SISO Systems Research Articles

Design of an Advanced Optimal Fuzzy Controller For a Binary Distillation Column

The most common control philosophy followed in the chemical process industries is the SISO system using the conventional PID controller algorithms. One drawback is relying on models for both control and design work in Chemical process industries (CPI) is that many problems are very complex and accurate models are difficult, if not impossible to obtain. To overcome these problems, it will be helpful to apply techniques that use human judgment and experience rather than precise mathematical models, which in the major cases deduced from the linearization of the system and simplification hypothesis. The fuzzy logic systems are capable of handling complex, nonlinear systems using simple solutions. However, obtaining an optimal set of fuzzy membership functions is not an easy task. In this chapter a solution based on artificial intelligence is proposed to improve the control of a binary distillation column. The solution is based on the use of the advantages of both fuzzy logic and genetic algorithms. The fuzzy logic is used as a supervisory PI controller that is a simple PI controller that generally used in controlling distillation columns with parameters deduced from the fuzzy supervisor. The membership functions shape is deduced by using research algorithms based on hierarchical genetic algorithms. The results show that the Fuzzy supervisory PI controller provide an excellent tracking toward set point change.

MIMO Technology is Used to Improve the Security Capacity Stability of Rayleigh Wiretap Channel

This paper analyzes the security capacity when the Rayleigh channel codec is known, and studies the relationship between the transmit power, the estimation error and the security capacity difference. The results show that the increase of transmit power can make the security capacity difference tend to a stable value, which indicates that the influence of imperfect channel state information on the security capacity tends to be stable. By comparing the transmission power of SISO system and MIMO system, it is proved that MIMO system usually has higher transmission power under the same received power, so it is more suitable for improving the security capacity stability of Rayleigh channel.

Comparative Evaluation of NOMA and OFDMA over Multipath Fading Rayleigh Channels in Downlink SISO System

Comparative Evaluation of NOMA and OFDMA over Multipath Fading Rayleigh Channels in Downlink SISO System

A Comparative Analysis of DNN and Conventional Signal Detection Techniques in SISO and MIMO Communication Systems

A Comparative Analysis of DNN and Conventional Signal Detection Techniques in SISO and MIMO Communication Systems

Design of active vibration isolation system for satellite optical load based on Stewart platform

According to the requirements of hyperstatic environment of a satellite optical load during in orbit operation, the Stewart active vibration isolation platform was designed, and its dynamic equation was established by Newton Euler method. The virtual prototype model was established in ADAMS, and the accuracy of the model was verified by comparing the numerical solution and simulation solution of the modal frequency of the platform; On this basis, the dynamic model is decoupled into six SISO systems, and the optimal controller is designed for each subsystem. Finally, the whole system is simulated by ADAMS/MATLAB, and the displacement response curves of the load before and after control are obtained. The simulation results show that the Stewart active vibration isolation platform can effectively suppress the vibration.

Delay‐difference approximations of PD‐controllers. Improperly‐posed systems in multiple delays case

SummaryIn this paper, we study the behavior of characteristic roots of a class of strictly proper LTI SISO systems that are subject to a PD controller for which the derivative action has been implemented by means of a delay‐difference approximation scheme that uses multiple delays. To such an end, the improperly‐posed case is discussed in depth, that is, the situations where “small delays” induce instability in the closed‐loop system. The effect of adding more delays on the improperly‐posed case is analyzed and a methodology for determining the set of parameters that produces such a situation for a given number of delays is derived. Some illustrative examples complete the presentation.

Model order reduction for SISO and MIMO system using improved adaptive differential evolution algorithm

Model order reduction for SISO and MIMO system using improved adaptive differential evolution algorithm

Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory

Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a single-user SISO (SU-SISO) system with out-of-cell interference, LA is performed by computing the post-equalization signal-to-interference-noise ratio (SINR) at the receiver. The same technique can be employed in multi-user MIMO (MU-MIMO) receivers that use linear detectors. Another important use of post-equalization SINR is for physical layer (PHY) abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations. However, for MU-MIMO systems with non-linear receivers, there is no known equivalent of post-equalization SINR which makes both LA and PHY abstraction extremely challenging. This important issue is addressed in this two-part paper. In this part, a metric called the bit-metric decoding rate (BMDR) of a detector, which is the proposed equivalent of post-equalization SINR, is presented. Since BMDR does not have a closed form expression that would enable its instantaneous calculation, a machine-learning approach to predict it is presented along with extensive simulation results.

Performance analysis of IRS-assist wireless communication system with Alamouti transmit diversity scheme

Intelligent Reflecting Surface (IRS) is an emerging and growing technology for upcoming B5G and 6G wireless standards to design a perfect smart radio environment. In this article, we try to enhance the physical layer performance of the IRS- assist wireless communication system by taking some helpful performance metrics such as the outage probability (OP), average bit error rate (ABER), and average capacity of the system. Here, we assume our system is mainly affected by phase shift noise and quantization noise due to a discrete number of phase shifts of the IRS elements. The Alamouti STBC transmitter diversity technique is used to design the IRS-assist MISO wireless communication system to mitigate the effect of these kinds of noise, leading to the works’ novelty. We have derived a new and most accurate mathematical framework of PDF and MGF of the corresponding proposed IRS-assist wireless communication system with an end-to-end signal-to-noise ratio (SNR). Behind this statistical distribution, we expressed unique and more precise closed-form expressions of each performance metric under IRS assist SISO and MISO wireless link for the entire SNR region and it is significantly observed that the proposed MISO system provides better outcomes than the general SISO system. Furthermore, we calculate the diversity order of both configurations and it shows that the diversity order of the links is associated with the number of reflecting elements, phase noise error, and average magnitudes of the system. We also validated our analytical outcomes using Monte Carlo simulation results from MATLAB.

A modified deep learning based MIMO communication for integrated sensing, communication and computing systems

The future wireless networks are expected to be data-driven integrated sensing, communication and computing (ISCC) systems and benefit from employing deep learning to process the large amount of data with less resources and computational limit. As a result, deep learning based learning MIMO communication has been widely discussed in recent years and show fascinating performance advantages over human designed MIMO systems. However, we find that the autoencoder frameworks proposed in current deep learning based methods are not able to be implemented under multipath channels, which is the most common channel model in actual environment. To overcome this problem, a modified autoencoder framework is proposed in this paper. This proposed autoencoder framework introduces well designed convolutional neural layers into the widely used feedforward neural networks, and demonstrates significant performance improvements over traditional Turbo code method adopted in existing wireless MIMO systems. Moreover, computer simulations are performed and the proposed autoencoder framework is also demonstrated to be effective in SISO and MISO systems under AWGN and Rayleigh fading channels, which verifies the generality of the proposed autoencoder framework for end-to-end learning communication systems.

Modified Active Disturbance Rejection Predictive Control: A fixed-order state–space formulation for SISO systems

This paper presents a novel control strategy that provides active disturbance rejection predictive control on constrained systems with no nominal identified model. The proposed loop relaxes the modelling requirement to a fixed discrete-time state–space realisation of a first-order plus integrator plant despite the nature of the controlled process. A third-order discrete Extended State Observer (ESO) estimates the model mismatch and assumed plant states. Moreover, the constraints handling is tackled by incorporating the compensation term related to the total perturbation in the definition of the optimisation problem constraints. The proposal merges in a new way state–space Model Predictive Control (MPC) and Active Disturbance Rejection Control (ADRC) into an architecture suitable for the servo-regulatory operation of linear and non-linear systems, as shown through validation examples.

Industrial Safety and Occupational Health Strategies: The Case of a Water Treatment Plant in Ecuador

This research aims to establish strategies in Industrial Safety and Occupational Health (SISO) using the case study of a water treatment plant in Ecuador; preventive activities in this area are poorly developed, being relegated to the background for not being considered important for production processes, this reality is worrying because despite the efforts made to implement a series of public policies; the figures of accidents and incidents indicate that it is still required to improve the SISO systems, to ensure the life, health and integrity of workers. The methodology used is descriptive and correlational, applying the inductive, deductive and analytical method, in addition to the integration of elements and sub-elements of an Occupational Safety and Health Management System proposed by the Andean Instruction Regulations, Resolution 957: 2008, and the ISO 45001:2018 standard; the population of interest was considered to be the 59 people working at the Water Treatment Plant in Santa Elena-Ecuador, of which 24 were selected to collect data through a survey. As a result, the proposed strategies are on par with the ISO 45001:2018 standard, which includes all the necessary requirements that an organization must have to ensure the safe provision of its services, minimizing risks or accidents, and providing a safe working environment for people.

Advances in MIMO Antenna Design for 5G: A Comprehensive Review.

Multiple-input multiple-output (MIMO) technology has emerged as a highly promising solution for wireless communication, offering an opportunity to overcome the limitations of traffic capacity in high-speed broadband wireless network access. By utilizing multiple antennas at both the transmitting and receiving ends, the MIMO system enhances the efficiency and performance of wireless communication systems. This manuscript specifies a comprehensive review of MIMO antenna design approaches for fifth generation (5G) and beyond. With an introductory glimpse of cellular generation and the frequency spectrum for 5G, profound key enabling technologies for 5G mobile communication are presented. A detailed analysis of MIMO performance parameters in terms of envelope correlation coefficient (ECC), total active reflection coefficient (TARC), mean effective gain (MEG), and isolation is presented along with the advantages of MIMO technology over conventional SISO systems. MIMO is characterized and the performance is compared based on wideband/ultra-wideband, multiband/reconfigurable, circular polarized wideband/circular polarized ultra-wideband/circular polarized multiband, and reconfigurable categories. The design approaches of MIMO antennas for various 5G bands are discussed. It is subsequently enriched with the detailed studies of wideband (WB)/ultra-wideband (UWB), multiband, and circular polarized MIMO antennas with different design techniques. A good MIMO antenna system should be well decoupled among different ports to enhance its performance, and hence isolation among different ports is a crucial factor in designing high-performance MIMO antennas. A summary of design approaches with improved isolation is presented. The manuscript summarizes the various MIMO antenna design aspects for NR FR-1 (new radio frequency range) and NR FR-2, which will benefit researchers in the field of 5G and forthcoming cellular generations.

DIRECT MODEL REFERENCE TAKAGI–SUGENO FUZZY CONTROL OF SISO NONLINEAR SYSTEMS DESIGN BY MEMBERSHIP FUNCTION

What is discussed in this article is to find a way for membership functions optimally. In most scholars, these functions are constant and have a limited number. Therefore, in some cases, this limitation reduces control performance improvement. One of the best solutions is finding these functions in a differential form. This article used the Takagi-Sugeno function as a fuzzy detector to identify and control a nonlinear SISO system by direct adaptive reference model control. Using this method with Lyapunov for the stability of the control system makes output fuzzy linguistic variables optimally. Then simultaneously using these values, membership functions can be defined in differential form. Therefore, there is no other limitation in the variance and midpoint.

Stable approximation of SISO and MIMO linear dynamic systems

Stable approximation of SISO and MIMO linear dynamic systems

An Improved Method for Order Reduction of High Order Uncertain SISO Dynamic Systems by Affine Arithmetic

This article presents a refined algorithm using Modified Polynomial Differentiation (MPD) method through Affine Arithmetic (AA) to reduce the high order uncertain systems. This new algorithm is applicable for the reduction of Continuous SISO systems. Measurement errors due to variation in ambient conditions may result the system as an uncertain system unlike other methods in literature. This strategy, unlike previous ones found in the Iteration literature, can produce a reduced order model that is stable from a original high order uncertain system that is also stable. Applications of Affine Arithmetic steer clear of many of the drawbacks of Interval Arithmetic, including unbounded solutions in a number of important applications. Utilizing common numerical examples from interval literature, the proposed approach has been validated

Q-Markov Covariance equivalent realizations for unstable and marginally stable systems

An observer-based q-Markov Covariance equivalent realization (QMC) formulation is presented in the paper. It is shown that by inserting an observer in the input–output relationship associated with the dynamical system, a QMC approach can be developed that is applicable to systems with marginally stable or unstable equilibrium points. A system of equations governing all linear state-space realizations, along with the corresponding observers, is derived from matching a set of Markov and Covariance parameters. The solution to this equation system is shown to parameterize all state-space realizations that match the pre-specified correlation functions. Four numerical examples are used to show the utility of the proposed approach. The numerical results show that the observer-based QMC presented in the paper can identify linear SISO and MIMO systems with stable, marginally stable, and unstable characteristics.

Adaptive global prescribed performance tracking control of uncertain robotic systems with unknown control directions

AbstractIn this article, we develop an adaptive global prescribed performance tracking control scheme for nonlinear multiple‐input multiple‐output (MIMO) robotic systems in the presence of unknown control directions and nonparametric uncertainties, in which the proposed solution mainly includes the following steps. Firstly, by constructing a global prescribed performance function, together with a series of transformations, the corresponding control guarantees the position tracking error evolves strictly within the prespecified region for all time, which is independent of initial conditions; Secondly, in order to cope with the problem of multiple unknown control directions, we make use of a symmetric matrix lemma to convert the underlying problem from matrix form to scalar form, so that the Nussbaum‐gain lemma for SISO systems becomes effective for MIMO systems with multiple inputs; In addition, we employ the integrable function to handle the nonparametric uncertainties, enabling the control scheme with the capability to drive the tracking error toward zero asymptotically. The effectiveness of the proposed control is validated via a two‐link rigid robotic manipulator.

Pseudo feedforward dynamic matrix control algorithm for desired closed-loop performance in MPC systems

As a practical approach, the dynamic matrix control (DMC) algorithm has been widely used in the control of chemical processes for decades. Nevertheless, there is no strong relationship between the tuning parameters of DMC and the closed-loop dynamics. In this article, a pseudo feedforward dynamic matrix control (PFFDMC) algorithm for realizing desired closed-loop performance in industrial systems is proposed. Building on insights from cascade control, a pseudo feedforward compensator is added in front of the process to precondition the overall system offline for expected characteristics achievement. Then, the actual manipulated variables can be obtained first by solving a modified objective function and then through matrix transformation. The proposed algorithm provides a quantitative relation between the pseudo feedforward sequence and the final setpoint tracking results rather than a qualitative way. Numerical simulations on SISO and MIMO systems are performed to verify the effectiveness of the proposed algorithm on performance criteria achievement.

Data‐driven fault‐tolerant control for SISO nonlinear system with unknown sensor fault

SummaryThis paper studies the adaptive fault‐tolerant control problem for a general nonlinear discrete‐time SISO system with unknown system model and sensor fault. First, utilizing the input‐output (I/O) data, an equivalent full‐form dynamic linearization (FFDL) data model is to be constructed by introducing a pseudo‐gradient vector. Then, to estimate the system's actual output from the sensor measurements corrupted by unknown faults, a nonlinear autoregressive with external input neural network (NARXNN) is employed and well‐trained, by which the compensation of the fault signal can hence be derived indirectly. Based on the optimality criterion, an adaptive fault‐tolerant control (FTC) strategy is therefore proposed, which promises the convergence of tracking error and the boundedness of system signals. The effectiveness of the proposed FTC algorithm is illustrated by simulation results.