Model-based Closed-loop Control Research Articles

Model-based closed-loop control of thalamic deep brain stimulation.

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

Model-based closed-loop process control for the manufacturing of hairpin coils

AbstractAs a consequence of the increasing market share of hybrid and all-electric mobility solutions, there is a need for robust manufacturing technologies that enable the efficient production of powerful electric traction motors in automotive industry. In the context of high-quality distributed stator windings, the so-called hairpin technology already meets the automotive demand regarding productivity – but process reliability is still limited by variations in the mechanical and geometric properties of the enameled rectangular copper wire leading to different springback effects within the forming processes. Against this background, a model-based closed-loop process control for the sequential tool-bound bending of hairpin coils is introduced and validated in this paper.

Model-based closed-loop control strategies for flex-fuel capability

Meeting future limits for greenhouse gas (GHG) and pollutant emissions represents major challenges for the further development of internal combustion engines for light and heavy-duty applications. To comply with increasingly higher standards, the use of renewable fuels is an important option due to their great potential in terms of energy density, sustainability and low-pollution combustion. However, the increased fuel diversity associated with their use increases the complexity of the powertrain development and calibration process, resulting in an increase in development time and cost. To address these challenges, advanced model-based closed-loop control strategies are applied to optimize the fuel- and air-path settings and make best use of the properties of the different fuels. One such approach is Combustion Rate Shaping (CRS). This ensures a closed-loop control of combustion parameters such as the indicated mean effective pressure, the center of heat release, and the combustion noise excitation. In addition, a model-based closed-loop NOx control could simplify the control on the air-path side. Such a closed-loop approach can further be combined with an onboard fuel detection algorithm and learning functionalities that eventually allow switching to optimal setpoints based on the fuel properties. This publication presents an evaluation of CRS as control concept with flex-fuel operation. The control strategy is implemented on a demonstrator vehicle built up with a Rapid Control Prototyping (RCP) system. The results of emission test cycles with conventional diesel fuel and later with a blend with 1- Octanol, OME3–5 and a paraffinic renewable diesel fuel are presented to illustrate the potential of the novel control concept. The developed CRS concept shows a reduced combustion parameters and emissions dispersion. In particular, the results with OME3–5 blend shows up to 40% reduction in NOx emissions and up to 1 dB(A) lower average combustion noise in comparison to conventional open-loop control. Moreover, an onboard fuel detection and an adaptive control functionality are proposed and experimental validations are carried out to demonstrate the potential to optimize engine emissions.

Advanced model-based closed-loop combustion control strategies with combustion rate shaping

Combustion rate shaping (CRS) is an advanced closed-loop concept which controls the crank-angle resolved combustion trace by adapting the fuel injection profile. The thermodynamic potential of this concept to enable real-time combustion control independent of fuel property variations, injector drift, cylinder tolerances, or changing air-path boundary conditions has already been demonstrated. The highest benefit of this control concept is achieved when it is directly applied on the engine control unit (ECU) in combination with an in-cylinder pressure measurement. This enables online optimization and adaption of the fuel- and air-path settings to the fuel used during vehicle operation. Therefore, the development of a CRS concept with real-time capability has been the main focus so far. To ensure a robust online real-time combustion control, a key development step was the identification of control variables that describe the entire combustion process and only minimally influence each other. For this purpose, the combustion phasing, the combustion gradient and the indicated mean effective pressure have been determined as parameters for the feedback control. However, the stringent requirements for higher efficiencies, emission robustness and combustion noise necessitate the extension of the CRS control concept. Therefore, in this work, the CRS control concept is extended with the aim of realizing a combustion with constant volume pressure increase to the peak firing pressure limit to achieve highest efficiency. In addition, intelligent control of the fuel injection profile is to be used to achieve a combustion temperature for lowest possible NOx raw emissions and a robust combustion noise excitation limitation. The optimized control strategy is implemented on a heavy-duty single cylinder engine test bench using a Rapid Control Prototyping (RCP) system, and the control performance is demonstrated.

Deep Reinforcement Learning: A New Beacon for Intelligent Active Flow Control

The ability to manipulate fluids has always been one of the focuses of scientific research and engineering application. The rapid development of machine learning technology provides a new perspective and method for active flow control. This review presents recent progress in combining reinforcement learning with high-dimensional, non-linear, and time-delay physical information. Compared with model-based closed-loop control methods, deep reinforcement learning (DRL) avoids modeling the complex flow system and effectively provides an intelligent end-to-end policy exploration paradigm. At the same time, there is no denying that obstacles still exist on the way to practical application. We have listed some challenges and corresponding advanced solutions. This review is expected to offer a deeper insight into the current state of DRL-based active flow control within fluid mechanics and inspires more non-traditional thinking for engineering.

Model-Based Closed-Loop Control for High-Power Current Source Rectifiers Under Selective Harmonic Elimination/Compensation PWM With Fast Dynamics

Selective harmonic elimination/compensation PWM (SHE/SHC-PWM) is widely used in current source rectifiers (CSRs) under normal/distorted grid conditions due to its best line current harmonic performance with reduced switching losses. However, due to the off-line nature of SHE/SHC-PWM that operates over fundamental frequency, its dynamic performance under closed-loop control is usually not satisfactory compared with the space-vector modulation or carrier-based PWM (CB-PWM) for CSR. To improve the closed-loop dynamics of CSR under SHE/SHC-PWM, a model-based closed-loop control scheme is proposed in this article. In case of transient when the dc current reference of CSR changes, the new input references of the SHE/SHC-PWM module, i.e., the modulation index and delay angle, are directly calculated and updated in real-time based on the mathematical model of CSR derived in this article. As a result, the dynamic performance for dc current tracking under closed-loop SHE/SHC-PWM operations can be improved greatly due to this added model-based feedforward path, while the conventional feedback controller is still adopted but only for steady-state error corrections. The application of the proposed method on back-to-back current source converter (CSC) motor drives is also introduced. Simulation and experimental results validate this proposed control for CSR with fast dynamics.

Combustion rate shaping for flex-fuel applications

Compliance with future greenhouse gas (GHG) and pollutant emissions poses major challenges for the further development of advanced internal combustion engines for light and heavy-duty applications. To meet all standards, the use of synthetic fuels and/or E-Fuels is an important alternative due to their enormous potential in terms of energy density, sustainability and low pollution combustion. However, the increased fuel diversity associated with the use of these alternative fuels adds an additional layer of complexity to the powertrain development and calibration process, resulting in an increase in development time and cost. This necessitates the application of advanced model-based closed-loop control strategies to optimize the fuel- and air-path calibration to make best use of the properties of the different fuels. One potential solution to address the impact of flex-fuel variances on the combustion process is the Combustion Rate Shaping (CRS) concept presented in this paper. It can maintain a desired combustion trace irrespective of fuel variations, hardware drifts and other external factors, thus ensuring optimal combustion. The highest benefit of this control concept comes from its application directly on the engine control unit (ECU) in combination with an in-cylinder measurement. This enables the online real-time control of the combustion, regardless of which fuel is being used. In addition, this also enables optimization and adaption of the fuel- and air-path settings to the used fuel while driving the vehicle. However, so far, the high computational cost of this control concept limits its real-time capability. Therefore, the optimization of the control concept to achieve real-time capability is the focus of this paper. The optimized control strategy is implemented on a demonstrator engine test bench built using a Rapid Control Prototyping (RCP) system. The controller performance is demonstrated both under steady-state and transient operating conditions, and the potential of the concept is demonstrated in several cases such as engine temperature variations, injector drift, individual cylinder balancing, and fuel-variation.

Low-Order Nonlinear Animal Model of Glucose Dynamics for a Bihormonal Intraperitoneal Artificial Pancreas.

The design of an Artificial Pancreas (AP) to regulate blood glucose levels requires reliable control methods. Model Predictive Control has emerged as a promising approach for glycemia control. However, model-based control methods require computationally simple and identifiable mathematical models that represent glucose dynamics accurately, which is challenging due to the complexity of glucose homeostasis. In this work, a simple model is deduced to estimate blood glucose concentration in subjects with Type 1 Diabetes Mellitus (T1DM). Novel features in the model are power-law kinetics for intraperitoneal insulin absorption and a separate glucagon sensitivity state. Profile likelihood and a method based on singular value decomposition of the sensitivity matrix are carried out to assess parameter identifiability and guide a model reduction for improving the identification of parameters. A reduced model with 10 parameters is obtained and calibrated, showing good fit to experimental data from pigs where insulin and glucagon boluses were delivered in the intraperitoneal cavity. A simple model with power-law kinetics can accurately represent glucose dynamics submitted to intraperitoneal insulin and glucagon injections. The reduced model was found to exhibit local practical as well as structural identifiability. The proposed model facilitates intraperitoneal bi-hormonal model-based closed-loop control in animal trials.

Experimental model-based closed-loop control of a separated boundary layer at high Reynolds number

Experimental robust closed-loop control of a two-dimensional turbulent boundary layer with substantial separation is investigated. An array of 22 round fluidic jets located upstream of the separation location is used as actuation to reattach the flow. Measurements of phase-averaged velocity with 2D2C Particle Image Velocimetry (PIV) and wall friction using hot-film anemometry are performed to characterize the flow dynamics under open-loop actuation to elaborate models of the flow transient. Different feedback model-based controllers (Proportional Integral, linear–quadratic, linear–quadratic-gaussian and H∞ regulators) are then designed and implemented experimentally. The performances in term of precision, reactivity and control cost of each of the controllers are presented and discussed. Only the H∞ controller is found to maintain high performances despite large upstream unsteady perturbations.

Dynamic Modeling of Fiber-Reinforced Soft Manipulator: A Visco-Hyperelastic Material-Based Continuum Mechanics Approach.

Robot-assisted surgery is gaining popularity worldwide and there is increasing scientific interest to explore the potential of soft continuum robots for minimally invasive surgery. However, the remote control of soft robots is much more challenging compared with their rigid counterparts. Accurate modeling of manipulator dynamics is vital to remotely control the diverse movement configurations and is particularly important for safe interaction with the operating environment. However, current dynamic models applied to soft manipulator systems are simplistic and empirical, which restricts the full potential of the new soft robots technology. Therefore, this article provides a new insight into the development of a nonlinear dynamic model for a soft continuum manipulator based on a material model. The continuum manipulator used in this study is treated as a composite material and a modified nonlinear Kelvin-Voigt material model is utilized to embody the visco-hyperelastic dynamics of soft silicone. The Lagrangian approach is applied to derive the equation of motion of the manipulator. Simulation and experimental results prove that this material modeling approach sufficiently captures the nonlinear time- and rate-dependent behavior of a soft manipulator. Material model-based closed-loop trajectory control was implemented to further validate the feasibility of the derived model and increase the performance of the overall system.

Closed-loop control of eccentric presses based on inverse kinematic models

This work shows how to control the ram motion in servo-mechanical presses with non-linear kinematics by means of a model-based closed-loop control. The use of servo drives in forming machines allows for an arbitrarily controlled drive motion and paves the way for modern, more flexible and adaptable forming machines. However, servo-mechanical presses hardly go beyond the closed-loop control of their servo drives. Especially the control of eccentric presses suffers from non-linear kinematics that impede a closed-loop ram motion control. As a result, the actual position, speed and acceleration of the ram are uncontrollable and harmful effects like tilting occur under real process conditions. We present how to precisely control the ram motion by means of a model-based closed-loop control for a multi-point eccentric press. Using position sensors measuring the ram position in three degrees of freedom and the inverse kinematics of the machine, a closed-loop ram motion control is demonstrated. Experiments investigating different types of control under process conditions show a significant improvement in the accuracy of the ram motion.

Control-oriented models for ink-jet 3D printing

This paper proposes two control-oriented models to describe the layer-to-layer height evolution of the ink-jet 3D printing process. While the process has found wide applicability, as with many industrial 3D printing systems, ink-jet 3D printers operated in open loop, even when sensor measurements are available. One of the primary reasons for this is the lack of suitable control-oriented models of the process. To address this issue, two control-oriented models are proposed in this paper, the first is based on droplet geometry superposition, while the other is based on a graph-based characterization of local flow dynamics. The superposition model ignores the flow of the deposited liquid material, while the graph-based dynamic model is able to capture this phenomenon. These two models are compared with an existing flow-based empirical model and validated with experimental results, with the graph-based dynamic model demonstrating between 5 and 14% improvement in layer height prediction. Both the superposition model and the graph-based dynamic model are suitable for closed-loop control algorithm design (as they are discrete layer-to-layer state-space models). We envision that these control-oriented models will ultimately enable the design of model-based closed-loop control for high resolution ink-jet 3D printing.

Blanking-bending process chain with disturbance feed-forward and closed-loop control

Bending and other manufacturing processes are subject to uncertain process conditions like varying properties of the semi-finished parts. In order to obtain precise bending angles, these variations have to be compensated. Upstream processes often deliver valuable information about the actual part properties. This paper presents a blanking-bending process chain in which information from the blanking process is passed on to the bending process. A feed-forward bending controller uses this information as input state and compensates deviations from the nominal state. In a second instance, a closed-loop controller addresses model uncertainty of the feed-forward controller as well as unknown disturbances. A model-based closed-loop control design is shown which increases stability, stationary accuracy and time-optimality.

Parameter identification and observer-based control for distributed heating systems – the basis for temperature control of solid oxide fuel cell stacks

The control of high-temperature fuel cell stacks is the prerequisite to guarantee maximum efficiency and lifetime under both constant and varying electrical load conditions. Especially, for time-varying electrical load demands, it is necessary to develop novel observer-based control approaches that are robust against parameter uncertainties and disturbances that cannot be modelled a priori. Since we aim at real-time applicability of these control procedures, classical high-dimensional models – which result from a discretization of mathematical descriptions given by the partial differential equations for heat and mass transfer – cannot be applied. Furthermore, these models have to be linked to the electrochemical properties of the fuel cell. To reduce the order of these models to a degree that allows us to use them in real-time, information on both the temperature distribution in the fuel cell stack and the heat flow into its interior due to electrochemical reactions is required. However, a direct temperature measurement is not possible from a practical point of view. For that reason, it is essential to reliably estimate the temperature distribution and the heat flow on the basis of easily available measured data. These data have to be available not only during development stages but also in future series products. For such products, it is desirable to reduce the number of sensors to improve the system's reliability and to decrease the operating costs. The basic strategies that are applicable for model-based open-loop and closed-loop control of heating systems as well as for the identification of parameters, operating conditions and disturbances as well as for state monitoring are summarized in this article. They are demonstrated for exemplary set-ups in both simulation and experiment.

Experimental Study of Laser Interferometry Based Motion Tracking of a Flexure-Based Mechanism

This paper presents an experimental study of laser interferometry-based closed-loop motion tracking for flexure-based four-bar micro/nano manipulator. To enhance the accuracy of micro/nano manipulation, laser interferometry-based motion tracking control is established with experimental facility. The authors present and discuss open-loop control, model-based closed-loop control, and robust motion tracking closed-loop control for flexure-based mechanism. A comparative error analysis for closed-loop control with capacitive position sensor and laser interferometry feedback is discussed and presented. Model-based closed-loop control shows improvement in position and motion tracking over open-loop control. Robust control demonstrates high precise and accurate motion tracking of flexure-based mechanism compared to the model-based control. With this experimental study, this paper offers evidence that the laser interferometry-based closed-loop control can minimize positioning and tracking errors during dynamic motion, hence realizing high precision motion tracking and accurate position control.

Varying Parameters of the Object Model on Fuzzy Neural Network Controller for Activated Sludge Method of the Sewage Treatment System

In this paper, we take characteristics of wastewater treatment and process technology, drawing on the effectiveness of thetraditional PID control and on the basis of its lack, with the key steps in the sewage treatment process - Aeration control of part of the process parameters, Fuzzy neural network control of dissolved oxygen concentration (DO) to achieve negative feedback control loop,design a model-based closed-loop cascade control system. Fuzzy systems, membership function, the structure of the network topology and algorithms are based on the actual issues identified in the fuzzy variables. Aiming at the four parts of the fuzzy control, adopting four fuzzy neural network based on the standard model - the input layer, Fuzzy layer,Inference layer,Clear layer are corresponding with it. Standing on two points: the dissolved oxygen concentration control and the rate of change from the error ,then design the Fuzzy neural network controller. Then the fuzzy neural network control technology could be used in wastewater treatment on the specific application of process control.

Adaptive Closed-Loop Separation Control on a High-Lift Configuration Using Extremum Seeking

We present experimental results on adaptive closed-loop separation control on a 2-D generic high-lift configuration. Because model-based closed-loop flow control suffers from the lack of sufficient simple physical models for this configuration, a non-model-based control strategy, namely, the gradient-based extremum-seeking scheme, is used here. The controller exploits spanwise distributed pressure measurements and adjusts pulsed jets near the leading edge of the single-slotted flap. The jets are used for flow excitation to suppress separation over the flap at high angles of attack, high deflection angles, or to reattach an already separated flow. Starting from a single-input/single-output design, the extremum-seeking scheme is extended to both a single-input/single-output slope-seeking approach and a multi-input/multi -output approach. Multi-input/multi -output control accounts for spanwise-distributed, small-scale separation phenomena and shows the best performance. Additionally, this case even improves lift gain compared to preliminary open-loop studies. A lift increase is not only observed for angles of attack for which the unactuated flow obviously separates, but as well for smaller angles, which were assumed before to lead to an unseparated flow. Hence, closed-loop results demonstrate the capability of slope-seeking control to adjust the control signal automatically in an energy-efficient sense such that separation is minimized even in the presence of disturbances.

Modeling and closed-loop control of hypnosis by means of bispectral index (BIS) with isoflurane

A model-based closed-loop control system is presented to regulate hypnosis with the volatile anesthetic isoflurane. Hypnosis is assessed by means of the bispectral index (BIS), a processed parameter derived from the electroencephalogram. Isoflurane is administered through a closed-circuit respiratory system. The model for control was identified on a population of 20 healthy volunteers. It consists of three parts: a model for the respiratory system, a pharmacokinetic model and a pharmacodynamic model to predict BIS at the effect compartment. A cascaded internal model controller is employed. The master controller compares the actual BIS and the reference value set by the anesthesiologist and provides expired isoflurane concentration references to the slave controller. The slave controller maneuvers the fresh gas anesthetic concentration entering the respiratory system. The controller is designed to adapt to different respiratory conditions. Anti-windup measures protect against performance degradation in the event of saturation of the input signal. Fault detection schemes in the controller cope with BIS and expired concentration measurement artifacts. The results of clinical studies on humans are presented.

Towards Model-Based Closed-Loop Control of the Basic Oxygen Steelmaking Process

Model-based closed-loop control of the basic oxygen steelmaking process will give an opportunity to increase process operation performance. By using control algorithms that interact with thermodynamical and physical models capable of making real-time predictions of the effects of control actions, the time delays in order to wait for process response will be eliminated. Existing limitations like pre-set lance programs can be avoided. This paper outlines the concept of a recently started project aiming at model-based closed-loop control as well as present the initial work that has been made on the subject.

Use of a pharmacokinetic-dynamic model for the automatic feedback control of atracurium

A control algorithm for the closed-loop control of atracurium-induced neuromuscular blockade based on pharmacokinetic-dynamic model-based adaptive feedback is described. Mean offset from setpoint at 90% neuromuscular block was 0.04% and the mean standard deviation from mean neuromuscular block was 1.9%. The mean average atracurium requirement was 0.37 mg.kg-1.h-1. The model-based adaptive closed-loop control of atracurium infusion provided reasonable control of muscle relaxation. It offers a solution to the problem of adapting pharmacokinetic and pharmacodynamic data to individuals when using mean data as starting values for drug therapy.