Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks

Vatankhah, Bahareh; Farrokhi, Mohammad

doi:10.1016/j.jfranklin.2017.06.005

Cited by 24 publications

(23 citation statements)

References 31 publications

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“…Proof: Since the optimization problem (26) is feasible, (27) and (28) are satisfied and (28) guarantees (24). Since…”

Section: Online Mode-dependent Mpc Designmentioning

confidence: 99%

“…Thus the optimization problems of NMPC are nonlinear and nonconvex, which are difficult to solve even for cases involving only few variables [23]. If the nonlinear items can be represented by neural network model [28], T-S fuzzy model [29]- [31] or polyhedral model [23], [32], [33], the linear analytical expressions of original systems can be obtained and the relatively mature results on stability and feasibility of linear MPC can be applied in nonlinear MJSs. Considering the nonlinear MJS with nonhomogeneous process, the constrained MPC design was proposed and avoids solving nonlinear optimization problem through applying a differential-inclusion-based design [23].…”

Section: Introductionmentioning

confidence: 99%

“…For linear MJSs subjected to the linear combinational constraints of states and input controls, constrained MPC synthesis based on coupled invariant sets can guaranteed the convergence of the closed loop responses [19], [24]. Considered input-to-state stable problems and Lyapunov-like sufficient conditions, MPC scheme was a powerful tool for nonlinear systems subjected to hard constraints and disturbances [28], [29], [32], [33]. Using quadratic boundedness, the receding-horizon estimators were designed for linear systems with bounded disturbances [34], [35].…”

Section: Introductionmentioning

confidence: 99%

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Constrained Model Predictive Control for Nonlinear Markov Jump System With Persistent Disturbance via Quadratic Boundedness

Feng

Gao

2020

IEEE Access

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In this paper, a robust quadratic-boundedness-based model predictive control (MPC) scheme, for a discrete-time nonlinear Markov jump system (MJS), is extended to the case with persistent bounded disturbance and nonhomogeneous transition probability. By applying the S-procedure, the constraint conditions, the persistent bounded disturbance and the sufficient stability conditions are all derived in term of a few linear matrix inequalities (LMIs), thus the original min-max optimization problem is transformed into a convex optimization problem in LMI paradigm. At each sampling time, the control moves satisfying the control constraint are obtained online and implemented in the nonlinear MJS. Quadratically boundedness and min-max MPC are combined to achieve the closed-loop stochastic stability of the controller with respect to the persistent bounded disturbance. A numerical example is presented to demonstrate the effectiveness of the proposed results. INDEX TERMS Model predictive control, nonlinear Markov jump system, quadratic boundedness, persistent disturbance, stochastic stability, linear matrix inequality.

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“…Proof: Since the optimization problem (26) is feasible, (27) and (28) are satisfied and (28) guarantees (24). Since…”

Section: Online Mode-dependent Mpc Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Constrained Model Predictive Control for Nonlinear Markov Jump System With Persistent Disturbance via Quadratic Boundedness

Feng

Gao

2020

IEEE Access

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“…where e(k) is the increment of the estimation error, which can be formulated as [34] e(k) = (∂e(k)/∂W(k)) T W(k)…”

Section: A the Online Learning Rbfnnmentioning

confidence: 99%

Optimal Design and Tracking Control of a Superelastic Flexure Hinge Based 3-PRR Compliant Parallel Manipulator

et al. 2019

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A major obstacle that restricts the application of notched flexure hinge based compliant mechanism is its limited motion range. This paper presents the optimal design and tracking control of a superelastic flexure hinge based 3-PRR compliant parallel manipulator (CPM) to achieve high precision planar motion within centimeter's translation range and up to 10 degrees' rotational range. Firstly, a novel asymmetric ellipse-parabola (AEP) notch shape is proposed, and the geometric parameters of the AEP superelastic flexure hinge are acquired via multi-objective optimization to obtain desirable transmission performance. Secondly, a nominal inverse kinematic model of the CPM is established, and the dimension parameters of the 3-PRR manipulator are synthesized to maximize the dexterity of the CPM over the regular workspace. Thereafter, a disturbance observer based inverse kinematic control scheme (DOB-IKM) is proposed to suppress the model mismatches and external disturbances of the 3-PRR CPM, where the unmodeled factors of the system are approximated through an online learning radical basis function neural network (RBFNN) and the external disturbances of the CPM is observed and compensated by a disturbance observer (DOB). Finally, a prototype of the 3-PRR CPM is manufactured, and experimental tests show the effectiveness of the proposed control scheme and the superiority of the 3-PRR CPM. INDEX TERMS Compliant parallel manipulator, inverse kinematics, superelastic flexure hinge, tracking control.

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“…Gelişen teknoloji ile birlikte çalışma sırasında modelin optimize edildiği uyarlamalı MÖK çalışmaları literatürde yerini almıştır. Örneğin[27]'de sistem parametrelerindeki belirsizliklerle mücadele etme amacı ile adaptif yapay sinir ağı geliştirilmiş ve MÖK içerisinde tahmin modeli olarak kullanılmıştır. Başka bir çalışmada ise götit prosesine ağırlıklı sürekli karıştırma reaktörü modeli uygulanmış ve Lyapunov fonksiyonu kullanılarak sistem tanımlama ile elde edilen model çalışma sırasında özgün yazılım ile optimize edilmiştir[28].…”

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Gradient Based Identification of Wiener Systems

Aliskan¹

2018

Pamukkale J Eng Sci

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Tek bir bloktan oluşan sistemlerde geribesleme ile kontrol ve giriş-çıkış ilişkisinin kurulması klasik kontrolde yaygın olarak görülmektedir. Fakat doğal sistemler göz önüne alındığında çok bloklu yapılar ve bu bloklar içerisinde de lineer olmayan fonksiyonlar görülür. Bu çalışmanın konusu, giriş işaretinin lineer bloka uygulandığı ve çıkışın lineer olmayan fonksiyondan alındığı sistem yapısı olan Wiener tipi sistemlerin tanımlamasıdır. Durum geribeslemenin mümkün olmadığı bu sistem tipinde tanımlama ile farklı kontrol algoritmalarının kullanımı mümkündür. Sistem tanımlamada harici girişli otoregresif ağ (ARX)-polinom kaskad bağlantısı tercihi ile en küçük kareler yöntemi ve eğim bilgileri sayesinde sistemin giriş-çıkışı arasındaki matematiksel ilişki elde edilmiştir. Üç farklı örnek sistem üzerinde çalışmalar yapılmış, MATLAB/Simulink ortamında veri kümeleri elde edilmiş ve yapılan sistem tanımlamalarının başarımı grafikler ile sunulmuştur. In systems consisting of a single block, the establishment of feedback control and input-output relationship is common in classical control. However, considering natural systems, there are many block structures and non-linear functions within these blocks. The subject of this study is the identification of Wiener type systems, which is the system structure in which the input signal is applied to the linear block and its output is taken from the nonlinear function. It is possible to use different control algorithms with identification in this type of system where state feedback control is not possible. In system identification, the mathematical relationship between the input and output of the system has been obtained by the choice of auto regressive with exogenous inputs (ARX)-polynomial cascade connection with using the least squares method and gradient information. Three different benchmark systems have been studied in MATLAB/Simulink and the performances of the identifications made with the dataset are presented graphically.

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Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks

Cited by 24 publications

References 31 publications

Constrained Model Predictive Control for Nonlinear Markov Jump System With Persistent Disturbance via Quadratic Boundedness

Constrained Model Predictive Control for Nonlinear Markov Jump System With Persistent Disturbance via Quadratic Boundedness

Optimal Design and Tracking Control of a Superelastic Flexure Hinge Based 3-PRR Compliant Parallel Manipulator

Gradient Based Identification of Wiener Systems

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