Iterative Data-Driven Tuning of Controllers for Nonlinear Systems With Constraints

Rădac, Mircea-Bogdan; Precup, Radu‐Emil; Petriu, Emil M.; Preitl, Ștefan

doi:10.1109/tie.2014.2300068

Cited by 71 publications

(46 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the recent research works [6,9], nonlinear fractional-order systems with affine structures have been also investigated. • Nonaffinity: However, there exist several nonlinear nonaffine systems in practice, such as chemical reactors, biochemical processes, some aircraft and pendulum dynamical models, and so on [1,10,12,19,20,23,25,26,44,50,56,58]. Due to the great efforts devoted by researchers, remarkable adaptive control approaches have been developed for non-affine systems [19,44,50,56].…”

Section: Introductionmentioning

confidence: 99%

“…These facts require specific control tools to deal with the controller design process, being one of the most extended ones the adaptive control paradigm. The conventional controllers have been constructed thanks to the mathematical models of identified plants from the input-output data [43][44][45][46][47]. For coping with conventional controller problems, data-driven control techniques have been used in many researches [44].…”

Section: Introductionmentioning

confidence: 99%

“…The conventional controllers have been constructed thanks to the mathematical models of identified plants from the input-output data [43][44][45][46][47]. For coping with conventional controller problems, data-driven control techniques have been used in many researches [44]. As presented in [43][44][45][46], iterative feedback tuning, correlation-based tuning, frequency-domain tuning, iterative regression tuning, simultaneous perturbation stochastic approximation, data-driven predictive control, model-free adaptive control, model-free control, unfalsified control, adaptive online iterative feedback tuning, data-driven reinforcement learning control, and model-free or datadriven iterative learning control represent the main iterative data-driven controller tuning techniques.…”

Section: Introductionmentioning

confidence: 99%

“…For coping with conventional controller problems, data-driven control techniques have been used in many researches [44]. As presented in [43][44][45][46], iterative feedback tuning, correlation-based tuning, frequency-domain tuning, iterative regression tuning, simultaneous perturbation stochastic approximation, data-driven predictive control, model-free adaptive control, model-free control, unfalsified control, adaptive online iterative feedback tuning, data-driven reinforcement learning control, and model-free or datadriven iterative learning control represent the main iterative data-driven controller tuning techniques. Besides, virtual reference feedback tuning is a ''oneshot'' direct data-based controller design method which converts the model reference control problem into an identification problem [43].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

View full text Add to dashboard Cite

This paper focuses on the adaptive neural output feedback control of a class of uncertain multi-input-multioutput nonlinear time-delay non-integer-order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. Thus, the mean value theorem and a Gaussian error function-based continuous differentiable model are used in the paper to describe the unknown asymmetric saturation actuator and to get an affine model in which the control input appears in a linear fashion, respectively. The design of the controller follows a number of steps. Firstly, based on the semigroup property of fractional-order derivative, the system is transformed into a normalized fractional-order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real condition, the Razumikhin Lemma, the frequency-distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof. The main advantages of this work are that:(1) it can handle systems with constant, time-varying, and distributed time-varying delays, (2) the considered class of systems is relatively large, (3) the number of adjustable parameters is reduced, (4) the tracking errors converge asymptotically to zero and all signals of the closed-loop system are bounded. Finally, some simulation examples are provided to demonstrate the validity and effectiveness of the proposed scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Based on these studies, in this paper, the authors propose an L 1 adaptive controller for a two-input-two-output vertical rotor orientation control system and design an experimental rig based on dSPACE to test the performance of the rotor orientation control system. The work in this paper may also be applied to twin-rotor mechanical system control [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

L1 Adaptive Control for a Vertical Rotor Orientation System

Liu

Yang

et al. 2016

Applied Sciences

View full text Add to dashboard Cite

Bottom-fixed vertical rotating devices are widely used in industrial and civilian fields. The free upside of the rotor will cause vibration and lead to noise and damage during operation. Meanwhile, parameter uncertainties, nonlinearities and external disturbances will further deteriorate the performance of the rotor. Therefore, in this paper, we present a rotor orientation control system based on an active magnetic bearing with L 1 adaptive control to restrain the influence of the nonlinearity and uncertainty and reduce the vibration amplitude of the vertical rotor. The boundedness and stability of the adaptive system are analyzed via a theoretical derivation. The impact of the adaptive gain is discussed through simulation. An experimental rig based on dSPACE is designed to test the validity of the rotor orientation system. The experimental results show that the relative vibration amplitude of the rotor using the L 1 adaptive controller will be reduced to ∼50% of that in the initial state, which is a 10% greater reduction than can be achieved with the nonadaptive controller. The control approach in this paper is of some significance to solve the orientation control problem in a low-speed vertical rotor with uncertainties and nonlinearities.

show abstract

Prescribed performance control of Euler–Lagrange systems with input saturation and output constraints

Zhang

Yang

Zhang

et al. 2022

Adaptive Control & Signal

View full text Add to dashboard Cite

In this article, we investigate the performance-guaranteed tracking control for Euler-Lagrange (EL) systems subject to input saturation and output constraints.The prescribed performance and output constraints are first considered for EL systems with input saturation. Time-varying barrier Lyapunov function (BLF) is employed to ensure constraint satisfaction. An adaptive control method for EL systems with saturation is put forward by adding a speed transformation into the BLF and designing auxiliary variables to the system. Subsequently, neural network is introduced to compensate for uncertainties of systems. In combination with backstepping technique, it is demonstrated that the controller not only guarantees that the tracking error converges to a preset compact set near zero at a specified decay rate, but also ensures that the output always stays within a given boundary. Simulation results further demonstrate the effectiveness of the proposed control scheme.

show abstract

Iterative Data-Driven Tuning of Controllers for Nonlinear Systems With Constraints

Cited by 71 publications

References 49 publications

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

L1 Adaptive Control for a Vertical Rotor Orientation System

Prescribed performance control of Euler–Lagrange systems with input saturation and output constraints

Contact Info

Product

Resources

About