Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model

Hu, Bin; Zhao, Zhao; Liang, Jun

doi:10.1016/j.jprocont.2011.09.002

Cited by 43 publications

(26 citation statements)

References 28 publications

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“…Kaspar and Ray proposed a proportion-integration-differentiation (PID) control scheme under this framework [12], and Chen and Cheng [14] designed multi-loop adaptive PID controllers based on a modified decoupling PLS framework. Hu et al [16,17] proposed a multi-loop internal model controller in the dynamic PLS framework and achieved better performance for disturbance rejection. LÜ and Liang [18] proposed a multi-loop constrained MPC scheme.…”

Section: Dynamic Pls Model Descriptionmentioning

confidence: 99%

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Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

2017

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This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

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Section: Dynamic Pls Model Descriptionmentioning

confidence: 99%

“…Suppose {∆t r (k|k), · · · , ∆t r (k + N p − 1|k)} is the optimal sequence of solution Equation (17). At every time t, ∆t r (k|k) is added to control rate at the previous time and acts on the system.…”

Section: Stability Analysismentioning

confidence: 99%

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

2017

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“…It is used to predict behavior of structures equipped with passive controller systems. It is worth mentioning that, as the system order increases, the number of candidate terms becomes very large [18][19][20] which leads to increased complexity in the analysis [21]. The ARX is described detail in [21,22].…”

Section: Nonlinear Arxmentioning

confidence: 99%

Identification of the Response of a Controlled Building Structure Subjected to Seismic Load by Using Nonlinear System Models

Kaloop

Bigdeli

2016

Applied Sciences

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Abstract:The present study investigates the prediction efficiency of nonlinear system-identification models, in assessing the behavior of a coupled structure-passive vibration controller. Two system-identification models, including Nonlinear AutoRegresive with eXogenous inputs (NARX) and adaptive neuro-fuzzy inference system (ANFIS), are used to model the behavior of an experimentally scaled three-story building incorporated with a tuned mass damper (TMD) subjected to seismic loads. The experimental study is performed to generate the input and output data sets for training and testing the designed models. The parameters of root-mean-squared error, mean absolute error and determination coefficient statistics are used to compare the performance of the aforementioned models. A TMD controller system works efficiently to mitigate the structural vibration. The results revealed that the NARX and ANFIS models could be used to identify the response of a controlled structure. The parameters of both two time-delays of the structure response and the seismic load were proven to be effective tools in identifying the performance of the models. A comparison based on the parametric evaluation of the two methods showed that the NARX model outperforms the ANFIS model in identifying structures response.

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“…(19) was used and the resulting modeling performance is depicted in Fig. 6b, which shows a good modeling to the testing signal with a MSE of 2.759 × 10 −4 .…”

Section: Plantmentioning

confidence: 99%

“…In this context, to train the NN in the IMC, the most commonly applied learning methods in the literature are the gradient-based methods such as the back-propagation algorithm (BPA) [9,19]. However, these methods suffer from several drawbacks like the slow convergence speed and the tendency to get trapped at local minima in the search space.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Neural Network-Based NARMA-L2 Internal Model Control Utilizing Micro-artificial Immune Techniques to Control Nonlinear Systems

Lutfy

Selamat

2015

Arab J Sci Eng

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This paper presents an intelligent control strategy based on internal model control (IMC) to control nonlinear systems. In particular, a wavelet neural network (WNN)-based nonlinear autoregressive moving average (NARMA-L2) network is used to acquire the forward dynamics of the controlled system. Subsequently, the control law can be directly derived. In this approach, a single NARMA-L2 with only one training phase is required. Hence, unlike other related works, this design approach does not require an additional training phase to find the model inversion. In the literature, gradient descent methods are the most widely applied training techniques for the neural network-based IMC. However, these methods are characterized by the slow convergence speed and the tendency to get trapped at local minima. To avoid these limitations, the newly developed modified micro-artificial immune system (modified Micro-AIS) is employed in this work to train the NARMA-L2. The simulation results have demonstrated the effectiveness of the proposed approach in terms of accurate control and robustness against external disturbances. In addition, a comparative study has shown the superiority of the WNN over the multilayer perceptron and the radial basis function based IMC. Moreover, compared with the genetic algorithm, the modified Micro-AIS has achieved better results as the training method in the IMC structure.

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Multi-loop nonlinear internal model controller design under nonlinear dynamic PLS framework using ARX-neural network model

Cited by 43 publications

References 28 publications

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

Identification of the Response of a Controlled Building Structure Subjected to Seismic Load by Using Nonlinear System Models

Wavelet Neural Network-Based NARMA-L2 Internal Model Control Utilizing Micro-artificial Immune Techniques to Control Nonlinear Systems

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