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Due to the nonlinear characteristics of the valves and the interactions between the controlled variables, designing a control system for coupled tanks is a difficult task. This paper deals with the comparative study between Fuzzy-PID and GA-PID controllers for decoupling level and flow rate control of two tank systems for beverage factories with minimum time optimal operation. In most process control industries, each process requires multiple control variables. Here two input two output (TITO) systems are considered highly interacting multivariable control systems. The decoupling control scheme (Pre-compensator (dynamic) decoupling) is used to reduce the correlation between the controlled and manipulated variables by diagonalizing the system. The two independent SISO systems are further controlled by different controllers so that the system can trace the set point and yield a good time response. Two radically different control approaches are presented and compared for this system's dynamics, motivated by a desire to provide precise liquid-level control and regulate the flow rate. MATLAB /Simulink model and tuning algorithm (GA) are used for simulation. As the simulation result ensured, the GA-PID controller is the used for the specified system which is based on the transient and steady-state specifications. Quantitatively; the GA-PID controller has 39.167ms rise time, 8.50sec settling time, and -0.393% overshoot; whereas FLC-PID has 118.101ms rise time, 8.65sec settling time, and -0.033% overshoot. But in GA with PID controllers, the external disturbance tolerance capability of the proposed scheme, meaning robustness against external disturbance has a slight difference and FLC-PID has perfectly achieved the robustness. Depending on the result, FLC-PID has more result than GA-PID controller based on the set of specifications. Hence robustness is more important than time performances. [ABSTRACT FROM AUTHOR]

Natural gas-to-methanol plants are receiving renewed interest with the significant increase in shale gas availability. Methanol serves as a crucial raw material for producing various industrial and consumer goods as well as key platform chemicals, including acetic acid, methyl tertiary butyl ether, dimethyl ether, and methylamine. In this research, a dynamic model is developed for Natgasoline's methanol manufacturing plant. A hierarchical control system comprising Dynamic Matrix Control (DMC) and a basic regulatory control loop is constructed using this dynamic model to minimize methanol losses and utility costs under various process upsets. A subspace identification methodology is used to develop rigorous DMCplus controller models. The simulation results in the ASPEN manufacturing software platform show that the DMCplus controller developed in this study can reduce methanol losses by 96% and utility requirements by 40%. The controller is robust to feed flow variations of ±10%. Furthermore, disturbances due to the variation in hydrogen content in the syngas are also successfully rejected by the controller. This hierarchical multivariable control system performs significantly better than the traditional regulatory PID control strategy in optimizing the methanol process under process constraints. [ABSTRACT FROM AUTHOR]

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique. The approach uses PCA to reduce the system's dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method's capacity to maintain control accuracy while reducing computational load, making it a promising solution for applications in industrial and engineering systems that require robust, efficient control mechanisms. [ABSTRACT FROM AUTHOR]

This study explores a closed-loop multivariable control system for HVAC systems. A hybrid distributed parameter-lumped model was developed using MATLAB's System Identification Application, resulting in a transfer function matrix. The Direct Nyquist Array (DNA) control strategy was applied, dividing the HVAC system into two SISO loops, each equipped with PID controllers. Stability analysis confirmed system stability according to the Nyquist criterion. Simulation of the system under DNA control showed improved performance over the open-loop setup, with faster response times of nearly 1 s and overshoots between 7% and 15%, maintaining thermal comfort and occupant satisfaction. The system effectively managed ambient heat transfer variations, maintaining desired airflow rate and air temperature. The closed-loop setup exhibited robustness against disturbances, quickly returning to steady-state values. Settling times ranged from 1 to 2 s with minimal impact on HVAC performance. Overall, the closed-loop multivariable DNA control with SISO PID controllers proved reliable for enhancing energy efficiency and thermal comfort in HVAC systems. Practical application: This study provides a comprehensive framework for multivariable control in HVAC systems, significantly benefiting built environment professionals by enhancing energy efficiency and occupant comfort. By applying advanced control strategies, the research offers practical solutions for optimizing HVAC performance, reducing operational costs, and ensuring sustainability in building management. The detailed analysis and methodologies presented can be directly implemented to improve system reliability and responsiveness, thereby supporting professionals in creating smarter, more resilient, and environmentally friendly built environments. [ABSTRACT FROM AUTHOR]

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system's excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics. [ABSTRACT FROM AUTHOR]

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE1 = 1.865, ISE2 = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure. [ABSTRACT FROM AUTHOR]

The article discusses the concept of digital twins and their applications in Industry 4.0. Digital twin technology has become more advanced and widespread, driving the development of Industry 4.0. It is used in various sectors such as product design, predictive maintenance, and virtual reality applications. The article highlights different articles in a special issue that explore the potential applications of digital twins in sectors like construction, education, energy, manufacturing, and maritime. The article concludes by discussing the future research directions and challenges in implementing digital twin technology. [Extracted from the article]

Carbon nanotubes (CNTs) are efficient nanostructures for fabricating photo-thermionic emission (PTE) cathodes due to their high optical absorbance and the wide range of properties related to their morphological and structural characteristics. In this study, the photothermal and PTE responses of different CNT cathodes were numerically investigated. The CNTs varied in their outer diameters while maintaining the wall thickness. The analysis considered short-pulse laser sources with nanosecond, picosecond, and femtosecond pulse durations. The thermal properties of the CNTs were computed using the phonon density of states by simulating the vibrational phonon modes. The optical irradiances induced nonlinear optical processes from which the photothermal and PTE responses of the CNTs were obtained. Finally, thermal and time decay variables as a function of irradiation and nanotube diameter were analyzed. This work aims to manipulate the linear and nonlinear optical and mechanical properties of the CNTs to achieve multivariable all-optical control systems. [ABSTRACT FROM AUTHOR]

The quadruple-tank system (QTS) is a popular educational resource in universities for studying multivariable control systems. It enables the analysis of the interaction between variables and the limitations imposed by multivariable non-minimum phase zeros, as well as the evaluation of new multivariable control methodologies. The works utilizing this system present a theoretical model that may be too idealistic and based on erroneous assumptions in real-world implementations, such as the linear behavior of the actuators. In other cases, an identified linear model is directly provided. This study outlines the practical grey-box modeling procedure conducted for the QTS at the University of Cordoba and provides guidance for its implementation. A configurable nonlinear model was developed and controlled in a closed loop using different controllers. Specifically, decentralized control, static decoupling control, and simplified decoupling control were compared. The simulation designs were experimentally validated with high accuracy, demonstrating that the conclusions reached with the developed model can be extrapolated to the real system. The comparison of these three control designs illustrates the advantages and disadvantages of decoupling in certain situations, especially in the presence of non-minimum phase zeros. [ABSTRACT FROM AUTHOR]

The design of multivariable process control systems is specially complicated when there are strong interactions between the different control loops, and even more with multiple time delays. This paper proposes an iterative design method of centralized proportional-integral-derivative (PID) controllers for stable linear systems. The methodology is based on the linear parameterization of equivalent loop transfer functions (ELTFs) for centralized control. These functions capture the dynamics of the other loops and, from a prior design, allow solving the design problem at each iteration with linear programming that shapes the Nyquist plot of the ELTFs in the frequency domain, which also avoids the need for approximations. Two optimizations are proposed: (I) maximizing integral gains by fulfilling linear robustness margins in each ELTF and (II) maximizing linear robustness margins by fulfilling minimum bandwidths in each loop. In both optimizations, static decoupling and decoupling at a frequency close to the bandwidth of each loop are included as constraints, which improves the decoupling performance and the procedure convergence. The effectiveness of the method is verified in three simulation examples (square and non-square) and a lab experimental process. The proposed designs achieve a similar or better response when compared to that achieved by other authors. [ABSTRACT FROM AUTHOR]

High‐speed trains (HSTs) often face challenges associated with car‐body hunting, resulting from factors such as complex operating environments and wheel‐rail contact degradation. These issues have a significant impact on the ride quality and operational safety. Passive suspension systems are inadequate to provide satisfactory dynamic performance for HSTs in such circumstances. However, active suspension systems can provide an effective solution. To address the car‐body hunting stability of HSTs, this study proposes a novel control approach called displacement inequality constraint following control (ICFC‐BIS) for the active suspension of HSTs. The ICFC‐BIS leverages the beneficial nonlinearity of bio‐inspired structures (BIS) to indirectly achieve excellent dynamic characteristics of the BIS through actuators installed in the suspension of HSTs, eliminating the need for actual BIS installation. Numerical simulation results demonstrate that the proposed ICFC‐BIS can effectively suppress car‐body hunting motion, thereby enhancing the ride comfort of HSTs. Consequently, the operational safety and ride comfort indices of HSTs equipped with active suspension systems are significantly improved. [ABSTRACT FROM AUTHOR]

Fractional calculus is defined by expanded integer order integration and differentiation. In this paper, multiple mathematical models of a nonlinear dual-tank device are precisely formulated by fractional calculus. Using the accurate model, a multivariable fractional-order controller is designed for nonlinear devices. The merits of the fractional-order design include: (1) control of multivariable nonlinearities, (2) compensation of uncertainties, and (3) elimination of coupling effects. Simulations and experiments are conducted to verify the precision of the fractional order models and the effectiveness of the multivariable fractional-order control system design. [ABSTRACT FROM AUTHOR]

We assess the performance of two control strategies on the IEA 15MW reference floating wind turbine through OpenFAST simulations. The multivariable feedback (MVFB) control tuned by the toolbox of the Reference OpenSource Controller (ROSCO) is considered to be a benchmark for comparison. We then tune the feedback gains for the multivariable control, considering two cases: with and without lidar-assisted feedforward control. The tuning process is performed using OpenFAST simulations, considering realistic offshore turbulence spectral parameters. We reveal that optimally tuned controls are robust to changes in turbulence parameters caused by atmospheric stability variations. The two optimally tuned control strategies are then assessed using the design load case 1.2 specified by the IEC 61400 standard. Compared with the baseline multivariable feedback control, the one with optimal tuning significantly reduced the tower damage equivalent load, leading to a lifetime extension of 19.7 years with the assumption that the lifetime fatigue is only caused by the design load case 1.2. With the assistance of feedforward control realized using a typical four-beam lidar, compared with the optimally tuned MVFB control, the lifetime of the tower can be further extended by 4.6 years. [ABSTRACT FROM AUTHOR]

This paper deals with modelling and robust control of linear multivariable uncertain systems based on the new linear multivariable ARX (Auto-Regressive with eXogenous input)-Laguerre model and the loop-shaping design procedure with the relative gain array theory. In fact, to guarantee significant parameter number reduction of the multivariable ARX-Laguerre model compared with the classic multivariable ARX model, we propose the optimization of the Laguerre poles of the model using the genetic algorithm where the Fourier coefficients are identified using the recursive least square method. Later, the optimized multivariable ARX-Laguerre model is exploited to propose a robust control algorithm for linear multivariable uncertain systems. Indeed, we propose to combine the loop-shaping design procedure approach and the relative gain array theory to develop a simplified final robust controller guaranteeing reference tracking and robust stability against parametric uncertainties. This proposition aims for the simplification of the control's scheme by adjusting the weighting matrix and the final robust controller calculated from the loop-shaping design procedure for a simpler and more experimentally applicable control scheme. The identification of Fourier coefficients and Laguerre poles as well as the robust multivariable control algorithm are experimentally validated on a laboratory coupled two-tank system showing good results in terms of modelling and attending desired control performances. [ABSTRACT FROM AUTHOR]

This work proposes a low-cost Arduino-based methodology for simulation and control of dynamic systems. The proposed methodology allows the real-time Hardware-In-the-Loop simulation of multivariable systems and the evaluation of multivariable control techniques. In order to obtain a HIL simulation, two Arduinos are interconnected. One Arduino is used to simulate the plant, and the other is used to implement the controller. In order to evaluate the performance of the proposed methodology, three multivariable dynamic systems are simulated in real-time: an RLC system, a twin rotor MIMO system, and a microgrid. A state feedback controller by using a state observer is used to implement the controller over each dynamic system. An additional I+PD control structure is also used to compare the state feedback control method. An additional software is designed to display the data in a graphical interface in real-time and to analyze and evaluate the performance of each proposed plant and controller. [ABSTRACT FROM AUTHOR]

The selection of uncertainty structures is an important aspect of system identification for robust control. The aim of this paper is to provide insight into uncertain multivariable systems for robust control. A unified method for visualizing model sets is developed by generating Bode plots of multivariable uncertain systems, both in magnitude and phase. In addition, these model sets are compared from the viewpoint of the control objective, allowing a quantitative analysis as well. An experimental case study on an automotive transmission application demonstrates these connections and confirms the importance of the developed framework for control applications. In addition, the experimental results provide new insights into the shape of associated model sets by using the presented visualization procedure. Both the theoretical and experimental results confirm that a recently developed robust‐control‐relevant uncertainty structure outperforms general dual‐Youla‐Kučera uncertainty, which in turn outperforms traditional uncertainty structures, including additive uncertainty. [ABSTRACT FROM AUTHOR]

Due to the multi-loop coupling characteristics of multivariable systems, it is difficult for traditional control methods to achieve precise control effects. Therefore, this paper proposes a control method based on deep reinforcement learning to achieve stable and accurate control of multivariable coupling systems. Based on the proximal policy optimization algorithm (PPO), this method selects tanh as the activation function and normalizes the advantage function. At the same time, based on the characteristics of the multivariable coupling system, the reward function and controller are redesigned structures, achieving stable and precise control of the controlled system. In addition, this study used the amplitude of the control quantity output by the controller as an indicator to evaluate the controller's performance. Finally, simulation verification was conducted in MATLAB/Simulink. The experimental results show that compared with decentralized control, decoupled control and traditional PPO control, the method proposed in this article achieves better control effects. [ABSTRACT FROM AUTHOR]

This paper proposes a new parameter tuning approach for active disturbance rejection control (ADRC) of multi-input multi-output (MIMO) uncertain systems. Firstly, decentralized ADRC (DADRC), dynamic decoupling ADRC (DD-ADRC), and inverted decoupling ADRC (ID-ADRC) are introduced. Three control schemes are uniformly transformed into a two-degree-of-freedom (2DOF) equivalent structure for analysis. Then, a parameter tuning approach based on multivariable quantitative feedback theory (QFT) is proposed to achieve the desired performance. Considering the coupling effects, the QFT performance specifications are reformulated to reduce the conservatism in design, and the closed-loop stability conditions are studied to establish the robust stability performance specification. Finally, the control schemes and the proposed tuning approach are applied to a heat integrated distillation column (HIDiC) process. The effectiveness of the parameter tuning approach is verified, and the respective advantages of the ADRC control schemes are analyzed in parameter tuning and simulations. • A parameter tuning approach based on quantitative feedback theory is proposed. • Performance specifications are reformulated to reduce conservatism in design. • Closed-loop stability conditions are studied to establish robust stability performance specifications. • The stability and disturbance rejection performance are analyzed and compared. [ABSTRACT FROM AUTHOR]

In this paper, a controller for a doubly fed induction generator (DFIG) connected to a wind system is proposed. This control assigning its own structures as an optimal control method, the electric model in the DFIG state space is also shown, for which it is expected to estimate a linear model through the subspace technique and thus to design the controller. It will be possible to show that a structure assignment controller is undoubtedly a good option for the control of multivariable systems. The results of the output signals will be analyzed when applying the controller, assigning their own structures, which will allow us to observe that the response and disturbance times are below two tenths of a second. [ABSTRACT FROM AUTHOR]

There have been high demands of high-quality, highly efficient processing methodologies on "difficult-to-cut" titanium alloys. The current methods for dealing with this kind of materials are mainly mechanical cutting ones. However, because of high processing costs, poor surface qualities, and restrictive machining operations, the costs of mechanical cutting methods are high. Electrical discharge machining (EDM), because of its flexibility, was considered as a supplement. However, serious difficulties arose while machining titanium alloys by EDM. Because of low thermo-conductivity of titanium alloys, the liquid temperature in gap between electrode and workpiece rose quickly after a series of pulse discharges. The high temperature of gap liquid usually led to gap liquid breakdown strength to decline. The consequence was discharging pulses tended out to be stable arc pulses or short pulses, burning workpiece surface and wearing electrode. The machining process became unstable. The low thermal conductivity of titanium alloys was the inherent property which could be hardly changed, and at present, the only way to settle the hard-to-cut problem of machining titanium alloys by EDM was to seek a way to keep gap liquid breakdown strength not go down so fast but still be suitable for effective pulse discharges. To solve this problem, this paper first listed three conditions to be met and analyzed the reasons to affect gap liquid breakdown strength in detail and concluded with three factors, gap distance, amount of chips left in gap, and gap liquid deionization after pulse discharges and then came up with a proposition to the problem. Technically, the proposition was accomplished by constructing a multiple-variable adaptive control system in which gap servo-voltage proportional to gap distance was in charge of discharging extent of pulses, electrode-discharging time decided the amount of chips produced in an electrode discharging cycle, and pulse-off time decided gap liquid deionization after discharges. These variables were timely regulated to keep the liquid breakdown strength suitable for discharging and meanwhile avoiding arcing in machining. The verification test demonstrated that the multivariable control system really helped electrical discharge machining titanium alloys in severe machining situations and proved its usefulness in applications. [ABSTRACT FROM AUTHOR]

The study deals with designing a multivariable centralized proportional‐integral (PI) controller for an activated sludge process (ASP) by adopting a new tuning technique. To overcome the tedious task of manual tuning by a classical method that was based on Ziegler–Nichols tuning, a novel approach is developed by formulating the optimization problem and considering it along with the theoretical results to calculate the controller parameters. The proposed method is then compared with the reported method to demonstrate the advantages of the proposed approach. A nonlinear ASP model is selected to evaluate the performance of the designed controller. The simulation carried through the proposed optimization technique exhibits a significant improvement in closed‐loop performance for tuning the centralized controller. It is observed that the proposed method minimizes the effort of tuning the controller significantly. The proposed controller achieves better load disturbance response and lower overshoot than the reported method. The proposed method could improve integral absolute error (IAE) and integral time absolute error (ITAE) performance indices, ranging between 18.69% to 53.80% and 16.42% to 52.97% for a step change in yr1 and yr2, respectively, which are better compared to the reported method. The study also assesses the control system's robustness for their uncertainties with the time constant and time delay. [ABSTRACT FROM AUTHOR]

This study proposes a centralized control system for an islanded multivariable minigrid to improve its performance, stability and resilience. The integration of renewable energy sources and distributed energy storage systems into microgrid networks is a growing trend, particularly in remote or islanded areas where centralized grid systems are not available. The proposed control system is designed to be implemented at two levels a high-level control system and a low-level control system. Hence, the high-level control system balances energy resources and demand, makes decisions for effective resource utilization and monitors energy transactions within the minigrid. Real-time data from various sources and advanced algorithms are used to optimize energy management and distribution enabling the integration of renewable energy sources and enhancing the resilience of the minigrid against power outages. Moreover, the low-level control system monitors energy parameters such as voltage, current, frequency and mechanical energy. The control system ensures these parameters remain within the specified range, maintaining system stability and ensuring efficient energy distribution. It also protects the minigrid against power outages improving system reliability and security. Finally, the proposed centralized control system offers a promising solution for improving the performance, stability and resilience of microgrid networks. The system provides real-time monitoring, efficient energy management and distribution, and the integration of renewable energy sources. These results have important implications for the development and deployment of microgrid networks in remote or islanded areas. [ABSTRACT FROM AUTHOR]

A new method of designing controllers with an integral term as part of multivariable optimal control systems in discrete time is proposed. Traditional methods of designing multivariable controllers have been investigated, and a prototype of the controller has been chosen as a basis for comparison. Specific formulas for design of the proposed controller are given. Comparing the transition processes of the control systems outputs with a unit-step disturbance at the plant input using the proposed controller and optimal controller obtained by the typical method, it can be concluded that the proposed controller compensates the disturbance and gives a faster transition process. The main feature of the proposed method is that, for multivariable systems with a large number of inputs and outputs, it significantly simplifies the design of the controller. This is because it does not require the extension of the plant state matrix by the number of inputs at the transfer of the matrices to the optimal controller design programs. [ABSTRACT FROM AUTHOR]

Abstract DC microgrid is a high‐efficiency plan for exploiting and controlling distributed generation (DG) resources that can supply local loads in grid‐connected or islanded mode. Conventionally, the droop control method is utilized to attain proper load sharing among the DGs as a decentralized approach in DC microgrids. However, accurate current sharing among DG units with different line resistances is not achievable by the conventional droop control, whereas this method also increases the DC bus voltage deviation. In this paper, a new droop‐based distributed control method is proposed in the secondary control level to overcome the limitations of droop control. To implement the proposed method, only the amount of precalculated virtual voltage drop of DG units is shared with adjacent DGs through a low‐bandwidth communication, and other calculations are local. So, there is no need to define and share new parameters, and get feedback from the DC bus voltage. By employing the proposed method, not only accurate current sharing among the DG units is guaranteed but also DC bus voltage is restored to the nominal value. To ensure the stability of the proposed method, the robust stability and performance of the closed‐loop system are analyzed by structured singular value (µ) under resistive load and communication delay changes. Finally, a DC microgrid is evaluated using MATLAB/Simulink as well as experimental tests to illustrate the validity and efficiency of the proposed method.

Featured Application: The method proposed can be used for the identification and control of multivariable nonlinear systems. A novel dynamic inverse control method based on a dynamical neural network (DNN) is proposed for the trajectory tracking control of a flexible air-breathing hypersonic vehicle (FAHV). Firstly, considering that the accurate model of FAHV is difficult to obtain, the FAHV is regarded as a completely unknown system, and a DNN is designed to identify its nonlinear model. On the basis of Lyapunov's second law, the weight vectors of the DNN are adaptively updated. Then, a dynamic inverse controller is designed based on the identification model, which avoids the transformation of the nonlinear model of FAHV, thereby simplifying the controller design process. The simulation results verify that the DNN can identify FAHV accurately, and velocity and altitude can track the given reference signal accurately with the proposed dynamic inverse control method. Compared with the back-stepping control method, the proposed method has better tracking accuracy, and the amplitude of the initial control law is smaller. [ABSTRACT FROM AUTHOR]

In this paper, robust stabilization and predictive tracking control for a class of nonlinear uncertain multivariable systems is presented. The control scheme is built by incorporating nonlinear model predictive control in discrete sliding mode control to obtain optimal results while satisfying hard constraints and closed-loop robustness in the presence of external disturbance (matched or unmatched) and parametric uncertainty. Additionally, the control input domain limitation is involved in construction of the model predictive control scheme by employing sliding functions admissible bounds. The problem of cost minimization is solved through optimal selection of terminal cost gain and allocation of system dynamics with respect to sliding surfaces while robust stabilization and feasibility are ensured through the prediction horizon. [ABSTRACT FROM AUTHOR]

Electrochemical reduction of CO 2 gas is a novel CO 2 utilization technique that has the potential to mitigate the global climate crisis caused by anthropogenic CO 2 emissions, and enable the large-scale storage of energy generated from renewable sources in the form of carbon-based chemicals and fuels. However, due to the complexity of the electrochemical reactions, the explicit first-principles models for CO 2 reduction are not available yet, and there has been a limited effort to develop process modeling, optimization and control of CO 2 electrochemical reactors. To this end, a rotating cylinder electrode (RCE) reactor has been constructed at UCLA to understand the mass transfer and reaction kinetics effects separately on the productivity. In the RCE reactor, the applied potential strongly influences the reaction energetics and the electrode rotation speed affects the hydrodynamic boundary layer and modifies the film mass transfer coefficient, which involves convective and diffusive transport. The present work aims to develop a multi-input multi-output (MIMO) control scheme for the RCE reactor that integrates techniques from artificial and recurrent neural network modeling, nonlinear optimization, and process controller design. Specifically, production rates of two products from the experimental reactor, ethylene and carbon monoxide, are controlled by manipulating two inputs, applied potential and catalyst rotation speed. Process dynamics and controllability are analyzed, a feedback control strategy is designed and the controllers are tuned accordingly. The experimental electrochemical cell is employed to gather data for process modeling and implement the multivariable control system. Finally, the experimental results are presented which demonstrate excellent closed-loop performance by the control system and regulation of the outputs at three different set-points including an economically-optimal set-point. • Machine learning modeling of product concentration dynamics. • Real-time economic optimization for set-point calculation. • Input-output pairing determination and controller design. • Experimental implementation of the control system. [ABSTRACT FROM AUTHOR]

This article presents the control of Soil moisture, Nitrogen, and Salt content in soil using a robust control technique to maintain a desired quality of soil in a drip irrigated field. The control of this multi-variable system is accomplished by adopting two possible control structures namely (i) a decentralized controller backed by a feed-forward controller (ii) a diagonal centralized de-coupler along with a diagonal controller. The controllers are designed using quantitative feedback theory. The efficacy of the proposed techniques is validated through numerical simulations. A comparative study among the two methods reveals that the former performs better than the later. Another comparative study shows that the proposed controller outperforms some of the reported works. • Control of Soil moisture, Nitrogen and Salt content in a drip irrigated field. • A 3 × 3 uncertain model is derived from physical laws and practical data. • De-centralized QFT along with feed-forward controllers is proposed. • Centralized decoupler with diagonal QFT based controllers is proposed. [ABSTRACT FROM AUTHOR]

Technology of Ancient Greece As Nicos pointed out "automaton" (Graph HT ht ) is a Homeric word that first appeared in the Iliad (Vasileiadou et al., [14]). Assigning the roots of Graph HT ht is much more difficult. Likewise the Greek word Graph HT ht "system" comes from the ancient verb Graph HT ht which occurs frequently in ancient Greek written sources in many contexts with a basic meaning that the whole is made from parts and that the whole may have properties not always attributed to the individual parts. [Extracted from the article]

Anode hydrogen circulation system with purge valve has become one of the most effective ways for proton exchange membrane fuel cell (PEMFC) to alleviate performance degradation caused by nitrogen permeation and improve the hydrogen utilization rate. However, nonlinearity and multivariable coupling of the system make it difficult for PEMFC to achieve pressure and hydrogen supply synchronous tracking under complex operating conditions. To solve the problem, a novel nonlinear model predictive control scheme based on coupling analysis is proposed in this paper. Firstly, coupling characteristics of the hydrogen circulation system and the control pairing of the multivariable system are analyzed based on the relative gain array method. Then a novel control scheme based on an adaptive model predictive controller and a nonlinear model predictive controller is designed to keep the anode pressure stable and sufficient hydrogen supply, which use nonlinear observers to estimate the internal states online. Finally, the proposed controllers are implemented in control experiments of a 50 kW PEMFC anode hydrogen circulation system. The results demonstrate that the proposed control approach has great dynamic performance, anti-disturbance ability and can maintain a high hydrogen utilization rate under purge operation, current disturbance and uncertain intake pressure. • A PEMFC anode model with gas permeation and water transmission is proposed. • Multivariable coupling and control pairing of anode system are analyzed. • LPV-MPC and NMPC with state observers for anode pressure and flow regulation. • Dynamics of the recirculation system are improved under pressure fluctuations. • Trade-off between dynamic performance and efficiency of anode system is discussed. [ABSTRACT FROM AUTHOR]

In this work, a new multi‐strategy fuzzy control (MSFC) based on fuzzy fusion techniques for the control of multivariable nonlinear systems is proposed. This new nonlinear control method is a mixture of different control techniques by fuzzy interpolation using the fuzzy Takagi‐Sugeno model. It combines various control strategies to achieve smooth transient responses and zero steady state error. The following techniques are merged within the proposed MSFC structure: an optimal state feedback control that yields an optimal transient response, optimal control through an incremental state model that returns a zero steady state error, and a constant input control to maintain the system behavior within predefined boundaries whenever the MSFC method is applied. [ABSTRACT FROM AUTHOR]

Abstract With new telecommunications engineering applications, the cognitive radio (CR) network‐based internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR‐IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross‐layer routing protocol based on CR‐IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine‐learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR‐IoT.

Abstract A fault current compensation technique is proposed in this paper for resonant grounded power distribution systems in bushfire prone areas. Arc suppression devices with residual current compensation inverters are used to compensate fault currents due to single line‐to‐ground faults in order to mitigate powerline bushfires. The main contribution of this paper is the design of a compensation technique for the T‐type residual current compensation inverter using a non‐singular terminal sliding mode control scheme. The main objective of the proposed scheme is to reduce the fault current and bring its value to a level so that it cannot ignite fires. The proposed controller is designed based on the selection of a sliding surface in a way the singularity problem can be avoided and chattering effects in existing sliding mode controllers can be eliminated. The desired current injection through the residual current compensation inverter is ensured by enforcing the control law into the terminal sliding surface where the control law is determined by satisfying the Lyapunov stability criteria. The performance of the non‐singular terminal sliding mode controller is compared with an integral sliding mode controller by considering different values of fault currents where these values are varied by changing fault resistances. Results for simulation in the software and processor‐in‐loop simulations are verified against operational standards which are essential for mitigating powerline bushfires. This work focuses to design a non‐singular terminal sliding mode controller for the residual current compensation inverter which is used in an arc suppression device to compensate both active and reactive components of the fault current and keeps its value below 0.5 A within 2 s after activating the residual current compensation inverter which is a requirement as per the operational standard. This controller is designed based on the selection of a terminal sliding surface while satisfying the condition for avoiding the singularity problem.

Abstract In this paper, we discuss the cooperative output tracking problem for networked multi‐agent systems (NMASs) with plant‐model mismatch as well as random communication constraints in the forward and feedback channels of each agent. In order to compensate actively for random communication constraints, that is, network‐induced delays and packet dropouts, an incremental networked predictive control scheme based on a state observer is proposed, which consists of a data buffer, an incremental networked predictor, and a network delay compensator. For both the plant‐model mismatch case and the plant‐model match case, the stability conditions of resulting closed‐loop NMASs are derived, respectively. Using the proposed method, the simulation results for three DC motor systems are presented to indicate that desired tracking performance can be achieved.

Abstract This work investigates the gravity compensation topic, from a control perspective. The gravity could be levelled by a compensating mechanical system or in the control law, such as proportional derivative (PD) plus gravity, sliding mode control, or computed torque method. The gravity compensation term is missing in linear and nonlinear optimal control, in both continuous‐ and discrete‐time domains. The equilibrium point of the control system is usually zero and this makes it impossible to perform regulation when the desired condition is not set at origin or in other cases, where the gravity vector is not zero at the equilibrium point. The system needs a steady‐state input signal to compensate for the gravity in those conditions. The stability proof of the gravity compensated control law based on nonlinear optimal control and the corresponding deviation from optimality, with proof, are introduced in this work. The same concept exists in discrete‐time control since it uses analog to digital conversion of the system and that includes the gravity vector of the system. The simulation results highlight two important cases, a robotic manipulator and a tilted‐rotor hexacopter, as an application to the claimed theoretical statements.

Due to the ability of handling the coupling property of multivariable control system, generalized predictive control (GPC) has been widely employed in process industry. However, the existing GPC method normally utilizes the frequency domain indices in the design procedure, which is not intuitive to the industrial end users. Therefore, a new GPC method with improved time domain performance is proposed for multiple-input-multiple-output system. First, the slope and rate of change of the slope of the system outputs are considered as two new weighting terms in the cost function of GPC such that the dynamic characteristics of the system can be better considered in the optimal control problem of the controller. Second, given the modified cost function, the resultant GPC algorithm is developed to improve the time domain performance of the system. Third, the convergence and stability of the closed-loop system under the proposed method are theoretically proved. Finally, the experimental results based on the variable air volume (VAV) air conditioning system show that the time domain performance indices, including overshoot and settling time, of the proposed method, have been improved by 68.75% and 66.67%, compared with the traditional GPC, showing the advantages of the new GPC method. [ABSTRACT FROM AUTHOR]

Disturbance-observer-based control is a well-known technique for the rejection of unmeasurable disturbances. The application of disturbance-observer-based control is well established for single-input and single-output systems but presents two challenges when applied to multivariable systems with time delays. The first challenge is the calculation of the inverse of the nominal model. The second challenge is the optimal synthesis of the disturbance observer filter to obtain a trade-off between the disturbance rejection and measurement noise attenuation. In this paper, we propose strategies to address these two challenges. To facilitate the design and implementation of the disturbance observer, we propose to approximate the inverse of the nominal model by employing the concept of equivalent transfer function. The equivalent transfer function approximates the inverse of a multivariable model by a matrix whose elements are the inverse of first-order plus time delay transfer functions, that are simpler to deal with. To tackle the second challenge, we propose a synthesis formulation based on a multi-objective optimization problem. Through a multi-objective evolutionary optimization algorithm, it is possible to obtain a set of efficient solutions with different trade-offs between the two objectives. We apply the proposed strategies to two well-known problems in the field of process control to demonstrate its efficiency. We verify that the use of the equivalent transfer function presents the best results in comparison to other methodologies considering the three control objectives: tracking of reference signals, rejection of disturbances, and attenuation of measurement noises. [ABSTRACT FROM AUTHOR]

The paper aims to develop a new method for the design of hybrid multivariable automatic control systems. We determine a hybrid control system as a system that includes two different types of parallel operating controllers. The paper's aim is achieved by solving the following tasks. The first task is to develop a method for the design of hybrid control systems. Investigated systems include PI controllers and a linear-quadratic controller. The second task is to investigate the resulting control system. The investigation is based on direct quality indicators, robustness, and fragility analyses. The most significant result of the paper is the development of a method for the tuning of a linear-quadratic controller, which should work with the decentralized control system in parallel. BLT (Biggest Log Modulus) method is used for decentralized control system tuning. BLT is a well-known method of PI controller detuning. The idea of BLT is to ensure the robustness of multivariable control systems tuned by Ziegler Nichols's rules. The proposed method for the design of the control system is not complicated. The possibility of correcting the tunings of decentralized controllers remains. Design of the new hybrid automatic control system for the distillation plant made using the developed method. The analysis of the dynamic properties of the designed control system showed that the use of a parallel linear-quadratic controller provides a small percentage of overshoot in the control by the reference and high accuracy of stabilizing the values of controlled variables at the largest disturbances. At the same time, the robustness does not decrease and the control system remains fragile, i.e., it is possible to change the settings of the controllers without significant loss of robustness. [ABSTRACT FROM AUTHOR]

Abstract Decentralized optimal control of interval state space systems involves significant complications due to the required sparsity, robustness, and convergence rate. By adding an l1 term to the standard linear quadratic optimization, the obtained composite index, accommodating the sparsity, contains both differentiable and non‐differentiable terms. Knowing that the choice of optimization techniques are problem‐dependent, in this paper, several smart provisions such as “Active set”, “Coordinate descent”, and separately managing the differentiable part of the cost function are put together to form a relatively fast algorithm. In addition, a rule is introduced to adjust automatically the degree of sparsity and a constraint to confine the interval closed‐loop system eigenvalues to the desired s plane region. As a result, a robust sparse decentralized control (RSDC) approach for interval networks is established. The RSDC method is utilized for the control of a multi‐terminal VSC (Voltage Source Converter ) ‐based DC (Direct Current) grid with parametric uncertainty under various external random perturbations. The test results show that the proposed method with just 20 feedback links (out of available 100) performs superiorly to the alternative ones in terms of sparsity, convergence rate, and time‐domain performances, verified through extensive simulations.

Abstract This article examines a decentralized event‐triggered robust model predictive control (MPC) for a class of networked large‐scale non‐linear Lipchitz systems. It is assumed that the subsystems are geographically distributed and the connections can be made over a communication network and therefore local event generator modules are used. An event‐triggering condition is then proposed for each module, which only uses local information to trigger data via the communication channel. In this way, the information exchange between subsystems can be reduced significantly compared to time‐triggered conventional control approaches, while the asymptotic stability of the closed‐loop is maintained. In contrast to the reported event‐triggered MPC results, the optimized controller is calculated based on state feedback control law for individual subsystems, which minimizes the upper limit on the infinite horizon cost function subject to constraints on the control inputs. The validness of the proposed scheme is demonstrated by simulation results.

Abstract The guaranteed‐performance consensus tracking (GPCT) for non‐linear multi‐agent systems (MASs) with switching communication topologies is presented. Different from the existing work, the non‐linear range of one‐sided Lipschitz non‐linear MASs is wider than the Lipschitz ones, and the upper bound of the tracking performance is given while achieving the consensus tracking. First, a guaranteed‐performance tracking control protocol is proposed, where consensus tracking regulation performance is involved. Then, by the linear matrix inequality (LMI), sufficient conditions are provided to achieve the GPCT under the one‐sided Lipschitz and quadratic inner bounded conditions. It is worth noting that a special LMI structure is directly constructed by introducing these two conditions to simplify the complexity of the derivation. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed results.

Abstract This paper proposed an adaptive super‐twisting (AST)‐based fractional‐order sliding mode control algorithm under disturbances for redundantly actuated cable driving parallel robots. A novel fractional‐order non‐singular fast terminal sliding mode surface is constructed, and the fast response convergence and accuracy tracking performance can be obtained in the sliding mode phase; meanwhile, an auxiliary system is designed to overcome the adverse effects of the input saturation. Then, to compensate for the model uncertainty and external disturbances, the uncertainty and disturbance estimation is designed, and a novel AST algorithm is developed to suppress the estimation error without the known bound of the adaptive gains and guarantee the finite‐time convergence in the reaching phase. The Lyapunov stability theory is employed to prove the convergence of the proposed controller. Finally, numerical simulations are conducted in the commercial software of MATLAB/SIMULINK to demonstrate the effectiveness of the proposed controller.

Abstract Bilateral teleoperation systems are widely used to compensate for image spin and in many other applications. Good transparency (position tracking and force feedback) is an important performance index in the control design of bilateral teleoperation systems. This study aims to achieve good transparency for teleoperation systems using force sensorless by proposing an alternative to direct force measurement. Our proposed method is a finite‐time adaptive sliding‐mode‐assisted disturbance observer, which combines disturbance observer with adaptive sliding mode control. The finite‐time stability of adaptive sliding‐mode‐assisted disturbance observer is analysed using the Lyapunov theory. Moreover, a bilateral control law based on adaptive sliding‐mode‐assisted disturbance observer is designed. The proposed method not only improves the system transparency with both force feedback and position tracking, but is also chatter‐free. Experiments are conducted using bilateral brushless DC motors in an experimental setup to demonstrate the effectiveness of the proposed method.

Abstract Vehicle platooning can significantly reduce traffic accidents and enhance transportation safety. Among many subsystems and functionalities equipped in a vehicle platoon, cooperative braking is a safety‐critical one. Besides, as actuator faults would deteriorate vehicle safety and stability, they pose a great challenge to the platoon, particularly during cooperative braking. To address the aforementioned problems, this study designed an adaptive distributed finite‐time fault‐tolerant controller for vehicle platoons under cooperative braking condition. First, based on the graph theory and vehicle longitudinal dynamics, the cooperative braking model of a platoon is constructed. Then, the models of hydraulic braking system and regenerative braking system are developed with malfunction analysis. Based on the vehicle's fault model and the representation of the platoon consensus errors, an adaptive finite‐time sliding‐mode fault tolerant controller is proposed to maintain consensus between vehicle's states during fault occurring. Further, a distributed adaptive law is presented considering the parameter estimation error and sliding‐mode surface. The finite‐time convergence property and the stability of the proposed controller are demonstrated. Numerical simulations are conducted under different conditions. The simulation results reflected by the control performance and actuators' responses validate the feasibility and effectiveness of the designed control method.

Abstract The main purpose of this paper is to extract the maximum power from the variable speed wind turbine (VSWT), which is modelled using fractional calculus. The use of the fractional‐order operator can harden the practical implementation and numerical simulation of the fractional‐order systems. To apply the fractional calculus theory, we assume that the orders of all fractional derivatives of the system have a small deviation from the nearest integers. By defining this small deviation as a perturbation parameter, an integer‐order approximation method is presented for approximating the fractional‐order system into a perturbed integer‐order system in the time domain. Then, a new adaptive second‐order sliding mode control (SOSMC) method is proposed using the terminal proportional integral derivative (TPID) sliding surface to achieve maximum power extraction and reduce mechanical loads and chattering. A TPID sliding surface is employed to ensure better tracking, have a zero steady‐state error, decrease the mechanical stresses, and eliminate the chattering phenomenon. The stability of the adaptive SOSMC based on the obtained integer‐order approximation is analyzed using the Lyapunov stability criterion. The simulation results of the proposed method are presented and compared with some designed controllers for wind turbines (WTs) modelled with fractional and integer derivatives. The obtained results show the effectiveness of the proposed strategy.

Abstract A second‐order adaptive integral terminal sliding mode controller is proposed for the trajectory tracking control of robotic manipulators with uncertainties. A second‐order integral terminal sliding mode surface is designed for which an integral sliding mode (ISM) surface and a fast nonsingular integral terminal sliding mode surface are combined. By using the ISM surface, the reaching phase is removed, which enhances system robustness. The steady‐state error is reduced because of the presence of an error integral term. A fast second‐order nonsingular integral terminal sliding mode surface is employed to ensure that the ISM surface is able to converge to zero rapidly within a finite period of time without leading to a singularity problem. The control input of the proposed controller is continuous. Thus, the chattering phenomenon is removed. An adaptation technique is employed to estimate the upper bound of unknown lumped disturbance. The second‐order derivative of position is calculated using a robust differentiator, making it practical. Simulations and experiments show that the proposed scheme improves the tracking performance and eliminates chattering.