Multiple-input Multiple-output Control Research Articles

Adaptive sliding-mode delay compensation for real-time hybrid simulations with multiple actuators

Real-time hybrid simulation (RTHS) is a widely applied test method in structural engineering, which is developed from pseudo-dynamic test. Much of the past work has been centered on one-dimensional RTHS using a single hydraulic actuator. When the complexity of the problem demands to increase the number of degrees of freedom to be enforced on the boundary conditions, more than one hydraulic actuator must be used. Multiple-actuator or multi-axial RTHS (maRTHS) requires that more than one hydraulic actuator exerts the required motion on experimental substructures demanding the implementation of multiple-input multiple-output (MIMO) control strategies. A new maRTHS benchmark control problem has been developed, focusing on a frame subjected to seismic load at the base, substantially transforming and intensifying the complexity of the problem. The time delay generated by the dynamic characteristics of the loading system and the transmission process as well as the high coupling between the hydraulic actuators and the nonlinear kinematics escalates the complexity of the actuator control tracking. A sliding mode adaptive delay compensation method suitable for maRTHS is proposed, which utilizes a MIMO sliding mode method to reduce the coupling effects of actuators and the adaptive compensation method to compensate the residual delay. The effectiveness of the method is verified by numerical simulating different working conditions in the Benchmark Problem Platform.

Enhancing Vapor Compression Refrigeration Systems Efficiency via Two-Phase Length and Superheat Evaporator MIMO Control

The present investigation focuses on enhancing the efficiency of a vapor compression refrigeration system (VCRS) by proposing a Multiple Input Multiple Output (MIMO) control strategy based on the evaporator’s two-phase length and superheat temperature. A moving boundary dynamic model for the VCRS is implemented using the Thermosys Matlab Toolbox. The study analyzes the influence of actuation parameters, specifically compressor speed and expansion valve opening, on control parameters, namely two-phase length and superheat temperature. A comprehensive analysis based on the first and second laws of thermodynamics is conducted across a wide range of operating conditions. Simulation results demonstrate that two-phase length can be effectively utilized as a control parameter by selecting operating points that maximize the system efficiency. Additionally, the study reveals that extending the evaporator’s two-phase length to 80–90% of its limit increases system efficiency, enabling a reduction in compressor speed while maintaining the cooling capacity.

Evaluation of data-driven NARX model based compensation for multi-axial real-time hybrid simulation benchmark study

Actuator control takes a pivotal role in achieving stability and accuracy, particularly in the context of multi-axial real-time hybrid simulation (maRTHS). In maRTHS, multiple hydraulic actuators are necessitated to apply precise motions to experimental substructures thus necessitating the application of multiple-input multiple-output (MIMO)control strategies. This study evaluates the data-driven nonlinear autoregressive with external input (NARX) based compensation for the servo-hydraulic dynamics within the maRTHS benchmark model. Different from previous study, nonlinear terms are incorporated into the NARX model. Online least square and ridge regression techniques are utilized to estimate the model coefficients to achieve optimal compensation. The influence of various model order and window length is assessed for the NARX model-based compensation. The findings of this research demonstrate that NARX-based compensation has significant potential not only in facilitating precise actuator control for maRTHS but also in enabling robust control in the presence of unknown uncertainties inherent to the servo-hydraulic system.

Multiple-input multiple-output Radial Basis Function Neural Network modeling and model predictive control of a biomass boiler

This study presents a model predictive control of a 4 × 3 Multiple-Input Multiple-Output (MIMO) biomass control system that uses radial basis function (RBF) as an activation function to enhance the control performance. The biomass boiler model is developed from the system identification box in MATLAB by collecting measured input and output data from the 31.5 MW Wonji sugar mill and analyzing the data using black box modeling approach. The major goal of this study is to improve the MIMO control performance of a biomass boiler by utilizing Radial Basis Function Neural Network (RBFNN) to increase model accuracy which ultimately enhances the overall control system. Biomass boilers have four inputs and three outputs. The inputs are airflow 1, airflow 2, water flow and stocker speed (the speed of the motor which feeds bagasse or sugarcane pulp) and outputs are temperature, pressure and drum level. The RBFNN model was programmed using MATLAB R2021a software with system identification toolbox. Shorter settling times of 0.286 s, 1.873 s, and 0.637 s, as well as tolerable overshoots of 0.505%, 1.5%, and 15.698% for temperature, pressure, and level respectively, were achieved with the suggested model predictive controller using RBFNN model. However, when utilizing the identical controller and state space model, the settling times are 2.318 s, 5.461 s, and 6.147 s, with overshoots of 4.737%, 8.152%, and 38.194% for the three variables respectively. The suggested method was compared to the model predictive control (MPC) biomass boiler state space model, which can be considered as a model without RBFNN. The simulation results indicate that the RBFNN-based MPC performed better than the state space model-based MPC. This is because, the state space model is always constant, in contrast to the neural network's active monitoring of boiler dynamics.

Model based optimal magnetic feedback control of multiple resistive wall modes with discrete coil and sensor arrays

A model-based optimal control method for multiple resistive wall mode (RWM) feedback stabilization has been developed and tested in plasma experiments at the EXTRAP T2R reversed field pinch (RFP) device. The controller is designed to target issues that arise in connection with RWM magnetic feedback stabilization systems based on discrete control coil and sensor arrays in tokamak and reversed field pinch devices. Multi-mode control capabilities in these systems is limited due to coupling of modes induced by the control system. The coupling originates from the generation of side-band control field harmonics and from aliasing of multiple harmonics in the sensor measurements. These couplings naturally leads to a multiple-input, multiple-output (MIMO) control problem. A model based state space controller has been designed based on a relatively simple physics model of the RWM plasma response. The physics model, which is applicable for the high aspect ratio RFP, is the basis for Fourier decoupling of the MIMO control problem into a set of single-input, multiple-output (SIMO) systems using the discrete Fourier transform (DFT). The linear, time-invariant physics model allows for the design of a state space model with states representing physical quantities; in this case the Fourier harmonics of the radial field at the resistive wall. Since the states cannot be directly measured, a Kalman filter is used for estimation of the states from the aliased sensor array measurements. A linear–quadratic (LQ) optimal state controller has been implemented. Design parameters, such as the LQ control cost function state weights and the Kalman filter input error covariances have been used to optimize the control operation in various ways. The controller has enhanced multi-mode control capabilities compared to earlier designs. For example it allows the prioritizing of suppression of one of the multiple magnetic field Fourier harmonics produced by a given control current DFT component. The controller has been tested in plasma experiments at EXTRAP T2R device, utilizing a newly installed extended sensor array, and the enhanced capabilities for multiple RWM feedback stabilization has been demonstrated.

A single‐input single‐output versus multiple‐input multiple‐output feedback controllers for the Korteweg‐de Vries‐Kuramoto‐Sivashinsky equation

SummaryThe dynamics and control of the Korteweg‐de Vries‐Kuramoto‐Sivashinsky (KdVKS) equation subject to periodic boundary conditions are considered. First, the dynamics of the KdVKS equation is analyzed using the Fourier Galerkin reduced‐order method. This reduced‐order method is used to generate a finite‐dimensional approximation of the KdVKS equation. Then, a multiple‐input multiple‐output (MIMO) and a single‐input single‐output (SISO) controllers are designed to stabilize the finite‐dimensional approximation of the linearized KdVKS equation. We show that both the MIMO and SISO controllers which are designed to stabilize the linearized system will locally stabilize the infinite‐dimensional KdVKS equation. The Frechét differentiability of the semigroup generated by the closed‐loop system is crucial to prove the local stability results of the controllers. Furthermore, we show that the condition on the number of output measurements that must equal to the total number of unstable and critically stable eigenvalues of the linearized KdVKS equation is not necessary for the local exponential stability of the KdVKS equation. Finally, numerical simulations based on the two designed controllers are presented and compared to illustrate the developed theory.

Model-free MIMO control tuning of a chiller process using reinforcement learning

The performance of HVAC equipment, including chillers, is continuing to be pushed to theoretical limits, which impacts the necessity for advanced control logic to operate them efficiently and robustly. At the same time, their architectures are becoming more complex; many systems have multiple compressors, expansion devices, evaporators, circuits, or other elements that challenge control design and resulting performance. In order to maintain respectful controlled speed of response, stability, and robustness, controllers are becoming more complex, including the move from thermostatic control, to proportional integrator (PI), and to multiple-input multiple-output (MIMO) controllers. Model-based control design works well for their synthesis, while having accurate models for numerous product variants is unrealistic, often leading to very conservative designs. To address this, we propose and demonstrate a learning-based control tuner that supports the design of MIMO decoupling PI controllers using online information to adapt controller coefficients from an initial guess during commissioning or operation. The approach is tested on a physics-based model of a water-cooled screw chiller. The method is able to find a controller that performs better than a nominal controller (two single PI controllers) in terms of decreasing deviations from the operating point during disturbances while still following reference changes.

A Novel MIMO Control for Interleaved Buck Converters in EV DC Fast Charging Applications

This brief proposes a new multiple input multiple output (MIMO) control for off-board electric vehicle (EV) dc fast chargers. The proposed feedback matrix design avoids multiple tuning of controllers in multiple and interconnected loops while improving the performance of interleaved dc buck converters over classical PI/PID controls. The innovative features of the presented strategy are the reference current monotonic tracking from any initial state of charge with an arbitrarily fast settling time and the fast compensation of both load variations and imbalances among the legs. Numerical results validate the performance improvements of the proposed discrete-time MIMO algorithm for interleaved buck converters over classical PI/PID controls. Full-scale hardware-in-the-loop (HIL) and scaled-down prototype experimental results prove the feasibility and effectiveness of the proposal.

Process model structures for transverse direction gauge control of tentered polymeric film

AbstractOne of the most successful applications of multiple input multiple output control is the control of thickness in sheet forming processes including paper making and plastic film casting. While there are many similarities between film manufacture and other sheet forming processes, the physics differs from process to process. The choice of both process model and optimal control algorithm must reflect these differences. Here we consider how the basic physics of film manufacture a reflected in two classes of process models: phenomenological (interaction) models used in conventional gauge control algorithms and mechanistic models based on engineering science. Mechanistic models are more mathematically robust than purely statistical phenomenological models for some aspects of gauge control and may prove advantageous for other sheet forming applications.

Robust model-based switching MIMO air handling control of turbocharged lean-burn SI natural gas variable speed engines

This paper demonstrates a multiple-input multiple-output (MIMO) controller design framework and a controller switching algorithm for MIMO controllers in their state-space form, which together achieve robust, efficient control of turbocharged lean-burn engines over a wide operating space. The controller design framework requires a linearized plant model, and uses the [Formula: see text]-synthesis and DK-iteration algorithms while considering state and output uncertainties and actuator bandwidths to synthesize a robust [Formula: see text] controller. A controller switching methodology using slow-fast controller decomposition and also incorporating hysteresis at switching points is utilized to smoothly transfer control authority between several MIMO controllers. The approach is applied to a high-fidelity truth-reference GT-Power engine model for a lean-burn natural gas-fueled engine to evaluate the closed-loop controller performance. The multi-tracking control problem targets engine speed, differential pressure across throttle as well as air-to-fuel ratio to achieve satisfactory engine performance and emissions without compressor surge. The engine response obtained using the robust MIMO controller is compared with that obtained using a state-of-the-art benchmark controller to evaluate the additional benefits of the MIMO controller.

System Identification for the design of behavioral controllers in crowd evacuations

Behavioral modification using active instructions is a promising interventional method to optimize crowd evacuations. However, existing research efforts have been more focused on eliciting general principles of optimal behavior than providing explicit mechanisms to dynamically induce the desired behaviors, which could be claimed as a significant knowledge gap in crowd evacuation optimization. In particular, we propose using dynamic distance-keeping instructions to regulate pedestrian flows and improve safety and evacuation time. We investigate the viability of using Model Predictive Control (MPC) techniques to develop a behavioral controller that obtains the optimal distance-keeping instructions to modulate the pedestrian density at bottlenecks. System Identification is proposed as a general methodology to model crowd dynamics and build prediction models. Thus, for a testbed evacuation scenario and input–output data generated from designed microscopic simulations, we estimate a linear AutoRegressive eXogenous model (ARX), which is used as the prediction model in the MPC controller. A microscopic simulation framework is used to validate the proposal that embeds the designed MPC controller, tuned and refined in closed-loop using the ARX model as the Plant model. As a significant contribution, the proposed combination of MPC control and System Identification to model crowd dynamics appears ideally suited to develop realistic and practical control systems for controlling crowd motion. The flexibility of MPC control technology to impose constraints on control variables and include different disturbance models in the prediction model has confirmed its suitability in the design of behavioral controllers in crowd evacuations. We found that an adequate selection of output disturbance models in the predictor is critical in the type of responses given by the controller. Interestingly, it is expected that this proposal can be extended to different evacuation scenarios, control variables, control systems, and multiple-input multiple-output control structures.

Enhanced Observer Control of a Cubic Stewart Platform With Image Sensor Measurement

A cubic Stewart platform with an image sensor meausring is proposed for a line-of-sight (LOS) pointing and stabilization. The charge couple device (CCD) as a image sensor mounted on the Stewart platform can provide the LOS error only in the azimuth and elevation directions, so a decoupled control strategy through the transformation of the Jacobian matrix is introduced to transform a Multiple-Input Multiple-Output (MIMO) control into Single-Input Single-Output (SISO) control. Due to the low bandwidth of the Stewart platform and the finite sampling rate of the CCD, the closed-loop performance is greatly restricted. An optimal observer control based on Youla parameterization is proposed to improve the tracking performance in the CCD-based Stewart platform control system. This improved controller relies on the LOS error combined with the controller output to produce high gain, plugging into the tradditional closed-loop structure, which results in minimizing the error attenuation function by designing an appropriate Q-filter. Both the simulations and experiments are carried out to prove the effectiveness of the proposed observer compared with the classical feedback controller.

Homogeneity based finite/fixed‐time observers for linear MIMO systems

AbstractThe article is devoted to the problem of finite‐time and fixed‐time observation of linear multiple input multiple output control systems. The proposed dynamic observers do not require system transformation to a canonical form and guarantee convergence of the observation error to zero in a finite or in a fixed time. It is shown that the observers are robust (in input‐to‐state sense) against input disturbances and measurement noises. The results are supported with simulation examples.

Multidomain Narrowband Interference Intelligent Suppression Method Based on Cognitive Radio and MIMO in UAV Data Link.

In this paper, a kind of antinarrowband interference intelligent method based on cognitive MIMO (multiple-input multiple-output) airspace, time domain, frequency domain, and code domain is proposed. This method combines perceptual technology, spread spectrum code, MIMO space-time coding, and so on. Through coordinating space, time, frequency, and code element diversity, the narrowband interference in the UAV (unmanned aerial vehicle) monitoring and control link is effectively countered. The input and output model of MIMO monitoring and control link of UAV with natural interference are described. Then, the realization principle of the multidomain antinarrowband interference method based on CR-MIMO is presented, and the corresponding models of receiving and transmitting are also given. Finally, the anti-interference performance of the proposed method is analyzed theoretically and validated through simulation experiments, and the effectiveness of the proposed method is verified.

Modeling Transient Control of a Turbogenerator on a Drive Cycle

<div class="section abstract"><div class="htmlview paragraph">GTDI engines are becoming more efficient, whether individually or part of a HEV (Hybrid Electric Vehicle) powertrain. For the latter, this efficiency manifests itself as increase in zero emissions vehicle mileage. An ideal device for energy recovery is a turbogenerator (TG), and, when placed downstream the conventional turbine, it has minimal impact on catalyst light-off and can be used as a bolt-on aftermarket device. A Ricardo WAVE model of a representative GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated using steady state mapping data. This was integrated into a co-simulation environment with a SISO (Single-Input, Single-Output) dynamic controller developed in SIMULINK for the actuator control (with BMEP, manifold air pressure and TG pressure ratio as the controlled variables). Transient verification with WAVE-RT was conducted on WLTP and NEDC drive cycles, estimating dynamic energy recovery and fuel consumption improvement. Hints are given for a more advanced MIMO (Multiple-Input, Multiple-Output) control system architecture and calibration.</div></div>

Dynamic measurement with in-cycle process excitation of HCCI combustion: The key to handle complexity of data-driven control?

Homogeneous Charge Compression Ignition (HCCI) is a low temperature combustion technique with a high potential for reducing emissions while simultaneously improving fuel consumption. However, the high sensitivity to changing boundary conditions and low combustion stability at the edges of the operating range has lead to implementation challenges. Additionally, cyclic coupling through internal exhaust gas recirculation causes cyclic variations of the process, resulting in incomplete combustion, or even misfiring. Thus, consecutive cycles must be decoupled to increase the process stability. To achieve an accurate description of the coupling effects on a cycle-to-cycle and an inner-cyclic timescale, a novel measurement methodology is presented to generate data with a high variance. For this purpose, an active process excitation is performed to capture all relevant interactions between operating and feedback variables to enable modeling of the coupling effects on both timescales. To demonstrate the potential of the methodology, the generated data is used to design multiple input, multiple output (MIMO) models for both cyclic and inner-cyclic timescales. Artificial neural networks are then utilized to address the highly nonlinear process by taking advantage of the large amount of training data. Inverse process models are then used to implement a pure cycle-to-cycle and a multiscale MIMO closed-loop controller. Compared to state-of-the-art rule-based control approaches, the process stability and its thermodynamic efficiency are significantly improved. For the multiscale MIMO controller, a reduction of the standard deviation of the indicated mean effective pressure and the combustion phasing of more than 65% is achieved. In particular, the additional inner-cyclic feedback loop achieves a remarkable reduction of the standard deviation of approximately 35% and a 1.2% higher indicated efficiency compared to the cycle-to-cycle MIMO controller. The dynamic measurement with active in-cycle process excitation has proven to be an enabler for data-driven MIMO control of HCCI on multiple timescales.

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Robust Switching MIMO Control of Turbocharged Lean-Burn Natural Gas Engines

This paper illustrates a multiple-input multiple-output (MIMO) controller design framework and a controller switching algorithm for MIMO controllers to achieve robust coordinated control of turbocharged lean-burn engines. The control problem tracks engine speed, diferential pressure across the throttle valve and the air-to-fuel ratio simultaneously to achieve satisfactory engine performance while avoiding compressor surge. The controller design approach is applied to a high-fidelity GT-Power engine model for a lean-burn natural gas engine to assess the closed-loop controller performance. The engine performance with the robust MIMO controller is compared with that using a benchmark production controller to evaluate the additional benefits of the MIMO controller. In a large step increase in desired engine speed and corresponding engine torque, it is observed that the MIMO controller leads to a slightly faster engine speed response. Furthermore, during transience, the minimum air-to-fuel ratio is 20% higher and the peak in differential pressure across the throttle is reduced by 59% when using the MIMO controller.

Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot

Differential drive wheeled mobile robot (DDWMR) is one example of a robot with a constrained movement, Multiple Input Multiple Output (MIMO), and nonlinear system. Designing a low resource position and heading controller using linear MIMO methods such as LQR became a problem because of the linearization of robot dynamics at zero value. One of the solutions is to design a MIMO controller using a Single Input Single Output (SISO) controller. This work design a controller using PID for DDWMR Jetbot and selects the best feedback gain using different scenarios. The designed controller manipulates both motors by using calculated control signal to achieve a complex task such as path tracking with robot position in x-Axis, y-Axis, and heading angle as the feedback. The priority between position and heading angle can be adjusted by changing three feedback gains. The controller was tested, and the best gain was selected using Integral Absolute Error (IAE) metrics in a path tracking task with four different path shapes. The proposed methods can track square, circle, and two types of infinity shape paths, with the less well-formed shape being the four edges square path.

Risk-theoretic optimal design of output-feedback controllers via iterative convex relaxations

This work presents a framework to address the MIMO (multiple-input multiple-output) control design problem under model uncertainty. The plant is considered to be LTI (linear and time-invariant) and that it is parametrized by a random variable whose distribution may be unknown but can be sampled from. The objective is to design a dynamic LTI MIMO controller optimizing the closed-loop H2-performance. To account for the uncertainty in the plant, we employ measures of risk on the performance and discuss how coherent measures of risk are suitable to address this problem. In particular, we introduce a measure called Conditional Value-at-Risk (CVaR) and analyze its advantages and disadvantages regarding other traditional measures of risk. A simulation study complements the discussion.