Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems

Khong, Sei Zhen; Nešić, Dragan; Tan, Ying; Manzie, Chris

doi:10.1016/j.automatica.2013.06.020

Cited by 93 publications

(70 citation statements)

References 23 publications

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“…For example, this could be done following the same approach but with a different robust nonlinear controller, obtained from the multiplicative fault tolerant controller proposed in [21]. Another improvement direction could target the convergence rate and domain of attraction of the auto-tuning algorithm, for example, by using different ES algorithms with semi-global convergence properties, [29,38,39]. Furthermore, we saw that the ES-based IFT proposed here requires less experiments per learning iteration compared with the available IFT algorithms, which require a number of experiments that is directly proportional to the number of tuned gains.…”

Section: Resultsmentioning

confidence: 99%

Multi‐parametric extremum seeking‐based iterative feedback gains tuning for nonlinear control

Benosman

2016

Intl J Robust & Nonlinear

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SUMMARYWe study in this paper the problem of iterative feedback gains auto-tuning for a class of nonlinear systems. For the class of input-output linearizable nonlinear systems with bounded additive uncertainties, we first design a nominal input-output linearization-based robust controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, that is, the robust nonlinear controller combined with the multi-parametric extremum seeking model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example.

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Section: Resultsmentioning

confidence: 99%

Multi‐parametric extremum seeking‐based iterative feedback gains tuning for nonlinear control

Benosman

2016

Intl J Robust & Nonlinear

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“…Lyapunov function based stability analysis established in [27] and extremum seeking algorithms in [28], to justify the modular design method for ILC-MPC proposed in [29], where an ES-based modular approach to design ILC-MPC schemes for a class of constrained linear systems is proposed.…”

Section: The Main Contribution Of This Work Is To Present a Rigorous mentioning

confidence: 99%

Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC)

Subbaraman

Benosman

2016

IFAC-PapersOnLine

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In this paper, we study a tracking control problem for linear time-invariant systems, with model parametric uncertainties, under input and states constraints. We apply the idea of modular design introduced in [1], to solve this problem in the model predictive control (MPC) framework. We propose to design an MPC with input-to-state stability (ISS) guarantee, and complement it with an extremum seeking (ES) algorithm to iteratively learn the model uncertainties. The obtained MPC algorithms can be classified as iterative learning control (ILC)-MPC.

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“…The technique is related to other methods previously adopted for parameter tuning [2], [10], in which however the goal is to optimise the performance and/or identify the parameters, while in the plant tuning problem the only aim is to reach the target output. It is also worth mentioning that the goal of driving the output to a desired value could be cast in terms of minimizing the norm of the error and faced as an extremum-seeking problem [15], [18], [20]. There are also interesting connections with robust optimization (see [4] for an extensive survey).…”

Section: Ggiordano@tudelftnlmentioning

confidence: 99%

“…Theorem 2: Given system (19), consider the control v = v * (y) as in (11) and assume it satisfies the bound (12) for the static problem, where ξ > 0 is a decision variable. Let ν be defined as in (15) and µ as in (21). Then, the closed-loop dynamic model is stable if…”

Section: Non-static Plantsmentioning

confidence: 99%

Model-free tuning of plants with parasitic dynamics

Bianchini

Fenu

Giordano

et al. 2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Abstract-We have recently considered the problem of tuning a static plant described by a differentiable input-output function, which is completely unknown, but whose Jacobian takes values in a known polytope of matrices: to drive the output to a given desired value, we have suggested an integral feedback scheme, whose convergence is ensured if the polytope of matrices is robustly full row rank. The suggested tuning scheme may fail in the presence of parasitic dynamics, which may destabilize the loop if the tuning action is too aggressive. Here we show that such tuning action can be applied to dynamic plants as well if it is sufficiently "slow", a property that we can ensure by limiting the integral action. We provide robust bounds based on the exclusive knowledge of the largest time constant and of the matrix polytope to which the system Jacobian is known to belong. We also provide similar bounds in the presence of parasitic dynamics affecting the actuators.

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Unified frameworks for sampled-data extremum seeking control: Global optimisation and multi-unit systems

Cited by 93 publications

References 23 publications

Multi‐parametric extremum seeking‐based iterative feedback gains tuning for nonlinear control

Multi‐parametric extremum seeking‐based iterative feedback gains tuning for nonlinear control

Extremum Seeking-based Iterative Learning Model Predictive Control (ESILC-MPC)

Model-free tuning of plants with parasitic dynamics

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