New incremental Takagi–Sugeno state model for optimal control of multivariable nonlinear time delay systems

Al‐Hadithi, Basil Mohammed; Jiménez, Agustín; Pérez-Oria, Juan

doi:10.1016/j.engappai.2015.07.006

Cited by 19 publications

(38 citation statements)

References 27 publications

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“…where H ∈ ℜ (n×q) . The optimal observer [30] solves the problem of calculating a matrix H which minimizes the cost index J:…”

Section: State Controller and Observermentioning

confidence: 99%

“…If the control objective is to approach a steady state output y r , this is equivalent to achieve the reference expanded state in incremental model [30]:…”

Section: Proposed Incremental State Controller and Observermentioning

confidence: 99%

“…Δx e (k) = x e (k) − x e (k − 1) (27) or directly by the incremental state observer [30], which is formulated as follows:…”

Section: Proposed Incremental State Controller and Observermentioning

confidence: 99%

See 2 more Smart Citations

Wind Turbine Multivariable Optimal Control Based on Incremental State Model

Adánez

Al‐Hadithi

Jiménez

2017

Asian Journal of Control

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The multivariable optimal control of a wind turbine by an approach based on incremental state model is proposed. The advantages of incremental state model in comparison with the non incremental one are that the control action cancels steady state errors and incremental state solves the problem of computing the target state, choosing zero as an objective. Linear Quadratic Regulator (LQR) and optimal state observer are applied. The effectiveness of the proposed control method, over the non incremental one, is examined by applying the linear controllers to the nonlinear wind turbine model. The results show that incremental LQR control presents good transient response and zero steady state errors, even in presence of disturbances, nonlinearities and modelling errors.

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“…where H ∈ ℜ (n×q) . The optimal observer [30] solves the problem of calculating a matrix H which minimizes the cost index J:…”

Section: State Controller and Observermentioning

confidence: 99%

“…If the control objective is to approach a steady state output y r , this is equivalent to achieve the reference expanded state in incremental model [30]:…”

Section: Proposed Incremental State Controller and Observermentioning

confidence: 99%

See 1 more Smart Citation

Wind Turbine Multivariable Optimal Control Based on Incremental State Model

Adánez

Al‐Hadithi

Jiménez

2017

Asian Journal of Control

Self Cite

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“…To obtain a good performance, a process model describing the effects of all the different inputs on all the outputs must be developed. Although the linear model predictive control is suitable for processes that are not highly nonlinear, this strategy has been applied in the control of nonlinear systems, whether for SISO systems [1][2][3] or for MIMO systems [4][5][6][7]. But many industrial processes have strong nonlinearities and predictive control is applied in order to provide satisfactory control results.…”

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

2017

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This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.

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“…Some of the methods are developed on the basis of incremental state model of the plant for zero steady state error. Multivariable optimal control based on incremental state model is presented [12] and fuzzy linear quadratic regulator is designed for better robustness [13].…”

Section: Introductionmentioning

confidence: 99%

Parameterized Disturbance Observer Based Controller to Reduce Cyclic Loads of Wind Turbine

Imran¹,

Hussain

Chowdhry

2018

Energies

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This paper is concerned with bump-less transfer of parameterized disturbance observer based controller with individual pitch control strategy to reduce cyclic loads of wind turbine in full load operation. Cyclic loads are generated due to wind shear and tower shadow effects. Multivariable disturbance observer based linear controllers are designed with objective to reduce output power fluctuation, tower oscillation and drive-train torsion using optimal control theory. Linear parameterized controllers are designed by using a smooth scheduling mechanism between the controllers. The proposed parameterized controller with individual pitch was tested on nonlinear Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code model of National Renewable Energy Laboratory (NREL)'s 5 MW wind turbine. The closed-loop system performance was assessed by comparing the simulation results of proposed controller with a fixed gain and parameterized controller with collective pitch for full load operation of wind turbine. Simulations are performed with step wind to see the behavior of the system with wind shear and tower shadow effects. Then, turbulent wind is applied to see the smooth transition of the controllers. It can be concluded from the results that the proposed parameterized control shows smooth transition from one controller to another controller. Moreover, 3p and 6p harmonics are well mitigated as compared to fixed gain DOBC and parameterized DOBC with collective pitch.

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New incremental Takagi–Sugeno state model for optimal control of multivariable nonlinear time delay systems

Cited by 19 publications

References 27 publications

Wind Turbine Multivariable Optimal Control Based on Incremental State Model

Wind Turbine Multivariable Optimal Control Based on Incremental State Model

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Parameterized Disturbance Observer Based Controller to Reduce Cyclic Loads of Wind Turbine

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