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This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.
Abstract
Purpose
This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.
Design/methodology/approach
The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.
Findings
Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.
Research limitations/implications
For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.
Originality/value
The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.
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Shobha Y.K. and Rangaraju H.G.
In order to optimize BER and to substantiate performance measures, initially, the filter bank multicarrier (FBMC) quadrature amplitude modulation (QAM) performance metrics are…
Abstract
Purpose
In order to optimize BER and to substantiate performance measures, initially, the filter bank multicarrier (FBMC) quadrature amplitude modulation (QAM) performance metrics are evaluated with the cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) system. The efficiency of CP-OFDM, as well as FBMC/QAM that is transmitting over specific fading channels, is evaluated in terms of quality trade-off metrics over bit error rate (BER) as well as modulation order. When compared with the traditional FBMC systems, the proposed FBMC QAM system shows better performance. The performance metrics of FBMC/QAM with the inclusion of multiuser multiple-input-multiple-output (MUMIMO) is validated with worst case channel environment. The performance penalty gap that exists in CP- OFDM is compared with improved FBMC QAM in terms of both BER and OOB radiation measures. The BER trade off comparison between ML and MMSE optimally determine the prominent signal detection model for high performance FBMC QAM system.
Design/methodology/approach
The main objective of this research work is to provide perceptions about performance, co-channel interference avoidance as well as about the techniques that are used for minimizing the complexity of the system that is related to FBMC QAM structure for reducing intrinsic interference with higher spectral features as well as maximal likelihood (ML) detector systems.
Findings
This research work also looks at the efficiency of multiuser multiple-input-multiple-output (MU-MIMO) FBMC/QAM over nonlinear channels. Furthermore, when compared with OFDM, it also significantly reduces the penalty gap efficiency, thereby enabling the accessibility of the proposed FBMC QAM system from BER as well as implementation point of view. Finally, the signal detection is facilitated by the sub-detector and is achieved on the downlink side by making use of threshold-driven statistical measures that accurately minimize the complexity trade-off measures of the ML detector over modulation order. The computation of the proposed FBMC method’s BER performance measures was carried out through MATLAB simulation environments, as well as efficiency of the suggested work was demonstrated through detailed analyses.
Originality/value
This research work intend to combine the efficient MU-MIMO based transmission scheme with optimal FBMC/QAM for improved QoS over highly nonlinear channels which includes both delay spread and Doppler effects. And optimal signal detection model is facilitated at the downlink side by making use of threshold-driven statistical measures that accurately minimize the complexity trade-off measures of the ML detector over modulation order. The computation of the proposed FBMC method’s BER performance measures was carried out through MATLAB simulation environments, as well as efficiency of the suggested work was demonstrated through detailed analyses.
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Vinayambika S. Bhat, Thirunavukkarasu Indiran, Shanmuga Priya Selvanathan and Shreeranga Bhat
The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates…
Abstract
Purpose
The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.
Design/methodology/approach
Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.
Findings
Research indicates that integral control parameters (Ki) affect outputs substantially more than proportional control parameters (Kp). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.
Originality/value
This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.
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Xin Liu, Hang Zhang, Pengbo Zhu, Xianqiang Yang and Zhiwei Du
This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the…
Abstract
Purpose
This paper aims to investigate an identification strategy for the nonlinear state-space model (SSM) in the presence of an unknown output time-delay. The equations to estimate the unknown model parameters and output time-delay are derived simultaneously in the proposed strategy.
Design/methodology/approach
The unknown integer-valued time-delay is processed as a latent variable which is uniformly distributed in a priori known range. The estimations of the unknown time-delay and model parameters are both realized using the Expectation-Maximization (EM) algorithm, which has a good performance in dealing with latent variable issues. Moreover, the particle filter (PF) with an unknown time-delay is introduced to calculated the Q-function of the EM algorithm.
Findings
Although amounts of effective approaches for nonlinear SSM identification have been developed in the literature, the problem of time-delay is not considered in most of them. The time-delay is commonly existed in industrial scenario and it could cause extra difficulties for industrial process modeling. The problem of unknown output time-delay is considered in this paper, and the validity of the proposed approach is demonstrated through the numerical example and a two-link manipulator system.
Originality/value
The novel approach to identify the nonlinear SSM in the presence of an unknown output time-delay with EM algorithm is put forward in this work.
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Alejandro M. Suárez, Manuel A. Duarte‐Mermoud and Danilo F. Bassi
To develop a new predictive control scheme based on neural networks for linear and non‐linear dynamical systems.
Abstract
Purpose
To develop a new predictive control scheme based on neural networks for linear and non‐linear dynamical systems.
Design/methodology/approach
The approach relies on three different multilayer neural networks using input‐output information with delays. One NN is used to identify the process under control, the other is used to predict the future values of the control error and finally the third one is used to compute the magnitude of the control input to be applied to the plant.
Findings
This scheme has been tested by controlling discrete‐time SISO and MIMO processes already known in the control literature and the results have been compared with other control approaches with no predictive effects. Transient behavior of the new algorithm, as well as the steady state one, are observed and analyzed in each case studied. Also, online and offline neural network training are compared for the proposed scheme.
Research limitations/implications
The theoretical proof of stability of the proposed scheme still remains to be studied. Conditions under which non‐linear plants together with the proposed controller present a stable behavior have to be derived.
Practical implications
The main advantage of the proposed method is that the predictive effect allows to suitable control complex non‐linear process, eliminating oscillations during the transient response. This will be useful for control engineers to control complex industrial plants.
Originality/value
This general approach is based on predicting the future control errors through a predictive neural network, taking advantage of the NN characteristics to approximate any kind of relationship. The advantage of this predictive scheme is that the knowledge of the future reference values is not needed, since the information used to train the predictive NN is based on present and past values of the control error. Since the plant parameters are unknown, the identification NN is used to back‐propagate the control error from the output of the plant to the output of the controller. The weights of the controller NN are adjusted so that the present and future values of the control error are minimized.
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Hong-Sen Yan, Zhong-Tian Bi, Bo Zhou, Xiao-Qin Wan, Jiao-Jun Zhang and Guo-Biao Wang
The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).
Abstract
Purpose
The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).
Design/methodology/approach
The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.
Findings
Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.
Research limitations/implications
Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.
Practical implications
MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.
Social implications
The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.
Originality/value
Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.
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Kamel Sabahi, Amin Hajizadeh and Mehdi Tavan
In this paper, a novel Lyapunov–Krasovskii stable fuzzy proportional-integral-derivative (PID) (FPID) controller is introduced for load frequency control of a time-delayed…
Abstract
Purpose
In this paper, a novel Lyapunov–Krasovskii stable fuzzy proportional-integral-derivative (PID) (FPID) controller is introduced for load frequency control of a time-delayed micro-grid (MG) system that benefits from a fuel cell unit, wind turbine generator and plug-in electric vehicles.
Design/methodology/approach
Using the Lyapunov–Krasovskii theorem, the adaptation laws for the consequent parameters and output scaling factors of the FPID controller are developed in such a way that an upper limit (the maximum permissible value) for time delay is introduced for the stability of the closed-loop MG system. In this way, there is a stable FPID controller, the adaptive parameters of which are bounded. In the obtained adaptation laws and the way of stability analyses, there is no need to approximate the nonlinear model of the controlled system, which makes the implementation process of the proposed adaptive FPID controller much simpler.
Findings
It has been shown that for a different amount of time delay and intermittent resources/loads, the proposed adaptive FPID controller is able to enforce the frequency deviations to zero with better performance and a less amount of energy. In the proposed FPID controller, the increase in the amount of time delay leads to a small increase in the amount of overshoot/undershoot and settling time values, which indicate that the proposed controller is robust to the time delay changes.
Originality/value
Although the designed FPID controllers in the literature are very efficient in being applied to the uncertain and nonlinear systems, they suffer from stability problems. In this paper, the stability of the FPID controller has been examined in applying to the frequency control of a nonlinear input-delayed MG system. Based on the Lyapunov–Krasovskii theorem and using rigorous mathematical analyses, the stability conditions and the adaptation laws for the parameters of the FPID controller have been obtained in the presence of input delay and nonlinearities of the MG system.
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Fenglin Lü, Huabin Chen, Chongjian Fan and Shanben Chen
Quality control of arc welding process is the key component in robotic welding system. The purpose of this paper is to address vision‐sensing technology and model‐free adaptive…
Abstract
Purpose
Quality control of arc welding process is the key component in robotic welding system. The purpose of this paper is to address vision‐sensing technology and model‐free adaptive control (MFC) of weld pool size during automatic arc welding system.
Design/methodology/approach
The shape and size parameters for the weld pool are used to describe the weld pool geometry, which is specified by the backside weld width. The welding current and wire‐feeding speed are selected as the control variable, and the backside width of weld pool is selected as the controlled variable. To achieve the goal of full penetration and fine weld seam formation, a multiple input single output (MISO) MFC is designed for control of the backside pool width.
Findings
The research findings show that it is feasible to develop such a MISO MFC of weld pool size, which is independent on mathematic model of weld pool dynamics. And the control algorithm is simple to use and has a minimal computational burden.
Research limitations/implications
This is a work in progress. The controlled process results are mainly influenced by the period of competing control algorithm and image processing, which could be improved by the hardware and enhancing computation speed. The closed‐loop control is a two inputs‐one output system. Thus, the means by which the multiple input multiple output (MIMO) control method is applied to weld pool dynamics is work for the future.
Practical implications
The control system is applicable to automatic gas tungsten arc welding (GTAW).
Originality/value
The MISO MFC has been set up for automatic GTAW to overcome the nonlinear and uncertainty of GTAW process, in which two welding parameters can be adjusted simultaneously. In addition, this controller is independent on welding pool dynamic model.
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Fan Yang, Zongji Chen and Chen Wei
The purpose of this paper is to build nonlinear model of a small rotorcraft‐based unmanned aerial vehicles (RUAV), using nonlinear system identification method to estimate the…
Abstract
Purpose
The purpose of this paper is to build nonlinear model of a small rotorcraft‐based unmanned aerial vehicles (RUAV), using nonlinear system identification method to estimate the parameters of the model. The nonlinear model will be used in robust control system design and aerodynamic analysis.
Design/methodology/approach
The nonlinear model is built based on mechanism theory, aerodynamics and mechanics, which can reflect most dynamics in large flight envelop. Genetic algorithm (GA) and time domain flight data is adopted to estimate unknown parameters of the model. The flight data were collected from a series of fight tests. The identification results were also analyzed and validated.
Findings
The nonlinear model of RUAV has better accuracy, the parameters are physical quantities, and having distinctly recognizable values. The GA is suitable for nonlinear system identification. And the results proved the identified model can reflect the dynamic characteristics in extensive area of flight envelop.
Research limitations/implications
The GA requires much more computing power, to identify 12 unknown parameters with 30 iterations, will takes more than 18 hours of a four cores desktop computer. Because of this is an off‐line identification process, and has more accuracy, extra time is acceptable.
Originality/value
GA method has significantly increased the accuracy of the model. The previous work of system identification used a ten states linear model, and using PEM identified 23 coefficients. By carefully building the nonlinear model, it has only 21 unknown parameters, but if the model is linearized, it will get a linear model more than 35 states, which shows nonlinear model contain more dynamics than linear model.
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Yanchao Sun, Liangliang Chen and Hongde Qin
This paper aims to investigate the distributed coordinated fuzzy tracking problems for multiple mechanical systems with nonlinear model uncertainties under a directed…
Abstract
Purpose
This paper aims to investigate the distributed coordinated fuzzy tracking problems for multiple mechanical systems with nonlinear model uncertainties under a directed communication topology.
Design/methodology/approach
The dynamic leader case is considered while only a subset of the follower mechanical systems can obtain the leader information. First, this paper approximates the system uncertainties with finite fuzzy rules and proposes a distributed adaptive tracking control scheme. Then, this paper makes a detailed classification of the system uncertainties and uses different fuzzy systems to approximate different kinds of uncertainties. Further, an improved distributed tracking strategy is proposed. Closed-loop systems are investigated using graph theory and Lyapunov theory. Numerical simulations are performed to verify the effectiveness of the proposed methods.
Findings
Based on fuzzy control and adaptive control theories, the desired distributed coordinated tracking control strategies for multiple uncertain mechanical systems are developed.
Originality/value
Compared with most existing literature, the proposed distributed tracking algorithms use fuzzy control and adaptive control techniques to cope with system nonlinear uncertainties of multiple mechanical systems. Moreover, the improved control strategy not only reduces fuzzy rules but also has higher control accuracy.
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