Nonlinear Model-based Control Scheme Research Articles

Design and Implementation of Takagi-Sugeno Fuzzy Model Based Control Scheme for the Continuous Stirred Tank Reactor

In this work, the authors have designed and implemented a Takagi-Sugeno fuzzy model based control scheme (TS-MBC) for the continuous stirred tank reactor (CSTR) process. The proposed control scheme can be viewed as an alternative to the nonlinear model based control scheme proposed by Panda & Prakash1. In this work the nonlinear process is represented using a bank of linear discrete state space models combined using fuzzy membership function. The output of the proposed control scheme is computed in two steps. The bank of linear models weighted using fuzzy membership function is used to compute the value of the controller output in the first step. In the second step, the controller output computed in the first step is updated based on the measurement. The performance of the proposed control scheme has been compared with that of a nonlinear model based control scheme (NMBC) and nonlinear PID (NPID).

Development of a partially accurate model and application to a reduced-order control scheme for heat integrated distillation column

The heat integrated distillation column (HIDiC) has huge potential on energy saving. A partially accurate model is proposed to describe this system with a reduced form. The new model improves the tracking accuracy of the dynamic behaviors at specific stages related to the distillation products so as to serve for an efficient nonlinear model-based control scheme. Estimates of the unmeasured model parameters are generated based on a limited number of temperature measurements, and the locations of these measurements are determined strategically by the combination of a weighted PCA (principal component analysis) method and a relative distribution index. A nonlinear control scheme is then formulated by combining the partially accurate model and the structure of an extended generic model control. The relative order of the HIDiC is reduced by the proposed model, and hence the number of the controller parameters are decreased. The proposed reduced-order control scheme is compared to two control schemes, namely a normal extended generic model control strategy and a classical PI controller, to evaluate the validity of the nonlinear model-based control for the HIDiC.

Modeling and nonlinear parameter identification for hydraulic servo-systems with switching properties

Today’s automotive industry faces challenges in reducing emissions to comply with ever tightening statutory provisions. The future is set to provide cleaner vehicles which, on the one hand, reduce the required energy consumption while on the other hand are fit to fulfill customer preferences with regard to driving performance. As a result, the design and control of automotive components in the drive train are reconsidered. This paper treats the modeling and parameter identification of a hydraulic circuit for clutch actuation in automatic transmissions (AT) or dedicated hybrid transmissions (DHT). The examined hydraulic servo-system’s function is to provide the necessary pressure and volume flow for clutch actuation without usage of a hydraulic accumulator. Physical modeling and parameter identification is executed using testbed measurements of an according prototype hardware, aiming to contribute in finding a way towards destruction free identification of hydraulic systems. The nonlinear identification for the examined system that shows switching behaviour is performed using the derivation free particle swarm optimization (PSO) method. The physical models are developed for the purpose of being employed for the design of a trajectory tracking nonlinear model-based control scheme for clutch actuation based on the concept of input-output linearization.

A proton exchange membrane fuel cell with an airflow cooling system: Dynamics, validation and nonlinear control

This work synthesizes a reduced-order sliding mode observer-based sliding mode controller for a proton exchange membrane fuel cell system. This multivariable closed-loop system is dealt with two inputs (hydrogen fuel flow and cooling air flow rate) and two outputs (output voltage and stack temperature). For this, at first, we develop a nonlinear transient proton exchange membrane fuel cell model. This model is verified with the available experimental data. To maintain the fuel cell operating temperature, it is coupled with an airflow cooling fan. Then a nonlinear model-based control scheme is formulated that consists of sliding mode controller and reduced-order sliding mode observer. A special care is taken in control model against chattering response. Evaluating the sliding mode observer performance, it is integrated with sliding mode controller and the resulting hybrid control structure is compared with a traditional proportional integral controller. The nonlinear sliding mode observer-based controller shows its superiority over the proportional integral controller in rejecting the disturbance as well as tracking the set point.

Robust multi-objective control design for underground coal gasification energy conversion process

ABSTRACTThe efficiency of an underground coal gasification (UCG) energy conversion process can be increased by maintaining a desired heating value of the product gases. In literature this task has been accomplished by adopting nonlinear model-based control strategies. To exploit the flexibility of the linear control design techniques, a linear model of the UCG process has been developed, which retains the dynamics of the nonlinear model around the operating point of interest. To account for external disturbance and modelling inaccuracies, an output-based robust multi-objective H2/H∞ control law integrated with pole placement has been proposed. The problem is solved by formulating linear matrix inequality (LMI) constraints for performance and robustness. The simulation results show that the designed controller gives adequate performance in the presence of modelling inaccuracies and external disturbance. Moreover, it has been shown that performance of the designed controller is better as compared to the standard PI controller.

Adaptive nonlinear model-based control scheme implemented on the nonlinear processes

In this work, two adaptive nonlinear model-based control schemes have been proposed and implemented on the simulated model of the nonlinear benchmark processes. The servo performance of the proposed control schemes was found satisfactory. In order to improve the servo-regulatory performance of one of the proposed control schemes, the model state and parameters have been estimated simultaneously online with the help of a derivative-free Kalman filter and the predicted values of model states have been used in the proposed control law. The performances have been compared between two proposed control schemes with conventional adaptive PI control scheme. From the extensive simulation studies, it has been found that proposed control schemes implemented on nonlinear processes are having better performance over conventional adaptive PI control scheme. It was also found that proposed control schemes are able to eliminate measurement noise and having good robustness features.

Modelling and control of nonlinear resonating processes: part II—model based control using orthogonal basis function based Wiener models

This paper is an extension of the work presented by Reddy and Saha (Int J Dyn Control 2016) i.e. Part I of this series of papers. Reddy and Saha (2016) developed Wiener type nonlinear models using orthogonal basis filters which were proved to be ideal candidates for modeling nonlinear resonating systems such as continuous stirred tank reactor(s). Those nonlinear models have further been employed in the present work for developing nonlinear model based control strategies and their closed-loop control performance have been evaluated through simulation case studies involving same continuous stirred tank reactor(s). Two types of model based control strategies are adopted viz. nonlinear model predictive control and nonlinear internal model control. In case of nonlinear internal model controller, the problem of model inversion has been addressed, first of its kind in approach, through tactful modification of Wiener model coupled with optimization technique. Deletion of positive zeros from orthogonal basis filters while modifying Wiener model is the uniqueness of this paper and it ensures stability of the controller.

Nonlinear model predictive control of fed-batch fermentations using dynamic flux balance models

Fed-batch fermentation is an important production technology in the biochemical industry. Using fed-batch Saccharomyces cerevisiae fermentation as a prototypical example, we developed a general methodology for nonlinear model predictive control of fed-batch bioreactors described by dynamic flux balance models. The control objective was to maximize ethanol production at a fixed final batch time by adjusting the glucose feeding rate and the aerobic–anaerobic switching time. Effectiveness of the closed-loop implementation was evaluated by comparing the relative performance of NMPC and the open-loop optimal controller. NMPC was able to compensate for structural errors in the intracellular model and parametric errors in the substrate uptake kinetics and cellular energetics by increasing ethanol production between 8.0% and 14.7% compared with the open-loop operating policy. Minimal degradation in NMPC performance was observed when the biomass, glucose, and ethanol concentration and liquid volume measurements were corrupted with Gaussian white noise. NMPC based on the dynamic flux balance model was shown to improve ethanol production compared to the same NMPC formulation based on a simpler unstructured model. To our knowledge, this study represents the first attempt to utilize a dynamic flux balance model within a nonlinear model-based control scheme.

Experimental validation of a Nonlinear Model based Control scheme on the Variable area tank process

Abstract In this paper, the authors have proposed a simple non-linear model based control scheme for the variable area tank process. The proposed control scheme relies on the nonlinear model of the conical tank process. The parameters of the nonlinear model have been determined using empirical approach. The proposed control algorithm has been implemented on pilot scale conical tank experimental setup. The performance of the proposed control scheme is compared with that of a gain scheduled PI controller.

Nonlinear burn condition control in tokamaks using isotopic fuel tailoring

One of the fundamental problems in tokamak fusion reactors is how to control the plasma density and temperature in order to regulate the amount of fusion power produced by the device. Control of these parameters will be critical to the success of burning plasma experiments like ITER. The most previous burn condition control efforts use either non-model based control designs or techniques based on models linearized around particular operating points. Such strategies limit the potential operational space and must be carefully retuned or redesigned to accommodate changes in operating points or plasma parameters. In this work, a nonlinear dynamic model of the spatial averages of energy and ion species densities is used to synthesize a nonlinear feedback controller for stabilizing the burn condition. The nonlinear model-based control strategy guarantees a much larger operational space than previous linear controllers. Because it is not designed around a particular operating point, the controller can be used to move from one burn condition to another. The proposed scheme first attempts to use regulation of the auxiliary heating power to reject temperature perturbations, then, if necessary, uses isotopic fuel tailoring as a way to reduce fusion heating during positive temperature perturbations. A global model of hydrogen recycling is incorporated into the model used for design and simulation, and the proposed control scheme is tested for a range of recycling model parameters. As we find the possibility of changing the isotopic mix can be limited for certain unfavorable recycling conditions, we also consider impurity injection as a back-up method for controlling the system. A simple supervisory control strategy is proposed to switch between the primary and back-up control schemes based on stability and performance criteria. A zero-dimensional simulation study is used to study the performance of the control scheme for several scenarios and model parameters. Finally, a one-dimensional simulation is done to test the robustness of the control scheme to spatially varying parameters.

A novel non-linear model-based control strategy to improve PEMFC water management – The flatness-based approach

In the area of PEMFC, water management and thus membrane humidity still remains one of the most challenging issues affecting PEMFC efficiency and lifetime. In this investigation, an innovative method to improve PEMFC water management is presented and a non-linear model-based control strategy is proposed. The novelty of this approach relies upon a simplified PEMFC model combining the benefits of the Differential Flatness Theory. Efficiency and relevance of the proposed water management strategy is confirmed in simulation environment through several controlled scenarios. It was found that in each case, the flatness-based controller successfully regulates the membrane humidity, while avoiding flooding or even membrane drying that can lead to irreversible damage. Furthermore, the novel model demonstrates excellent performance in terms of set-point tracking, disturbances rejection and robustness against parameters uncertainties and measurement noise. Overall, this novel approach appears to be a possible and promising towards improving PEMFC water management issues.

Nonlinear Model-Based Control of Thin-Film Drying for Continuous Pharmaceutical Manufacturing

This paper considers the model-based control of composition and thickness for a thin-film drying process used in the continuous manufacturing of pharmaceutical tablets. In this nonlinear distributed dynamical system, a drug formulation solution is coated onto a moving surface and then dried to form thin films of approximately 250 μm in thickness. A dynamic optimizer is designed that employs a first-principles process model to simulate the spatial distribution of solvent concentration in the film and the thin film shrinkage during drying. A critical parameter to describe the highly nonlinear dynamics of the thin-film drying is the mutual polymer–solvent diffusion coefficient, which strongly depends on solvent concentration and film temperature. Two optimal control problems are studied for set point tracking of solvent concentration and minimization of energy consumption in the dryer while satisfying various operational and product quality constraints. An unscented Kalman filter is designed to facilitate the output feedback implementation of the dynamic optimizer and to estimate unmeasured thin-film quality attributes such as the film thickness. The performance of the model-based controller is compared to that of a proportional integral controller in two simulation case studies. The nonlinear model-based control strategy has improved versatility and the potential to reduce production of off spec material.

Comparison of model-based and conventional controllers on a pilot-scale heat exchanger

This pilot-scale heat exchanger demonstration compares two relatively simple nonlinear model-based control strategies to conventional proportional-integral (PI) control. The two nonlinear controllers, generic model control (GMC) and process-model based control (PMBC), use a first-principles model thereby providing characterization of the nonlinear process throughout the operating range. There are two approaches to GMC, one uses a dynamic model, the other a steady-state model. This work uses the steady-state model; accordingly, will use the term GMC-SS, which can be classified as output characterization for a PI controller, making it relatively simple to implement. PMBC uses a dynamic model and adapts to represent the process. These two nonlinear controllers were selected for this application evaluation because of their simplicity (they can be implemented in-house within many commercial control systems), diversity (steady-state and dynamic models), and demonstrated utility for control of nonlinear single-input–single-output processes. The application and results are presented and discussed.Summarizing the results: Within a small temperature operating range PI provides good control, but over the full operating range, the nonlinear and variable delay of the process lead to poor control with PI. GMC can handle the nonlinear issues, but using the convenient steady-state model; it also, provides poor control because of the variable delay associated with flow rate. PMBC was able to provide good control throughout the entire operating range. PMBC has a further advantage of only having one tuning coefficient, while PI and GMC-SS have two.

Nonlinear predictive control in the LHC accelerator

This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.

An efficient multi-objective model predictive control framework of a PEM fuel cell

Fuel cell systems can produce clean energy and have attracted the interest of both industrial and basic research in the recent years. They are part of a promising benign and environmentally friendly technology and they can be used both in mobile and stationary applications. A dynamic model was constructed and validated using experimental data based on a specific application, consisting of a high temperature PEM Fuel Cell (FC) working at a constant pressure and a Power Conversion Device that controls the current drawn from the FC. An integrated framework that consists of an online maximum power point prediction algorithm and a non-linear model based control scheme is presented. The proposed framework aims to maintain the fuel cell close to the optimum power point and the corresponding oxygen excess ratio level. Simulation studies show that the proposed control framework results in improved performance regarding the efficient and safe fuel cell operation under varying operating conditions.

Neural Control of Fast Nonlinear Systems— Application to a Turbocharged SI Engine With VCT

Today, (engine) downsizing using turbocharging appears as a major way in reducing fuel consumption and pollutant emissions of spark ignition (SI) engines. In this context, an efficient control of the air actuators [throttle, turbo wastegate, and variable camshaft timing (VCT)] is needed for engine torque control. This paper proposes a nonlinear model-based control scheme which combines separate, but coordinated, control modules. Theses modules are based on different control strategies: internal model control (IMC), model predictive control (MPC), and optimal control. It is shown how neural models can be used at different levels and included in the control modules to replace physical models, which are too complex to be online embedded, or to estimate nonmeasured variables. The results obtained from two different test benches show the real-time applicability and good control performance of the proposed methods.

Nonlinear Predictive Control in the LHC Accelerator

Abstract This paper describes the application of a nonlinear model-based control strategy in a real challenging process. A predictive controller based on a nonlinear model derived from physical relationships, mainly heat and mass balances, has been developed and commissioned in the inner triplet heat exchanger unit (IT-HXTU) of the large hadron collider (LHC) particle accelerator at European Center for Nuclear Research (CERN). The advanced regulation maintains the magnets temperature at about 1.9 K. The development includes a constrained nonlinear state estimator with a receding horizon estimation procedure to improve the regulator predictions.

Nonlinear air-to-fuel ratio and engine speed control for hybrid vehicles

Internal combustion spark ignition engine management systems regulate the fuel, spark, and idle air subsystems to achieve sufficient engine performance at acceptable fuel economy and tailpipe emission levels. Engine control units also monitor other engine processes, using a suite of sensors, and periodically check the system actuators' operation to satisfy legislated onboard diagnostics. The majority of production engines regulate the air-to-fuel ratio using a speed-density, or air-flow, control strategy. In this approach, the mass of air drawn into a given cylinder is calculated using the engine speed, manifold absolute pressure, and inlet air temperature. Based on the air mass, appropriate fuel amounts are injected to achieve stoichiometric operation. However, the wide range of operating conditions, inherent induction process nonlinearities, and gradual component degradations due to aging have prompted research into model-based algorithms. In this paper, a nonlinear model-based control strategy will be proposed for simultaneous air-to-fuel ratio control and speed tracking in hybrid electric vehicles. The motivation for engine speed management resides in the integrated control of the engine and a continuously variable transmission for increased efficiency. The proposed backstepping controller uses an observer to reduce the inputs to manifold air mass (e.g., manifold absolute pressure and inlet air temperature) and engine speed. The underlying engine model describes the air intake, fuel injection, and rotational dynamics. For comparison purposes, an existing multisurface sliding mode controller and an integrated speed-density air-to-fuel controller with attached engine speed regulation have been implemented. The performance of each controller is studied using an analytical engine model with representative numerical results presented and discussed to provide insight into the overall performances.

Nonlinear Internal Model Control for Systems with Measured Disturbances and Input Constraints

A nonlinear model-based control scheme is presented which extends nonlinear internal model control to systems with constrained inputs and measured disturbances. A method is proposed for a direct synthesis nonlinear controller which solves an instantaneous optimization to minimize the performance loss associated with enforcing the actuator constraints. Within this framework, the nonlinear controller can be represented as an optimal linear controller with an auxiliary feedback loop which cancels the effects of nonlinear dynamics and measured disturbances. The methods developed within the paper are applied to three simulation examples: a constrained linear example with measured disturbances, an isothermal reactor with Van de Vusse kinetics, and an industrial styrene polymerization reactor.

Nonlinear Control for Maximum Production Rate of Latexes of Well-Defined Polymer Composition

In this work, on-line strategies based on calorimetry were used to maximize the production of vinyl acetate/butyl acrylate latexes under safe conditions, maintaining simultaneously the copolymer composition of the polymer at predefined values. It was also shown that these strategies were able to avoid monomer accumulation in the reactor, and hence potentially dangerous thermal runaway situations, without any deleterious effect on the polymer composition, when a sudden inhibition was induced by deliberately adding a solution of hydroquinone. For this purpose, nonlinear model-based control strategies and conventional feedback controllers were used.