Complex Multivariable Systems Research Articles

Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research

The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.

A Data-Driven Predictive Control Method for Modeling Doubly-Fed Variable-Speed Pumped Storage Units

In this study, a data-driven model predictive control (MPC) method is proposed for the optimal control of a doubly-fed variable-speed pumped storage unit. This method combines modern control theory with the dynamic characteristics of the pumped storage unit to establish an accurate dynamic model based on actual operating data. In each control cycle, the MPC uses the system model to predict future system behavior and determines the optimal control input sequence by solving the constrained optimization problem, thereby effectively dealing with the nonlinearity, time-varying characteristics, and multivariable coupling problems of the system. When compared with a traditional PID control, this method significantly improves control accuracy, response speed, and system stability. The simulation results show that the proposed MPC method exhibits better steady-state error, overshoot, adjustment time, and control energy under various operating conditions, demonstrating its advantages in complex multivariable systems. This study provides an innovative solution for the efficient control of doubly-fed variable-speed pumped storage units and lays a solid foundation for the efficient utilization of new energy sources.

Control strategy for power factor correction circuits based on model predictive control

Power factor correction circuits are being used more extensively, and with that, the requirements and standards have become increasingly stringent. To improve the power factor of the circuit, thereby improving its operational efficiency and mitigating the load effects in boost power factor correction circuits, reducing Total Harmonics Distortion is crucial for improving the quality of the current. An improvement upon the conventional dual-loop PID control algorithm has been proposed. It involves integrating Model Predictive Control into the current loop design and incorporates a Luenberger observer. Model Predictive Control methods can be applied for the control of multivariable systems, especially in complex multivariable systems, as they exhibit strong robustness. Finally, by performing both simulation and experimental validations and analyzing the data results, it is shown that under full load conditions, the power factor λ is 0.968, the efficiency η is 91.11%, the voltage total harmonic distortion Uthd is 1.88%, and the current total harmonic distortion Ithd is 7.04%. These data results strongly confirm the performance of the control mechanism proposed in this paper.

Precise torques and sliding mode compensation for trajectory tracking of manipulator with uncertainty

The control based on dynamic model could improve the dynamic performance of manipulators and obtain better control effects than the control based on kinematic model. As manipulators are complex online multivariable systems, there are various uncertainties in different environments and working conditions. Accurate dynamic parameters are difficult to obtain in practical engineering applications. In this article, a controller is designed by combining the precise control torques obtained by the analytical dynamics method with the compensation control torques obtained by sliding mode method for trajectory tracking of the manipulator with bounded uncertainty. Precise control torques obtained from the Udwadia–Kalaba modeling method could be applied for the control of the ideal manipulator tracking the desired trajectory. Compensation control torques obtained from the sliding mode concept and the Lyapunov stability theory could be applied to compensate the uncertainties of parameters and external disturbances, thus enhancing the robustness of the system. By combining precise control torques with compensation control torques, the end point of the manipulator with uncertainty could track the end of the ideal manipulator and then track the desired trajectory. The simulation results of the three-link manipulator with uncertainty show that the control method can make the controlled target approach the desired trajectory in relatively small torque ranges and obtain high stability accuracy, and the chattering is effectively reduced at the stage of approaching the sliding mode surface.

Turbojet Engine Parameters Calculation Based on Fuel Flow and Exhaust Gas Temperature

<div class="section abstract"><div class="htmlview paragraph">The aircraft jet engine is one of the most complex multivariable systems with multiple inputs and multiple outputs. To attempt to optimize control functions or to address diagnostic problems, a detailed knowledge of all jet engine design parameters and performances is required. Although jet engines have been around for almost a century, there are only a few companies in the world presently designing and manufacturing them; as such these companies possess detailed knowledge of all relevant design characteristics and performance parameters. In the event where jet engine technical details are unknown, or only a few of them are known from manufacturer’s catalogues, the challenge becomes how to calculate and extrapolate critical performance parameters based on only fuel flow, jet exhaust temperature and total thrust. The brief engineering concept of calculation steps presented herein has the goal of identifying unknown turbojet engine parameters and design characteristics based on limited measurements and test data obtained at static conditions and single operating point. Test data included steady state fuel flow, turbine gas exit temperature and total jet engine thrust. For the purpose of jet engine parameters estimate and identification, the Rolls-Royce VIPER 11 Mk 22-6 jet engine was used. Calculated and estimated jet engine design parameters were evaluated and compared with measured parameters under the same initial conditions. The results show that calculation and identification processes are accurate with an overall error of less than 1 percent. The presented numeric algorithm combined with off design parameters calculation, can be used in jet engine health monitoring and fault detection processes.</div></div>

Interest and Applicability of Meta-Heuristic Algorithms in the Electrical Parameter Identification of Multiphase Machines

Multiphase machines are complex multi-variable electro-mechanical systems that are receiving special attention from industry due to their better fault tolerance and power-per-phase splitting characteristics compared with conventional three-phase machines. Their utility and interest are restricted to the definition of high-performance controllers, which strongly depends on the knowledge of the electrical parameters used in the multiphase machine model. This work presents the proof-of-concept of a new method based on particle swarm optimization and standstill time-domain tests. This proposed method is tested to estimate the electrical parameters of a five-phase induction machine. A reduction of the estimation error higher than 2.5% is obtained compared with gradient-based approaches.

A Green Soundscape Index (GSI): The potential of assessing the perceived balance between natural sound and traffic noise

Urban soundscapes are dynamic and complex multivariable environmental systems. Soundscapes can be organized into three main entities containing the multiple variables: Experienced Environment (EE), Acoustic Environment (AE), and Extra-Acoustic Environment (XE). This work applies a multidimensional and synchronic data-collecting methodology at eight urban environments in the city of Córdoba, Argentina. The EE was assessed by means of surveys, the AE by acoustic measurements and audio recordings, and the XE by photos, video, and complementary sources. In total, 39 measurement locations were considered, where data corresponding to 61 AE and 203 EE were collected. Multivariate analysis and GIS techniques were used for data processing. The types of sound sources perceived, and their extents make up part of the collected variables that belong to the EE, i.e. traffic, people, natural sounds, and others. Sources explaining most of the variance were traffic noise and natural sounds. Thus, a Green Soundscape Index (GSI) is defined here as the ratio of the perceived extents of natural sounds to traffic noise. Collected data were divided into three ranges according to GSI value: 1) perceptual predominance of traffic noise, 2) balanced perception, and 3) perceptual predominance of natural sounds. For each group, three additional variables from the EE and three from the AE were applied, which reported significant differences, especially between ranges 1 and 2 with 3. These results confirm the key role of perceiving natural sounds in a town environment and also support the proposal of a GSI as a valuable indicator to classify urban soundscapes. In addition, the collected GSI-related data significantly helps to assess the overall soundscape. It is noted that this proposed simple perceptual index not only allows one to assess and classify urban soundscapes but also contributes greatly toward a technique for separating environmental sound sources.

Dynamic Phenomena Coupling Analysis and Modeling of Proton Exchange Membrane Fuel Cells

Dynamic variable coupling analysis is an important method to properly design a control structure for complex multivariable and multi-physical dynamic systems, such as fuel cells. The fuel cell is an electrochemical energy conversion device which includes different inter-coupled dynamic phenomena in electrical, fluidic, and thermal domains. In order to achieve optimized fuel cell performance, different operation variables, such as fuel cell temperature, inlet air flow rate, hydrogen pressure, and membrane water content, need to be properly controlled. In this paper, variable coupling analyses of fuel cell dynamic behaviors are presented and discussed based on a proton exchange membrane fuel cell (PEMFC) dynamic model, which considers in particular the transient phenomena in both fluidic and thermal domain. The analyses of dynamic phenomena step responses are conducted using the relative gain array for various control input variables. Quantitative analyses of coupling effects in different physical domains are shown and discussed. The analysis results can be used to optimize the controller design for fuel cell system.

A novel fast dynamic optimization approach for complex multivariable chemical process systems

AbstractA novel fast dynamic optimization approach is proposed for complex multivariable chemical process systems, where the control variables are parameterized with different non‐uniform time grids and are treated as optimal decision variables to obtain the optimal switching structures for tackling the shortcoming caused by the conventional uniform parameterization method. Meanwhile, an adaptive fast calculation approach is proposed to calculate the differential equations so as to decrease the optimization time. The gradient formulae of decision variables are therefore further derived so that the conventional gradient‐based optimization algorithm can be utilized easily. Two well‐known complex multivariable systems in engineering are tested as illustrations and are compared with other literature reports in detail, where the uniform discretization control vector parameterization (ud‐CVP) method is also developed as the comparative base. Numerical results show that the proposed method can achieve better optimization results with fewer parameters and lower computation costs.

Non-diagonal [formula omitted] weighting function design: Exploiting spatio-temporal deformations in precision motion control

Model-based control design requires a careful specification of performance and robustness requirements. In typical norm-based control designs, performance and robustness requirements are specified in a scalar optimization criterion, even for complex multivariable systems. This paper aims to develop a novel approach for the formulation of this optimization criterion for multivariable motion systems that exhibit spatio-temporal deformations. To achieve this, characteristics of the underlying system are exploited to design multivariable weighting functions. In contrast to pre-existing approaches, which typically lead to diagonal weighting functions, the proposed approach enables the design of non-diagonal weighting functions. Extensive experimental results confirm that the proposed procedure can significantly improve the performance of an industrial motion system compared to earlier approaches.

A predictive functional control algorithm for multivariable systems with time delay

To improve the dynamic characteristics and the coupling capability, a new predictive functional control algorithm is proposed for strong coupling multivariable systems with time-delay, which combines predictive functional control and decoupling control. Firstly, a decoupling control algorithm is proposed, in which first-order models with time-delay are established by analyzing the amplitude-frequency and phase-frequency characteristics of the decoupled subject. Then a controller is designed for the single-variable subjects after decoupling based on the principles of predictive functional control. The simulation results show that this proposed algorithm has less online computation time, faster tracking, and higher control accuracy. It can provide a more effective control for complex multivariable systems.

Polynomial design methods

Polynomial design methods

Designing fuzzy inference systems from data: An interpretability-oriented review

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.

Book reviews

Let us first leaf through the table of contents of this book. Chapter 1 reviews Laplace transforms and basic impulse response and input-output concepts. Chapter 2 uses analog computer simulation as a starting point for the study of realizations and their relationship to input-output behavior. This chapter includes controllability and observability, realizations and the solution of linear time-invariant state equations; it works simultaneously in discrete and continuous time, but is restricted to scalar (singleinput-single-output) systems. Chapter 3 introduces state feedback for pole placement and linear-quadratic design, while Chapter 4 introduces observers and direct transfer function design through polynomial equations; both chapters are also restricted to the scalar case. In Chapter 5, some algebraic ideas are presented, including linear subspaces. Chapter 6, 150 pages long, retraces the steps of Chapter 2, but for the more complex multivariable systems. The matrix fraction description is the basic representation used here; the required background in polynomial matrices is covered, and the input-output and internal (state) descriptions are well related. Chapter 7 does for multivariable systems what Chapter 3 does for the scalar case. The role of eigenvector selection in the state feedback problem is given particular prominence and yields a rather novel approach to the linear-quadratic problem. Chapter 8 is devoted to the polynomial matrix description introduced by Rosenbrock. Chapter 9 presents a few results on linear, time-varying systems, while Chapter 10 sketches developments in other areas of linear systems. There is an appendix with some useful facts from matrix theory.