Multi-input Multi-output Research Articles

Broadband MIMO all-optical unitary converter based on compact multimode interference on LNOI

We propose and demonstrate a reconfigurable 4 × 4 multi-input multi-output (MIMO) all-optical unitary converter (OUC) based on non-uniform multimode interference (nMMI-OUC) on a lithium-niobate-on-insulator (LNOI) platform, which has the advantages of broadband operation, compactness, low loss, and fabrication-friendliness. To enhance the bandwidth, compact non-uniform multimode interference is utilized to replace uniform multimode interference, achieving the same cross talk and larger bandwidth. Compared with OUC based on uniform multimode interference (uMMI-OUC), the couplers using nMMI-OUC are only half length. The experimental results show that the reconfigurable four mode demultiplexing have been realized with wavelength-dependent loss of less than 3 dB and cross talk of less than −17.3 dB over 100 nm (from 1520 nm to 1620 nm) wavelength range and ∼0.21 dB/coupler at 1550 nm wavelength for nMMI-OUC.

Enhancing Calibration Precision in MIMO Radar with Initial Parameter Optimization

For Advanced Driver Assistance Systems (ADASs), lots of researchers have been constantly researching various devices that can become the eyes of a vehicle. Currently represented devices are LiDAR, camera, and radar. This paper suggests one of the operation processes to study radar, which can be used regardless of climate change or weather, day or night. Thus, we propose a simple and easy calibration method for Multi-Input Multi-Output (MIMO) radar to guarantee performance with initial calibration parameters. Based on a covariance matrix, the modified signals of all channels improve performance, reducing unexpected interferences. Therefore, using the proposed coupling matrix, we can reduce unexpected interference and generate accurately calibrated results. To prove and verify the improvement in our method, a practical experiment is conducted with Frequency-Modulated Continuous-Wave (FMCW) MIMO radar, mounted on an automotive.

Optimized Deep Multi-Scale Three-Dimensional Convolutional Neural Network-Dependent Channel Estimation in 5G MIMO-OFDM Systems

Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is the main air interface for fifth-generation wireless communication. Channel estimation (CE) is a key factor for attaining efficient system performance of 5G wireless networks. The Deep Learning (DL) approaches employed in MIMO-OFDM systems can minimize the computational complexity of 5G networks while increasing the system performance and reliability. In this research work, an Optimized Deep Multi-Scale Three-Dimensional CNN-dependent Channel Estimation in a 5G MIMO-OFDM (DMTDCNN-CE-5G-MIMO-OFDM) System is proposed. It provides a comprehensive analysis of DMTDCNN methods, 5G channel methods, DMTDCNN-based channel estimation, and error performance for MIMO-OFDM receivers. Here, channel estimation plays a main role in the effectual system performance on the 5G wireless networks. Furthermore, DMTDCNN optimized with the Modified Social Group Optimization Algorithm (MSGOA) can develop the system performance with reliability by decreasing the computational complexity of 5G communication systems. It combines DMDTCN-MSGOA with convolutional and polar codes, utilizing hard and soft decoders, improving the quality of channel estimation by reducing PER and PAPR attenuation. In this case, 1/2-rate polar coding with block lengths of 128, 256, and 1024 bits is used, and the outcome is 1, 2, and 8 MIMO-OFDM signals that rely on 4-QAM. The proposed DMTDCNN-CE-5G-MIMO-OFDM method attains 20.89%, 33.45%, 25.67% high throughput and 15.67%, 22.57%, 38.98% low mean square compared with the existing models, such as DL-based CE in 5G MIMO-OFDM schemes (DL-CE-MIMO-OFDM), low complexity learning-dependent channel estimation OFDM by online training (LCL-CE-OFDM), dual CNN-based CE for MIMO-OFDM schemes (DCNN-CE-MIMO-OFDM) methods, respectively.

Dual Actuator Control Strategy for Temperature Stability in High Temperature Solar Receivers

Abstract Fluctuations in incoming solar energy adversely affect the temperature stability within solar receivers, leading to a decrease in thermal efficiency. Therefore, it is essential to design a control system with the capability to maintain quasi-steady temperatures inside the reactor consistently throughout the day. This study introduces a dual actuator control technology to regulate the temperature within a high–temperature cylindrical cavity type gas receiver. The actuating system comprises two primary components. The first component involves a variable aperture mechanism, executed through a rotary mechanism made of stainless steel. This mechanism features seven holes of fixed diameters arranged in a half-circle configuration. The rotary mechanism is powered by a stepper motor regulated by a feedback control system. The second actuator is a Mass Flow Controller (MFC) responsible for meticulous adjustment of the inlet gas flow directed towards the solar receiver. The Direct Normal Irradiance (DNI) is simulated using a 10 kW High Flux Solar Simulator (HFSS) with a variable power supply ranging from 80 to 200 A. This setup enables the simulation of different operational conditions. The dual actuator method concurrently adjusts both gas flowrate and aperture size. While utilizing each of these methods individually can achieve reasonable temperature control performance, the hybrid approach leverages the strengths of both control methods, resulting in a significant improvement in the temperature regulation performance of the solar receiver. Two control strategies, namely Proportional Integral (PI) and Model Predictive Controller (MPC), were implemented to regulate the temperature inside a cavity type gas receiver. Experimental tests indicate that the incorporating the dual actuators controller is a promising technique, and its application can be extended to include additional parameters for utilization in a multi–input multi-output (MIMO) control system.

Adaptive compensation for multi-axial real-time hybrid simulation via nonlinear parameter estimation

For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering.

Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs

This study proposes a regression-based approach for calculating the likelihood probability in time-varying multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters. These time-varying MIMO systems often face performance challenges because of the difficulty in tracking changes in the likelihood probability. To address this challenge, the proposed method leverages channel statistics and decoded outputs to refine the likelihood. An optimization problem is then formulated to minimize the mean-squared error between the true and refined likelihood probabilities. A linear regression approach is derived to solve this problem, and a regularization technique is applied to further optimize the calculation. The simulation results indicate that the proposed method improves reliability by effectively tracking temporal variations in the likelihood probability and outperforms conventional methods in terms of performance.

Contoured Bode Plot based Robust Decentralised Controller for Three-Input Integrated Dc-Dc Converter

Designing controllers for multi-input multi-output (MIMO) integrated Dc-Dc converter is complicated due to shared elements, integrated structure, and relation between the input and output variables of the converter. In this work, a robust PID controller based on a Contoured Robust Controller Bode Plot (CRCBP) is designed for control of the three-input integrated Dc-Dc (TIID) converter. This method combines robust control with classical loop-shaping. In this procedure, the outlines of the robust metric are drawn on the Bode charts of the controller, and the controller is adjusted till its frequency response does not cross the contours of the robust metric to meet the stability and performance goals. The TIID converter is modeled using state-space analysis and a Transfer Function Matrix (TFM) is acquired from the small signal continuous time model. The interactions between the inputs and outputs of the converter are quantified and input-output pairing is identified by Relative Gain Array (RGA). The input-output pairing suggested by RGA decides the controller structure. Further, the weight functions (loop-shaping filters) are designed based on the TFM which represents the desired robustness and performance of the controller. These weight functions are used to define the robust metric for the controller design. Based on this, the CRCBP controller is designed iteratively. A standard TIID converter of power rating 288 W with input voltage levels of 24V, 30V, and 36V is considered to show the effectiveness of the proposed controller under varying operating conditions. The real-time simulation results disclose the proposed controller's superiority over the existing approaches in the literature.

A state-space fuzzy control model to enhance the lifespan of hybrid storage systems

Renewable energy sources have become a priority in developing electrical systems due to growing environmental concerns over fossil fuel-based energy and its consequences. Much emphasis has been paid to integrating green energies, especially wind and solar power, with energy storage systems in microgrids and electric car systems. The lifespan of a battery system (BS) is usually short due to irregular charging patterns and frequent deep discharging cycles. This study presents a hybrid energy storage system (HESS) and energy management strategy (EMS) based on the designed state-space fuzzy control (SSFC) technique that uses a combination of state-space as an input to the fuzzy control to improve the HESS lifespan. The state space translates the system's digital computation, provides the allowance of multi-input multi-output (MIMO) into the system, gives zero state response to the system and describes the internal state of the system. The designed SSFC system is built to limit the degradation rates, equalize the state of charge (SoC), and monitor the temperature. Those factors are used to prolong the lifespan of the proposed hybrid energy storage system (HESS) and to improve energy management systems. The SSFC is simple to use with nonlinear and time-varying systems, and it offers a more comprehensive and precise representation of the behavior of the system. The system is modelled using MATLAB for coding and Simulink for system simulations. The comparison of the proposed SSFC, optimal, and fuzzy models show a considerable improvement in the parameters that affect the lifespan of the HESS. Therefore, this study can serve as a catalyst model for the optimal design and sizing of hybrid storage systems.

Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.

MIMO ultra-local model-based adaptive enhanced model-free control using extremum-seeking for coupled mechatronic systems

Multiple degree-of-freedom (DOF) mechatronic systems, such as robots and robotic arms, play a crucial role in modern life and production. However, due to strong coupling, uncertain dynamics, and external disturbance, accurately modeling these systems is challenging, making traditional model-based control methods impractical. To address this, this paper proposes an extremum-seeking-based adaptive enhanced model-free control for multi-input multi-output (MIMO) mechatronic systems to realize robust trajectory tracking. Unlike previous model-free control methods that decouple and reorganize the MIMO system into several single-input single-output ultra-local models, this paper develops a MIMO ultra-local model with a non-diagonal gain matrix α to approximate the system dynamics within an ultra-short time window. Time-delay estimation (TDE), Proportional-Derivative (PD) control law and accuracy compensation compose an TDE-based enhanced intelligent PD control that ensures the closed-loop stability. Furthermore, an extremum-seeking (ES) technique is designed to optimize the gain matrix α to enhance control performance. The main contributions of this paper are the development of a model-free control framework based on the MIMO ultra-local model and the successful application of ES to optimize the non-diagonal gain matrix α. Stability analysis of the closed-loop system is conducted using Lyapunov theorem. Finally, numerical simulations on a 2-DOF robotic manipulator and co-simulation results on a 3-DOF PUMA 560 robotic manipulator validate the effectiveness and superiority of the proposed methods.

A Fixed‐Time Composite Control With Variable Exponent Coefficients for Stewart Parallel Mechanism Tracking in Task Space

ABSTRACTTo improve tracking control performance in the task space of the Stewart parallel mechanism (SPM) with uncertainties of modeling errors and external disturbances in our developed rust removal sandblasting robot for the surface roughness processing of a bridge's steel box girder, a fixed‐time composite control with variable exponent coefficients (VEC‐FxTCC) approach for multi‐input multi‐output (MIMO) nonlinear robotic systems is developed. Firstly, by devising only one regulation term with a variable exponent coefficient (VEC) function in the system's Lyapunov differential inclusion, a novel VEC fixed‐time stability theorem for the MIMO nonlinear dynamic system is proposed and proven. Secondly, based on the proposed theorem, VEC‐FxTCC is formed by designing and combining a VEC fixed‐time disturbance observer (VEC‐FxTDOB) and a VEC fixed‐time super‐twisting control (VEC‐FxTSTC), in order to achieve fast, steady, and uniformly bounded‐time convergence for SPM's end‐effector tracking, while avoiding singularity. Finally, the effectiveness of the proposed VEC‐FxTCC approach is validated through the simulations and the rust removal sandblasting robot prototype experiments.

A Sub-6 GHz 8 × 8 MIMO Antenna Array for 5G Metal-Frame Mobile Phone Applications

This article introduces a broadband sub-6 GHz 8 × 8 MIMO (multi-input multi-output) antenna array for 5G (fifth-generation) metal-frame mobile phone applications. The unique advantage of this compact antenna design is its placement in the corners of the mobile phone, allowing for significant PCB board space reduction. The proposed antenna’s 6 dB impedance bandwidth ranged from 3.3 to 6 GHz, covering the n77/78/79 and WiFi-5GHz bands. The main radiating element was an open-slot antenna coupled by a T-shaped structure connected to a 50-Ω transmission line. The size of the single-antenna element was 12.25 mm × 2.5 mm × 7 mm, and these antennas were symmetrical at four corners of the smartphone. A wide slot and neutral line were incorporated to reduce mutual coupling between adjacent antennas. The MIMO antenna array achieved isolation above 12 dB. The peak realized gain ranged from 2 to 5.28 dBi, and the total efficiency spanned 37% to 71%. The ECC (envelope correlation coefficient) was less than 0.34, and the CC (channel capacity) ranged from 33 and 41 bps/Hz.

Mathematical Techniques in the Design of Robust Control Systems

Solid control frameworks are exceptionally imperative for making beyond any doubt that energetic frameworks remain steady and work well in a parcel of diverse circumstances and with a parcel of questions. This diagram tells you everything you wish to know about the numerical strategies utilized to form steady control systems. Strong control tries to bargain with issues like unknowns within the system model, stuns from the exterior, and changes within the system's parameters. It does this to form beyond any doubt that the framework keeps working well and remaining steady in spite of these issues. Lyapunov soundness hypothesis and the H-infinity (H∞) optimization strategy are two of the foremost critical arithmetic apparatuses. Lyapunov steadiness hypothesis gives us a way to see at how steady a framework is, and H∞ minimizes the worst-case pick up from unsettling influence to yield. State-space representations are moreover utilized to show and ponder multi-input multi-output (MIMO) frameworks, which makes control plan more organized. When making controls that guarantee steady execution, strategies like Direct Framework Disparities (LMIs) and Riccati conditions are utilized. The thought of unwavering quality limits is additionally displayed to degree how well the framework can handle factors. The solid control plan incorporates frequency-domain apparatuses, such as the Nyquist and Bode plots, to see at how the framework acts at distinctive frequencies. This makes it simpler to form controllers that can bargain with high-frequency occasions. We conversation around progressed strategies like α-synthesis and strong shaft situation that can offer assistance with certain control problems in dubious settings. Down to earth illustrations appear how these scientific strategies can be used, showing how vital they are in numerous building areas, such as the airplane, vehicle, and prepare control industries. The unique emphasizes how imperative these numerical strategies are for making beyond any doubt that solid control frameworks can handle the challenges of the genuine world, which improves system security, execution, and steadfastness in a wide run of circumstances. The abstract's objective with this outline is to provide you a basic idea of the math that's critical to the field of solid control framework plan.

Adaptive Two-Degree-of-Freedom Robust Gain-Scheduling Control Strategy

This study introduces a novel tracking control strategy tailored to aeroengines, which are highly nonlinear and characterized by significant uncertainty. The proposed method entails a robust extended Kalman filter (REKF) enhanced by a forgetting factor for improved performance. An accompanying augmented, mixed onboard adaptive model based on the REKF precisely estimates and manages engine performance degradation. This advanced model effectively counters the degradation term in the perturbation block of the engine’s uncertain model. Using this strategic approach, a robust gain-scheduling controller was constructed and was found to outperform its predecessors, marking a notable advancement in control system design. Controlling twin rotor multi-input, multi-output (MIMO) systems is a highly complex process due to model uncertainties and unpredictable external disturbances. To address these challenges, we constructed an adaptive two-degree-of-freedom robust gain-scheduling controller (ATDF-RGSC) using a mixed sensitivity approach. Rigorous performance analysis confirms that this controller offers enhanced robustness, faster tracking, and more precise disturbance attenuation compared to other methods. These advanced control strategies successfully manage uncertainties and disturbances, improving performance metrics in both simulated and experimental scenarios. The proposed method may significantly enhance the safety and reliability of aeroengines and MIMO systems in practical applications.

Spatial patterns of hydroecological health in the semi-arid yellow river basin: Revelations from machine learning models

The ecosystem of semi-arid watersheds is influenced by a combination of natural climate factors, rainfall, and habitat destruction, resulting in complex mechanisms of spatial differentiation and evolution of water ecological health. Indicator selection in mainstream water ecological health assessment methods, such as the Index of Biotic Integrity (IBI), often relies on subjective reference point choices. This approach tends to overlook the comprehensive impacts and interactions among various environmental stressors. For watersheds significantly influenced by natural climatic factors, considerable uncertainties arise, leading to a lack of scientific justification for establishing water ecological health protection goals. In this study, the nonlinear capabilities of the random forest (RF) model were applied to reduce subjectivity in traditional water ecological health assessments and to more accurately reveal the emerging spatial differentiation patterns and underlying causes of water ecological health in the Wei River Basin (WRB), the largest typical semi-arid watershed of the Yellow River in China. Our findings indicate: (1) Traditional evaluation indices indicate that the overall water ecological health of the WRB is classified as sub-healthy (60 %). The core indicators include dominant species, total algal density, and the percentage of diatom density, with no significant spatial differentiation observed. (2) An improved water ecological health assessment method for semi-arid watersheds, based on the RF model, has been developed to replace traditional subjective judgment steps. This method establishes a complex multi-input–output response relationship (R2>0.85) between environmental stress indicators and the biological integrity index for the WRB. (3) The model results identify key driving factors affecting changes in water ecological health in semi-arid watersheds, with the sensitivity of the new model increasing nearly 11-fold compared to traditional IBI methods. (4) Following improvements, the water ecological health characteristics of the WRB exhibit significant spatial heterogeneity, with a higher dispersion coefficient (1.21), and demonstrate enhanced nonlinear response trends to climatic factors. The application of machine learning models indicates that traditional methods may underestimate the extent of ecological health degradation in watersheds and tend to oversimplify spatial heterogeneity characteristics.

Reduced model of MIMO systems using Meixner-like functions

This article investigates the use of orthogonal basis functions, particularly Meixner-like functions, to streamline and optimize the parameter complexity in multi-input multi-output (MIMO) system modeling. We introduce a new optimal model for MIMO systems, developed by decomposing the auto-regressive MIMO model with external inputs (ARX) using orthonormal Meixner-like bases. This model, referred to as the MIMO ARX Meixner-like model, enables filtering of the system’s inputs and outputs with Meixner-like functions. Parameter reduction is achieved through the optimization of Meixner-like poles using an iterative technique based on the Newton-Raphson method. The proposed model’s efficacy is demonstrated through simulations on two experimental systems: a two-degrees-of-freedom (DOF) helicopter and a two-tank process. Results reveal that the model effectively reduces approximation errors with fewer parameters. Comparative analysis with the traditional MIMO ARX model confirms the superior performance of the MIMO ARX Meixner-like model.

A low-complexity solution for LMIs of hardly constrained nonlinear MIMO tracking control systems

This research is dedicated to the discrete-time tracking control of nonlinear multi-input multi-output (MIMO) Newtonian mechanics systems with an equal number of inputs and outputs, in the presence of input saturation and high-order dynamics of the actuation system. Firstly, the continuous-time model of the plant and actuation system has been transformed into the discrete-time domain using Adams–Bashforth and Adams–Moulton discretization techniques. In the next step, considering saturation boundaries, the modified position and velocity references are derived using the discrete-time reaching law-based control approach. These modified references are obtained through an optimization algorithm that calculates the closest achievable position and velocity trajectories with respect to the desired reference trajectory. Modifying desired trajectories in MIMO systems results in the formation of a system of linear matrix inequalities (LMIs). Solving such systems of inequalities requires the use of complex methods, and this difficulty increases with the increase in the system’s degrees of freedom (DOFs). The proposed strategy offers a more straightforward approach to solving these inequalities. Once the modified references are calculated, the corresponding position and velocity control signals can be obtained using the mentioned reaching law strategies. The final step of the proposed algorithm is to combine the resulting position and velocity control signals using a Gaussian-weighted averaging method to form the main control signal. A desirable performance for the control system can be achieved by appropriately adjusting the standard deviations of the Gaussian weighing functions. To evaluate the performance characteristics of the proposed method, it has been applied in the tracking control procedure of a nonlinear 3-DOF mechanical system. The numerical simulation results demonstrate the effectiveness of the method.

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

Identification methodology for MIMO Hammerstein nonlinear model with process noise

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.