Closed-loop Control Applications Research Articles

Indirect Field Oriented Control of Induction Motor

The various control strategies for the control of the inverter-fed induction motor have provided good steady-state but poor dynamic response. In the last decades, many control techniques have been developed providing good performance. Field-oriented control is one of the most significant control methods for high performance AC electric machines and drives. In particular, for induction machines, indirect field oriented control is a simple and highly reliable scheme which has essentially become an industry standard. With the advent of the vector control methods, an induction motor can be operated like a separately excited dc motor for high performance applications. The speed control is observed in closed-loop control applications. The speed regulation is achieved with a PI controller which converts the command speed into the command torque and therefore the speed of the drive. In this paper PI controller base indirect field orientation control of 3-phase 415V, 50Hz Induction motor using cascaded multilevel inverter is proposed in MATLAB/SIMMLINK 7.10.0 (R2010a).

Research on Active Repetitive Control for Tracking Lissajous Scan Trajectories with Voice Coil Motors Actuated Fast Steering Mirror

The performance of laser beams in tracking Lissajous scan trajectories is severely limited by beam jitter. To enhance the performance of fast steering mirror (FSM) control in tracking Lissajous scan trajectories, this paper proposed a fractional order active disturbance rejection controller (FOADRC) and verified its effectiveness in improving system scanning tracking accuracy. A dynamic mathematical model of a fast steering mirror was studied, and the design of parameters for the control mode of the closed-loop system was determined. A reduced-order linear active disturbance rejection controller suitable for FSM systems was designed, and the corresponding fractional-order proportional differentiation (FOPD) controller was determined according to the mathematical model. The use of the designed controller enabled high-performance tracking of high-frequency Lissajous scanning curves (X-axis 500 Hz, Y-axis 350 Hz) and met the need for high-frequency repetitive scanning. The controller has the characteristics of simple implementation and low computational complexity and is suitable for closed-loop control applications in engineering.

A linear programming formulation for eco-driving over road slopes

This research proposes a linear program (LP) formulation for energy-minimizing vehicle velocity optimization in hilly terrain and demonstrates comparable performance to its traditional formulation as a dynamic program (DP). The LP captures the kinetic energy dynamics and road grade preview exactly and employs a novel piecewise linear approximation to travel time. The LP runs faster than its DP counterpart and can run in real-time and in closed-loop control applications. To demonstrate its effectiveness, the LP velocity planner is integrated with a receding horizon motion planner and simulated in a Class 8 tractor–trailer application over a real road slope profile, with and without traffic. In these closed-loop simulations, energy savings are observed relative to an explicit cruise control law when maintaining constant speed requires excessive braking.

Cascade multi-resonant disturbance observer design. Application to a distillation column

In this work, we present a novel approach for input disturbance estimation design and implementation for dynamical processes under the influence of unknown disturbances that present a clear periodical behaviour of a known frequency. Both the design and the implementation are focused on simplicity. The observer consists of a set of transfer functions fed by the process manipulated variable and the sensor measurement that are implemented through a mixture of cascade and series connections. For the synthesis of the disturbance observer, we just need an input–output model of the process and one tuning parameter in each of the transfer functions. We present simple rules for the design considering the trade-off between the transient time needed to estimate changes in the behaviour of the disturbance and the robustness against measurement noise effects. We show the benefits in using this disturbance estimation as a feed-forward signal in both open loop and closed loop control applications, and we quantify the robustness modification when used together with a closed loop controller. The transfer functions of the observer include both a low pass filter and a set of resonant terms. We quantify the effect of the different terms and their parameter tuning. Our implementation method uses standard tools available in industrial control systems and we have applied it to a real distillation column under ambient temperature disturbances. The main contribution of this work is the simplicity of the design and implementation of the disturbance observer, making it suitable for the process industry and to be managed by non-experts in control systems. Another contribution is the a priori design based in intuitive engineering performance indices.

Layer-to-layer closed-loop feedback control application for inter-layer temperature stabilization in laser powder bed fusion

In laser powder bed fusion (LPBF), part quality and process conditions are highly dependent on the interlayer temperature (ILT) of the exposure surface of printed parts. State-of-the-art closed-loop control applications do not stabilize ILT, which has a decisive influence on the resulting part properties. This paper presents a closed-loop control strategy for LPBF that stabilizes the ILT. The thermal balance is defined with respect to exposure area change in overhanging parts and a control strategy is proposed to maintain the thermal balance by actuating energy input. The proposed control strategy uses an off-axis thermal camera for interlayer temperature acquisition to update the laser power layer to layer. An online optimization-based controller that leverages the repetitive nature of the LPBF process is implemented for the task. The developed controller is validated by experiments with different overhanging geometries. Successful ILT stabilization for various overhanging angles shows the robustness of the controller for an extended number of layers. However, modulation of laser power alone is shown to be insufficient to maintain the ILT within the stable processing window due to excessive heat accumulation in the long term. Nevertheless, the results of this study demonstrate the feasibility of ILT stabilization via of closed-loop control in LPBF and the potential for significant improvements in stabilizing the properties of the printed parts.

SoLiCiT: Synchronous Listen, Code, and Transmit Protocol for Wireless Control Applications

Industrial control applications are deployed in environments characterized by high signal attenuation and severe multipath fading, which is a potential cause for significant packet losses. This is in conflict with achieving low latency and highly reliable wireless communication, a requirement of closed-loop control applications. We propose SoLiCiT, a wireless networking protocol that employs concurrent transmissions with specifically designed erasure coding to meet these demands without the need for retransmissions. Synchronous transmissions help to realize a simple protocol design while using helper nodes to transmit redundancy allows for lost packets to be recovered within fixed and tight latency bounds. We provide a range of coding solutions that can be tailored to the application and also present a case study with a battery storage management application in which battery cells report their status to a central management unit. Using a realistic ray tracing model of the actual battery storage container, we evaluate the radio environment and show the efficacy of SoLiCiT compared to benchmarking schemes, such as a low-power wireless bus.

Control Systems with Tomographic Sensors-A Review.

Industrial process tomography offers two key advantages over conventional sensing systems. Firstly, process tomography systems provide information about 2D or 3D distributions of the variables of interest. Secondly, tomography looks inside the processes without penetrating them physically, i.e., sensing is possible despite harsh process conditions, and the operation of the process is not disturbed by intrusive sensors. These advantages open new perspectives for the field of process control, and the potential of closed-loop control applications is one of the main driving forces behind the development of industrial tomography. Despite these advantages and decades of development, closed-loop control applications of tomography are still not really common. This article provides an overview of the current state-of-the-art in the field of control systems with tomographic sensors. An attempt is made to classify the different control approaches, critically assess their strengths and weak points, and outline which directions may lead to increased future utilization of industrial tomography in the closed-loop feedback control.

Seismic Response Prediction of Open- and Closed-Loop Structural Control Systems Using a Multi-State-Dependent Parameter Estimation Approach

In this paper, a new modified multi-state-dependent parameter (MSDP) model estimation method is proposed with the aim of application for seismic response prediction of open- and closed-loop structural control systems. In the first phase, the recursive Kalman filtering is improved and an improved recursive fixed interval-smoothing algorithm is then proposed in the second phase. The numerical studies are conducted on a seismic-excited 3-story benchmark structure in three cases of open-loop structural system, open-loop structural control with tuned mass damper (TMD) as a passive device, and closed-loop structural control with an active tendon system (ATS). Using the proposed MSDP models, the time-histories of acceleration of the floors are predicted during earthquakes and the validation of the proposed models is evaluated with those given by the finite element method (FEM) of OpenSees software. Using three evaluation criteria, the superiority of the modified MSDP over the MSDP is found. The simulation results show that the proposed models are capable of following the seismic responses of the structure well and estimating them with high accuracy in further time steps. With high accuracy and lower computational cost, the surrogate models can be easily utilized in optimal design processes of the passive device for the open-loop structural control and the active control algorithm for the closed-loop structural control and structural health monitoring (SHM) applications.

Model-free adaptive filter to mitigate actuator wear

The present article introduces an adaptive filter of statistical basis developed for closed-loop control applications, whose goal is to reduce actuator wear while ensuring a similar control performance regarding the original closed-loop system. The main idea is to avoid the rapid change of the filtered signal when the system output has a derivative not statistically significant regarding the expected measurement noise. The adaptation law of the time constant of the filter is model-free, and the only required information is the variance of the additive noise that the measurements are subjected to. The performance of the proposed adaptive method is illustrated through a combined numerical and experimental study, in addition to its application in an operational oil plant. The results indicate that our formulation holds promise for extending the life of actuators and is easy to implement in most programmable logic controllers.

A Model-based Method of Monitoring Combustion Pressure Measurement Chains for Closed-loop Combustion Control Applications

Significant research is ongoing into the use of carbon neutral fuels for combustion engines. In order to fully exploit these new fuel technologies, alongside existing carbon based fuel types, the engine requires a sophisticated level of control for the combustion event based upon a cyclic feedback loop (i.e., a preceding cycle provides the optimization data for the following cycle). This then allows the engine and combustion controller to have the capability to respond and optimise, normal and abnormal combustion phenomena, based on information gained during run-time (as opposed to inferred or indirectly derived parameters). An essential element is the in-cylinder pressure sensor and its measuring chain, that supplies the raw in-cylinder pressure curve for real time analysis within the control system. This paper describes a model-based method for monitoring such a dynamic system, in which the condition of the physical system is represented in a dynamic model consisting of the engine itself, plus the measuring chain, as coupled models. The intent is to mimic the condition of the complete physical system in the model, allowing comparison of model and physical systems to observe residuals and identify fault conditions with all, or part of the measuring chain components.

From coarse wall measurements to turbulent velocity fields through deep learning

This work evaluates the applicability of super-resolution generative adversarial networks (SRGANs) as a methodology for the reconstruction of turbulent-flow quantities from coarse wall measurements. The method is applied both for the resolution enhancement of wall fields and the estimation of wall-parallel velocity fields from coarse wall measurements of shear stress and pressure. The analysis has been carried out with a database of a turbulent open-channel flow with a friction Reynolds number Reτ=180 generated through direct numerical simulation. Coarse wall measurements have been generated with three different downsampling factors fd=[4, 8, 16] from the high-resolution fields, and wall-parallel velocity fields have been reconstructed at four inner-scaled wall-normal distances y+=[15, 30, 50, 100]. We first show that SRGAN can be used to enhance the resolution of coarse wall measurements. If compared with the direct reconstruction from the sole coarse wall measurements, SRGAN provides better instantaneous reconstructions, in terms of both mean-squared error and spectral-fractional error. Even though lower resolutions in the input wall data make it more challenging to achieve highly accurate predictions, the proposed SRGAN-based network yields very good reconstruction results. Furthermore, it is shown that even for the most challenging cases, the SRGAN is capable of capturing the large-scale structures that populate the flow. The proposed novel methodology has a great potential for closed-loop control applications relying on non-intrusive sensing.

Three-Axis Inductive Displacement Sensor Using Phase-Sensitive Digital Signal Processing for Industrial Magnetic Bearing Applications

Non-contact rotor position sensors are an essential part of control systems in magnetically suspended high-speed drives. In typical active magnetic bearing (AMB) levitated high-speed machine applications, the displacement of the rotor in the mechanical air gap is measured with commercially available eddy current-based displacement sensors. The aim of this paper is to propose a robust and compact three-dimensional position sensor that can measure the rotor displacement of an AMB system in both the radial and axial directions. The paper presents a sensor design utilizing only a single unified sensor stator and a single shared rotor mounted target piece surface to achieve the measurement of all three measurement axes. The sensor uses an inductive measuring principle to sense the air gap between the sensor stator and rotor piece, which makes it robust to surface variations of the sensing target. Combined with the sensor design, a state of the art fully digital signal processing chain utilizing synchronous in-phase and quadrature demodulation is presented. The feasibility of the proposed sensor design is verified in a closed-loop control application utilizing a 350-kW, 15,000-r/min high-speed industrial induction machine with magnetic bearing suspension. The inductive sensor provides an alternative solution to commercial eddy current displacement sensors. It meets the application requirements and has a robust construction utilizing conventional electrical steel lamination stacks and copper winding.

A bi-bandwidth extended state observer for a system with measurement noise and its application to aircraft with abrupt structural damage

In this paper, we address the problem of disturbance estimation for a class of linear systems with both fast time-varying uncertainty and measurement noise. A novel bi-bandwidth extended state observer (BESO) is proposed to achieve fast disturbance tracking and satisfactory noise suppression. The direction information of the estimation errors is used through a directional switching operator (DSO) to dynamically tune the bandwidth of the BESO. Sufficient conditions for the stability of the BESO are given, and the error dynamics are proven to be bounded and to converge in finite time. A comprehensive comparison of the BESO and three other representative approaches is presented, which shows that the BESO achieves a performance improvement of greater than 13%. A closed-loop control application for a structurally damaged aircraft is also provided to demonstrate the effectiveness and efficiency of the BESO.

Layer-to-Layer Stability of Linear Layerwise Spatially Varying Systems: Applications in Fused Deposition Modeling

Closed-loop control applications for additive manufacturing (AM) technologies introduce unique challenges in control-oriented modeling and controller development. There have been developments in current literature to model the temporal and spatial dynamics of AM processes. The temporal dynamics of AM processes are often modeled using tools from fluid dynamics and mechanics to represent the material deposition and the motion of the deposition system. Spatial dynamics are often modeled by representing the spreading dynamics of the deposited material in the layerwise spatial domain. An important challenge with the spatial dynamics of AM processes is to understand the performance of the layer-to-layer (L2L) spatial dynamics under spatial disturbances in the system. To this end, this article presents a linear layerwise spatially varying (LLSV) systems modeling framework to represent the L2L spatial height evolution of a generic AM process. The proposed unifying modeling framework can easily represent various AM processes. An L2L stability measure is provided as a performance measure for the spatial dynamic state of an AM process. L2L stability is a novel analysis tool to understand and analyze the spatial characteristics of AM processes. Fundamental L2L stability definitions for a nominal system model and a system model with known Gaussian spatial noise are presented. Theoretical robustness bounds for L2L stability are experimentally compared to a fused deposition modeling (FDM) process. The results show that the theoretical robustness bounds given in this work provide an important foundation for developing novel closed-loop L2L spatial control applications.

Permeate Flux Control in SMBR System by Using Neural Network Internal Model Control

This paper presents a design of a data-driven-based neural network internal model control for a submerged membrane bioreactor (SMBR) with hollow fiber for microfiltration. The experiment design is performed for measurement of physical parameters from an actuator input (permeate pump voltage), which gives the information (outputs) of permeate flux and trans-membrane pressure (TMP). The palm oil mill effluent is used as an influent preparation to depict fouling phenomenon in the membrane filtration process. From the experiment, membrane fouling potential is observed from flux decline pattern, with a rapid increment of TMP (above 200 mbar). Membrane fouling is a complex process and the available models in literature are not designed for control system (filtration performance). Therefore, this work proposes an aeration fouling control strategy to measure the filtration performance. The artificial neural networks (Feed-Forward Neural Network—FFNN, Radial Basis Function Neural Network—RBFNN and Nonlinear Autoregressive Exogenous Neural Network—NARXNN) are used to model dynamic behaviour of flux and TMP. In this case, only flux is used in closed loop control application, whereby the TMP effect is used for monitoring. The simulation results show that reliable prediction of membrane fouling potential is obtained. It can be observed that almost all the artificial neural network (ANN) models have similar shape with the actual data set, with the highest accuracy of more than 90% for both RBFNN and NARXN. The RBFNN is preferable due to simple structure of the network. In the control system, the RBFNN IMC depicts the highest closed loop performance with only 3.75 s (settling time) for setpoint changes when compared with other controllers. In addition, it showed fast performance in disturbance rejection with less overshoot. In conclusion, among the different neural network tested configurations the one based on radial basis function provides the best performance with respect to prediction as well as control performance.

Expanding biological control to bioelectronics with machine learning

Bioelectronics integrates electronic devices and biological systems with the ability to monitor and control biological processes. From homeostasis to sensorimotor reflexes, closed-loop control with feedback is a staple of most biological systems and fundamental to life itself. Apart from a few examples in bioelectronic medicine, the closed-loop control of biological processes using bioelectronics is not as widespread as in nature. We note that adoption of closed-loop control using bioelectronics has been slow because traditional control methods are difficult to apply to the complex dynamics of biological systems and their sensitivity to environmental changes. Here, we postulate that machine learning can greatly enhance the reach of bioelectronic closed-loop control and we present the advantages of machine learning compared to traditional control approaches. Potential applications of machine learning-based closed-loop control with bioelectronics include further impact in bioelectronic medicine and fine tuning of reactions and products in synthetic biology.

Additive manufacturing methods: techniques, materials, and closed-loop control applications

Additive manufacturing encompasses a set of low-cost and highly versatile tools used to prototype and fabricate three-dimensional (3D) objects with ease. In most of the additive manufacturing techniques, materials are deposited layer by layer until a 3D object is reproduced. Several additive manufacturing techniques have been developed in the previous decade, and the application of additive manufacturing has increased in various industrial sectors. However, there are still drawbacks associated with additive manufacturing techniques, necessitating further study and development. In this study, we review the techniques and materials used in additive manufacturing. The vast majority of additive manufacturing processes are still based on open-loop control or implement some local controllers for specific variables (such as temperature), making them susceptible for errors. This study presents a review of the different additive manufacturing techniques, examples of academic and commercial efforts to improve the control systems for additive manufacturing, as well as the application of additive manufacturing in different fields such as aerospace, electronics, arts, and biomedical. The article ends highlighting the advantages of utilizing a closed-loop control system in additive manufacturing and discussing the work needed for further development.

Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients Receiving Trans-spinal Electrical Stimulation.

Trans-spinal direct current stimulation (tsDCS) provides a non-invasive, clinically viable approach to potentially restore physiological neuromuscular function after neurological impairment, e.g., spinal cord injury (SCI). Use of tsDCS has been hampered by the inability of delivering stimulation patterns based on the activity of neural targets responsible to motor function, i.e., α-motor neurons (α-MNs). State of the art modeling and experimental techniques do not provide information about how individual α-MNs respond to electrical fields. This is a major element hindering the development of neuro-modulative technologies highly tailored to an individual patient. For the first time, we propose the use of a signal-based approach to infer tsDCS effects on large α-MNs pools in four incomplete SCI individuals. We employ leg muscles spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-MNs discharges across multiple lumbosacral segments during isometric plantar flexion sub-maximal contractions. This is done before, immediately after and 30 min after sub-threshold cathodal stimulation. We deliver sham tsDCS as a control measure. First, we propose a new algorithm for removing compromised information from decomposed α-MNs spike trains, thereby enabling robust decomposition and frequency-domain analysis. Second, we propose the analysis of α-MNs spike trains coherence (i.e., frequency-domain) as an indicator of spinal response to tsDCS. Results showed that α-MNs spike trains coherence analysis sensibly varied across stimulation phases. Coherence analyses results suggested that the common synaptic input to α-MNs pools decreased immediately after cathodal tsDCS with a persistent effect after 30 min. Our proposed non-invasive decoding of individual α-MNs behavior may open up new avenues for the design of real-time closed-loop control applications including both transcutaneous and epidural spinal electrical stimulation where stimulation parameters are adjusted on-the-fly.

Design and Modelling of a 90-Degree Ball Valve with a Linear Pressure Drop

Globe valves also known as ball or gate valves are used to control fluid flow in a vast number of applications. Most of the existing applications use them because of their simplicity and very low cost. However, these valves are known for their poor precision for controlling the flow and the lack of electromechanical means for their actuation. Moreover, because of their low linearity their use in closed loop control applications make them nearly unusable. The goal of this research project was to investigate means for redesigning the metering area of a ball valve in such a manner that the pressure vs. flow characteristic would be close to a linear trend. Two different profiles where designed and tested experimentally and modelled using CFD techniques for the estimation of their valve flow coefficients. The model was able to accurately predict the behaviour of the valve with less than a 10% error when fully open. The model can be used for scaling the size of the ball for larger applications and for tuning controlling strategies for flow dispensation in real life applications.

Scaling Analysis of Phase Fluctuations of Brain Networks in Dynamic Constrained Object Manipulation.

In this study, we investigated the dynamic properties of oscillatory activities in the scalp electro-encephalographs (EEGs) of 20 participants involved in a novel dynamic manipulating task using a physical interface and a virtual feedback. The complexity of such a task a rises from the unexpected relationship between the magnitude of the motion and the feedback. The characterization of complex patterns arising from EEG is an important problem in identifying different mental intentions. We proposed a scaling analysis of phase fluctuation in the scalp EEG to discriminate the network states related to different EEG patterns, which correspond to manipulating the task with right or left movement intention. These intentions are generated while the participant is engaged in such a complex task. The phase characterization method was used to calculate the instantaneous phase from the operational EEG. Then, functional brain networks (FBNs) of 20 subjects based on the task-related EEG were constructed by phase synchronization. The degree features representing the structures and scaling components of brain networks are sensitive to the EEG patterns with left or right motor intention. The correlation between features and mental intentions was investigated by discriminant analysis. For 20 subjects, the average accuracy of state detection is , and the average mean-squared error (MSE) is . The brain state depicted by the results is related to high awareness, the phase characterization is of the effectiveness in EEG processing and FBN construction and the difference of control intentions can be explored by the phase characterization method. This finding may be relevant to understanding some neuronal mechanisms underlying the attention and some applications of closed-loop control for the safety operation of tools.