time-delay nonlinear dynamical systems

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