Robust Adaptive Controller Combined With a Linear Quadratic Regulator Based on Kalman Filtering

Kanieski, João Marcos; Tambara, Rodrigo Varella; Pinheiro, Humberto; Cardoso, Rafael; Gründling, Hilton Abílio

doi:10.1109/tac.2015.2468651

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…However, in practice, not all state variables are directly measurable in the case of large systems while any measurements would contain noise. Thus, it becomes imperative to estimate unavailable state variables from the available measurements of state variables using a state observer [9,15,20]. The present paper proposes to use the Kalman Estimator for estimating states from the available measurement of frequency change.…”

Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Pillai

Samuel

Unnikrishnan

2020

Prot Control Mod Power Syst

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Combined estimation of state and feed-back gain for optimal load frequency control is proposed. Load frequency control (LFC) addresses the problem of controlling system frequency in response to disturbance, and is one of main research areas in power system operation. A well acknowledged solution to this problem is feedback stabilization, where the Linear Quadratic Regulator (LQR) based controller computes the feedback gain K from the known system parameters and implements the control, assuming the availability of all the state variables. However, this approach restricts control to cases where the state variables are readily available and the system parameters are steady. Alternatively, by estimating the states continuously from available measurements of some of the states, it can accommodate dynamic changes in the system parameters. The paper proposes the technique of augmenting the state variables with controller gains. This introduces a non-linearity to the augmented system and thereby the estimation is performed using an Extended Kalman Filter. This results in producing controller gains that are capable of controlling the system in response to changes in load demand, system parameter variation and measurement noise.

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Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Pillai

Samuel

Unnikrishnan

2020

Prot Control Mod Power Syst

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“…To conclude this section about fully model‐based adaptation, we can cite other recent works, ie, post the latest general survey paper, which can be classified under the model‐based paradigm: for nonlinear models,) for models with time delay,) with parameter‐independent realization controllers, with input/output quantization,) under state constraints,) under inputs and actuator‐bandwidth constraints,) for Markovian jump systems,) for switched systems,) for partial differential equation (PDE)–based models,) for nonminimum/minimum‐phase systems,) to achieve adaptive regulation and disturbance rejection,) multiple‐model and switching adaptive control,) linear quadratic regulator (LQR)–based adaptive control, model predictive control–based adaptive control,) applications of model‐based adaptive control,) for sensor/actuator fault mitigation,) for rapidly time‐varying uncertainties, nonquadratic Lyapunov function–based MRAC, for stochastic systems,) retrospective cost adaptive control, persistent excitation–free/data accumulation–based control or concurrent adaptive control, sliding mode–based adaptive control,) set‐theoretic–based adaptive controller with performance guarantees, sampled data systems, and robust adaptive control …”

Section: Model‐based Adaptive Controlmentioning

confidence: 99%

Model‐based vs data‐driven adaptive control: An overview

Benosman

2018

Adaptive Control & Signal

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In this paper, we present an overview of adaptive control by contrasting model-based approaches with data-driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model-based adaptive control and data-driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield.

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“…The LQR controller requires angular position measurement only. A robust reference model-based adaptive controller is combined with LQR based on Kalman filtering is proposed in [16] to deal with unmodelled dynamics, uncertainties, and disturbances. In [17], an optimal state feedback controller based on model linearization along the desired trajectories is designed for a 3-DOF helicopter.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Interval Type-2 Fuzzy Logic Control of a Three Degree-of-Freedom Helicopter

Chaoui

Yadav²,

Ahmadi

et al. 2020

Robotics

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This paper combines interval type-2 fuzzy logic with adaptive control theory for the control of a three degree-of-freedom (DOF) helicopter. This strategy yields robustness to various kinds of uncertainties and guaranteed stability of the closed-loop control system. Thus, precise trajectory tracking is maintained under various operational conditions with the presence of various types of uncertainties. Unlike other controllers, the proposed controller approximates the helicopter’s inverse dynamic model and assumes no a priori knowledge of the helicopter’s dynamics or parameters. The proposed controller is applied to a 3-DOF helicopter model and compared against three other controllers, i.e., PID control, adaptive control, and adaptive sliding-mode control. Numerical results show its high performance and robustness under the presence of uncertainties. To better assess the performance of the control system, two quantitative tracking performance metrics are introduced, i.e., the integral of the tracking errors and the integral of the control signals. Comparative numerical results reveal the superiority of the proposed method by achieving the highest tracking accuracy with the lowest control effort.

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Robust Adaptive Controller Combined With a Linear Quadratic Regulator Based on Kalman Filtering

Cited by 25 publications

References 25 publications

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Optimal load frequency control through combined state and control gain estimation for noisy measurements

Model‐based vs data‐driven adaptive control: An overview

Adaptive Interval Type-2 Fuzzy Logic Control of a Three Degree-of-Freedom Helicopter

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