Multi-input Multi-output Model Research Articles

The nonlinear multi-variable grey Bernoulli model and its applications

This work uses the vector-valued Bernoulli equation to build a nonlinear multi-variable grey Bernoulli model, which is available to describe the nonlinear relationship between the output variables. By using Pade´ approximation, the proposed model can be implemented with high time efficiency. Additionally, the Sine Cosine Algorithm is employed to determine the Bernoulli exponent, thereby enhancing prediction accuracy. To evaluate the predictive performance of the proposed model, three case studies using three real-world data sets with different features of predicting per capita household income, fuel prices and crude oil prices are carried out. The results are compared with three existing grey multi-input multi-output models. Experimental results demonstrate that the proposed model excels in handling nonlinear relationships between variables and has strong robustness against noise, consistently delivering lower error values, demonstrating superior predictive performance.

Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing

Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing

A Novel Channel Estimation Framework in MIMO Using Serial Cascaded Multiscale Autoencoder and Attention LSTM with Hybrid Heuristic Algorithm.

In wireless communication, multiple signals are utilized to receive and send information in the form of signals simultaneously. These signals consume little power and are usually inexpensive, with a high data rate during data transmission. An Multi Input Multi Output (MIMO) system uses numerous antennas to enhance the functionality of the system. Moreover, system intricacy and power utilization are difficult and highly complicated tasks to achieve in an Analog to Digital Converter (ADC) at the receiver side. An infinite number of MIMO channels are used in wireless networks to improve efficiency with Cross Entropy Optimization (CEO). ADC is a serious issue because the data of the accepted signal are completely lost. ADC is used in the MIMO channels to overcome the above issues, but it is very hard to implement and design. So, an efficient way to enhance the estimation of channels in the MIMO system is proposed in this paper with the utilization of the heuristic-based optimization technique. The main task of the implemented channel prediction framework is to predict the channel coefficient of the MIMO system at the transmitter side based on the receiver side error ratio, which is obtained from feedback information using a Hybrid Serial Cascaded Network (HSCN). Then, this multi-scaled cascaded autoencoder is combined with Long Short Term Memory (LSTM) with an attention mechanism. The parameters in the developed Hybrid Serial Cascaded Multi-scale Autoencoder and Attention LSTM are optimized using the developed Hybrid Revised Position-based Wild Horse and Energy Valley Optimizer (RP-WHEVO) algorithm for minimizing the "Root Mean Square Error (RMSE), Bit Error Rate (BER) and Mean Square Error (MSE)" of the estimated channel. Various experiments were carried out to analyze the accomplishment of the developed MIMO model. It was visible from the tests that the developed model enhanced the convergence rate and prediction performance along with a reduction in the computational costs.

Circular economy-based multi-objective decentralized controller for activated sludge wastewater treatment plant

Introduction: Water scarcity and water pollution are two major issues in India. Circular economy-based wastewater treatment technology provides the most sustainable solutions for solving these issues. In this paper, a novel multi-objective decentralized controller (MODC) is proposed for benchmarking a multi-input multi-output (MIMO) activated sludge wastewater treatment plant (WWTP) to achieve maximum effluent quality with minimum cost. WWTPs with conventional control schemes consume more energy to achieve the desired effluent quality.Methods: In this study, a MIMO model is developed for the activated sludge process (ASP) from a physics-based model, and relative gain array (RGA) analysis are carried out to determine the interaction between the loops to identify a suitable control scheme for the MIMO process. In addition, a multi-objective decentralized control problem is formulated to achieve the conflicting multiple objectives of improving effluent quality and minimizing operational costs by efficient usage of energy.Results and discussion: The desired quality and cost reduction are verified by comparing the integral square error (ISE) and control effort (CE) values of a closed-loop WWTP. A multi-objective evolutionary algorithm (MOEA), namely, the non-dominated sorting genetic algorithm (NSGA)-II, successfully solves the multi-objective control problem. NSGA-II provides several optimal solutions in the Pareto front. In order to demonstrate the feasibility of the proposed controller, three optimal solutions are selected from the Pareto-optimal front, and their closed-loop performances are evaluated qualitatively and quantitatively for both servo and regulatory operations. Improving the quality of effluent enhances active sludge production, which in turn increases the methane production in the anaerobic digester.

Improving Dynamic Accuracy of the Electric Motor Emulator at High Speed via MIMO Design Method

The application of electric motor emulators (EME) has been prosperous in recent years along with the advancement in power hardware-in-the-loop technology. However, EME design methods based on a typical modeling approach and control structure tend to be invalid in promising a clear, dynamic accuracy objective and a robust dynamic accuracy performance over wide working points, especially at high operating speed. Hence, a new EME design method based on multi-input multioutput (MIMO) modeling and a MIMO closed-loop current control (MCLCC) structure is proposed in this article. Singular values of the MIMO motor model are analyzed to clarify the influence of the rotor speed on the dynamic accuracy objective of EME for the first time, based on which an unknown input observer is designed in the MCLCC structure to compensate for disturbance voltages caused by modeling errors. The transient tracking errors of both current and torque responses can thus be remarkably reduced at 300-Hz electrical speed in the experiment even if 35% modeling errors exist, verifying the superiority of the proposed EME design method in dynamic accuracy.

Inverse-model-based iterative learning control for unknown MIMO nonlinear system with neural network

This paper provides an inverse-model-based iterative learning control (ILC) for the unknown multi-input multi-output (MIMO) nonlinear system with neural network (NN), where a novel gradient adaptive law is used to update the NN weights both hidden and output layers such a faster convergence can be achieved. First, a three-layer NN structure is introduced to observe the MIMO nonlinear system with input–output data, and a new gradient algorithm is proposed to update the unknown parameters of both hidden and output layers. Then, the input dynamic can be obtained with the NN observer, and the inversion-model-based control is designed. Moreover, the ideal inversion control can be obtained based on the reference signal, and the inverse ILC is designed. The stability of the NN observer and the convergence of the inverse-model-based control are analyzed. Finally, a SCARA manipulator MIMO model is simulated to illustrate the correctness of the proposed methods.

MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration

The Municipal solid wastes incineration (MSWI) process with complex mechanism and strong nonlinearity is an important part of resource cycle. It is a challenge to design its multivariable controlled object model and control strategy. To solve this problem, a multi-input multi-output (MIMO) data-driven model and a multi-loop PID controller are proposed in this paper. Firstly, a feature selection method based on Pearson correlation coefficient (PCC) and expert knowledge is used to analyze the relationship between the manipulated variable and the controlled variable of MSWI process. Secondly, a MIMO Takagi–Sugeno fuzzy neural network (TSFNN) based on multi-task learning (MTL) is designed to construct the multivariable controlled object model. Thirdly, a multi-loop PID controller based on quasi-diagonal recurrent neural network (QDRNN) is constructed, which has self-feedback channel and interconnection channel, and can adjust the control parameters automatically. Next, the stability of control strategy is proved by Lyapunov second method. Finally, the modeling effect and control performance are confirmed on the simulation experiments based on the real MSWI process data.

Fact-condition statements and super relation extraction for geothermic knowledge graphs construction

Researchers utilize information from the geoscience literature to deduce the regional or global geological evolution. Traditionally this process has relied on the labor of researchers. As the number of papers continues to increase, acquiring domain-specific knowledge becomes a heavy burden. Knowledge Graph (KG) is proposed as a new knowledge representation technology to change this situation. However, the super relation is not considered in the previous KG, which bridges the geological phenomenon (fact) and its precondition (condition). For instance, in the statement (“the late Archean was a crucial transition period in the history of global geodynamics”), the condition statement (“crucial transition for global geodynamics”) works as the complementary fact statement (“the late Archean was a crucial transition period”), which defines the scale of crucial transition accurately in the late Archean. In this study, fact-condition statement extraction is introduced to construct a geological knowledge graph. A rule-based multi-input multi-output model (R-MIMO) is proposed for information extraction. In the R-MIMO, fact-condition statements and their super relation are considered and extracted for the first time. To verify its performances, a GeothCF dataset with 1455 fact tuples and 789 condition tuples is constructed. In experiments, the R-MIMO model achieves the best performance by using BERT as encoder and LSTM-d as decoder, achieving F1 80.24% in tuple extraction and F1 70.03% in tag prediction task. Furthermore, the geothermic KG with super relation is automatically constructed for the first time by trained R-MIMO, which can provide structured data for further geothermic research.

Comfort Assessment in Railway Vehicles by an Optimal Identification of Transfer Function

Purpose: This research proposes a multi-input multi-output (MIMO) model, based on a data-driven approach, to assess passenger vibration comfort on rail vehicles. The MIMO model represents the mathematical relationship between input and output variables that contains modal properties and vibration transmission. An optimization algorithm estimates the frequency response functions at the interface seat-passenger of the trains. Methods: The MIMO model evaluated the frequency response functions along x-, y- and z- axis. Multivariate analysis considered the calculation of the partial coherence functions, the cross-correlation functions, and the Anderson-Darling nonparametric test. Results: This research considered the acceleration measurements on two Trains, one Tramway, and one Underground. This research developed accelerations analyses in the time domain; the transmissibility of accelerations in the frequency domain. The measuring points were on the seat base and the interface seat-passenger of the trains. The frequency response data, obtained experimentally according to x- y- and z- axis, generated a multi-input and multi-output frequency response model. This research is based on experimental investigations and statistical tests. The calculation of partial coherence evaluated the percentage of spectrum output due to a specific (conditional) input. The partial coherences assess the energy contribution of each input to the output. The cross-correlation test showed the phase between input and output accelerations. The Anderson- Darling test showed the provenance of the data sample from a population with a specific distribution. Anderson-Darling's nonparametric test attaches weight to tails. Conclusions: The seats of the trains were exposed to complex vibrations according to the three directions x, y, and z. The three inputs are linear accelerations along the vertical, lateral, and forward direction of the seat base. If the accelerations on the seat base represented the inputs to the MIMO model, the accelerations acquired at the passenger-seat interface represented the output of the MIMO model.

Improved MIMO Modeling and Enhanced Transient Performance of Phase-Locked Loop During Grid Fault

A phase-locked loop (PLL) is commonly used in grid-connected power converter for synchronization. A single input single output (SISO) linear model of PLL with the phase angle of the point of common coupling (PCC) voltage as input and the estimated phase angle as the output is generally used in the PLL analysis and design. However, an undesirable coupling between the magnitude and phase of the input voltage is present when a filtering stage is incorporated before the control loop, which a SISO model cannot capture, and could result in a significant disturbance in the PLL estimated phase during grid faults. This paper proposes a generalized multi-input multi-output (MIMO) linear model that captures the complete PLL dynamics during symmetrical faults for a PLL equipped with any prefilter. Furthermore, a compensation method that decouples the magnitude dynamics of the input voltage from the phase dynamics is proposed in this paper. The proposed compensation improves the PLL's phase tracking performance by ensuring that the prefiltered PLL acts only on the PCC voltage phase changes. A vital application of the proposed compensation method for PLL is during a grid fault case, wherein the converters are expected to contribute to the fault current, which requires an accurate measurement of the PCC voltage phase. The compensation method's effectiveness is demonstrated using power hardware in the loop simulation of a hardware voltage source converter interfaced to a distribution system simulated in real-time.

Accelerating input-output model estimation with parallel computing for testing hippocampal memory prostheses in human

BackgroundHippocampal memory prosthesis is defined as a closed-loop biomimetic system that can be used for restoration and enhancement of memory functions impaired in diseases or injuries. To build such a prosthesis, we have developed two types of input-output models, i.e., a multi-input multi-output (MIMO) model for predicting output spike trains based on input spikes, and a double-layer multi-resolution memory decoding (MD) model for classifying spatio-temporal patterns of spikes into memory categories. Both models can achieve high prediction accuracy using human hippocampal spikes data and can be used to derive electrical stimulation patterns to test the hippocampal memory prosthesis. MethodsHowever, testing hippocampal memory prostheses in human epilepsy patients with such models has to be performed within a much shorter time window (48–72 h) due to clinical limitations. To solve this problem, we have developed parallelization strategies to decompose the overall model estimation task into multiple independent sub-tasks involving different outputs and cross-validation folds. These sub-tasks are then accomplished in parallel on different computer nodes to reduce model estimation time. ResultsImplementing both parallel schemes with a high-performance computer cluster, we successfully reduced the computing time of model estimations from hundreds of hours to tens of hours. Comparison with existing methodWe have tested the two parallel computing schemes for both MIMO and MD models with data collected from 11 human subjects. The performances of the parallel schemes are compared with the performance of the non-parallel scheme. ConclusionSuch strategies allow us to complete the modeling procedure within the required time frame to further test input-output model-driven electrical stimulations for the hippocampal memory prosthesis. It has important implications to test the model-based DBS intraoperatively and developing clinically viable hippocampal memory prostheses.

Nelinearna strategija kontrole pametnih tankih ploča sa kubnom nelinearnostima primenom tehnike aproksimacije funkcijama (TAF)

The current work introduces three nonlinear control solutions for the regulation of a vibrating nonlinear plate considering model uncertainty. These solutions are feedback linearization control (FBL), virtual velocity error-based control (VVEC), and backstepping control (BSC). In the FBL control, a nonlinear control law is designed with linear closed-loop dynamics such that dynamic stability is ensured. Whereas, by the VVEC (or so-called passivity-based approach in the robotics community) the limitations of the feedback linearization are overcome. On the other side, the BSC selects virtual control variables with stabilized intermediate control laws based on Lyapunov theory. Systematic modeling for the target vibrating plate with piezo patches is described. In effect, considering the nonlinear influence makes the resulted mode shapes for the vibrating structure are highly coupled and careful control design is required. Using the Galerkin approach, the partial differential equation for the smart plate is transformed into definite ordinary differential equations; the multi-input multi-output model is established. The aforementioned control strategies are evaluated and investigated in detail. In essence, they are powerful tools for dealing with nonlinear dynamic systems, however, the VVEC could be considered superior in comparison with the FBL control and the BSC since the designed control structure does not include inertia inverse matrix and modal coordinate acceleration that could make computational problems. As a result, simulation experiments were focused on the VVEC strategy, and the latter was implemented on a simply supported thin plate structure with collocated piezo-patches. The results show the validity of the proposed control architecture.

A Potential Issue of Using the MIMO Nyquist Criterion in Impedance-Based Stability Analysis

The impedance-based stability criteria are widely adopted for small-signal stability analysis of power systems. Among them, the determinant-based multi-input multi-output (MIMO) Nyquist criterion is particularly attractive since it simplifies the stability determination process without calculating the eigenvalues of MIMO systems. However, it is found in this letter that the determinant-based Nyquist criterion can potentially result in incorrect stability analysis results with pure inductance models in the MIMO models of the power network. This letter illustrates the issue and its root cause through mathematical derivation and numerical analysis. Then, the issue is demonstrated through an example system, and the corresponding solutions are provided as well.

Identification Method of SUAV in Diving Phase Based on Flight Tests

A mathematical model of the dive phase is an important research content for improving the accuracy of terminal control in the small unmanned aerial vehicle. The acquisition of the diving model poses new challenges, such as the small installation space, ultra-low flying height of small suicide drones, short flight time, strong coupling, less observable measurement, and elastic deformation of the wings during the drone dive phase. Based on the autoregressive moving average method, a multi-input multioutput noise term topology mathematical model is proposed in this paper. Through an improved least squares identification method, the diving model in the flight test is analyzed and verified. The identification results of the diving model obtained by the proposed method are compared with the least squares method dive model. The results indicate that the mathematical model and identification method proposed in this paper can effectively obtain the parameters of the drone dive model.

Effect of Frequency Coupling on Stability Analysis of a Grid-Connected Modular Multilevel Converter System

Due to the internal dynamics of the modular multilevel converter (MMC), the coupling between the positive and negative sequences in impedance, which is defined as frequency coupling, inherently exists in MMC. Ignoring the frequency coupling of the MMC impedance model may lead to inaccurate stability assessment, and thus the multi-input multi-output (MIMO) impedance model has been developed to consider the frequency coupling effect. However, the generalized Nyquist criterion (GNC), which is used for the stability analysis of an MIMO model, is more complicated than the stability analysis method applied on single-input-single-output (SISO) models. Meanwhile, it is not always the case that the SISO model fails in the stability assessment. Therefore, the conditions when the MIMO impedance model needs to be considered in the stability analysis of an MMC system should be analyzed. This paper quantitatively analyzes the effect of frequency coupling on the stability analysis of grid-connected MMC, and clarifies the frequency range and grid conditions that the coupling effect required to be considered in the stability analysis. Based on the quantitative relations between the frequency coupling and the stability analysis of the grid-connected MMC system, a simple and accurate stability analysis method for the grid-connected MMC system is proposed, where the MIMO impedance model is applied when the frequency coupling has a significant effect and the SISO impedance model is used if the frequency coupling is insignificant.

Handling Enhancement of Autonomous Emergency Steering for Reduced Road Friction Using Steering and Differential Braking

Steering has more potential than braking to prevent rear-end collisions at higher relative velocities. A path tracking controller based on multi-input multi-output (MIMO) model predictive control (MPC) is proposed to enhance the handling performance of autonomous emergency steering in this paper. A six-state MIMO bicycle model including actuator dynamics of steering and differential braking is used for model prediction. Two control inputs are front wheel steering angle and direct yaw moment. Two model outputs are lateral displacement and heading angle. According to the work load ratios at four wheels, control allocation is used to determine the optimal braking force distribution to prevent tire force saturation. The performance of a single-input single-output (SISO) MPC that uses only steering angle control to track the lateral displacement of the desired path is employed to benchmark the performance of the proposed algorithm. Simulation results show that both SISO MPC and MIMO MPC can track the path on nominal road surface with high road friction coefficient of 0.9. For a road surface with medium road friction coefficient of 0.7, the SISO MPC is unable to track the path and loses directional stability. However, the MIMO MPC can still track the path and demonstrate robust path tracking and handling enhancement against model uncertainty due to reduced road friction.

Modeling and Dual Threshold Algorithm for Diffusion-Based Molecular MIMO Communications.

Molecular communication, as an emerging research direction, has emerged in the field of communication, which usually combined with nanotechnology and bio-related knowledge. In the direction of communication channel research, the most widespread model for a molecular communication channel is the diffusion-based channel, where the information-carrying molecules propagate randomly in the medium based on Brownian motion. Multi-input multi-output (MIMO) technology is often used to improve communication quality in the traditional communication field. Compared with the SISO model, which only has inter-symbol interference (ISI) as the interference source, the interference in MIMO communication model includes ISI as well as inter-link interference (ILI), which emerges when receiver receive other transmitters' molecules. In this paper, MIMO communication models are built, based on diffusion channel, CSK, probabilistic theory, considered with ISI and ILI, to establish the calculation formula of related bit error rate, And the influence of relevant parameters in the model on bit error rate is studied. Then, SISO and SIMO models will be built to compare with MIMO models. Last, self-adaptive dual threshold algorithm is proposed to reduce BER of the 2×2 MIMO system. Simulation results show that the proposed algorithm has better performance on reducing BER than other approaches.

MIMO ILC using complex-kernel regression and application to Precision SEA robots

This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR); (ii) develop Geršgorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot’s operating speed with the use of the MIMO ILC.

Output space transfer based multi-input multi-output Takagi–Sugeno fuzzy modeling for estimation of molten iron quality in blast furnace

The molten iron quality (MIQ) in the blast furnace (BF) needs on-site sampling and laboratory tests, which is very unfavorable to the control. To estimate multiple molten iron quality indicators (MIQIs) such as molten iron temperature (MIT), silicon content ([Si]), phosphorus content ([P]) and sulfur content ([S]) in real-time, some data-driven multi-input multi-output (MIMO) modeling methods have lately been developed. However, those modeling methods ignore the inter-indicator correlation. Moreover, the above modeling methods do not consider the processing strategy for the samples with missing indicators. To conquer the above two problems in the process of modeling, this paper proposed an output space transfer based MIMO Takagi–Sugeno (T–S) fuzzy modeling method for the four MIQIs. Through sparse learning of the correlation matrix, the inter-indicator correlation is specifically modeled. Due to the full use of inter-indicator correlation, the impact of missing indicators on modeling accuracy is greatly reduced. An effective alternative optimization algorithm is also provided for the corresponding intractable optimization problem. The actual data test shows that the proposed method can greatly improve the accuracy of multiple MIQIs estimations.

A Model Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude

The adaptive trajectory and attitude control is essential for the four-dimensional (4D) trajectory operation of civil aircraft in symmetric thrust flight. In this work, an integrated trajectory and attitude control scheme is proposed based on the =multi-input multi-output (MIMO) model free adaptive control (MFAC) method. First, the full-form dynamic linearization technique is adopted to build the equivalent data model of aircraft. Also, the MIMO MFAC scheme with saturation constraint is designed to achieve an accurate tracking control for a given 4D trajectory and attitude. Besides, the performance limitations of aircraft are taken into consideration, and the MIMO MFAC scheme with hard constraints is designed. In addition, to improve the simulation efficiency, a control scheme with mixed constraints, i.e., saturation and hard constraints, is further proposed. It can be seen from the simulation results that the proposed method can perform an integrated control of the aircraft 4D trajectory and attitude without precise modeling, and the control performance is better than that of the model-based control method in terms of flight altitude and yaw angle control. The integrated data-driven control scheme proposed in this paper provides a theoretical solution for the precise operation of aircraft under 4D trajectory.