Nonlinear Model-based Control Research Articles

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.

Adaptive control for multistage machining process scenario—bar turning with varying material properties

This paper discusses the multistage manufacturing scenario in context of progressive machining and demonstrates an adaptive control scheme for turning operation of a partially hardened bar. A nonlinear mechanistic force model-based control framework attempts to control the cutting force at a designated set point, with material properties changing over the cut. The force coefficients for the material are calculated offline using experimental data and Bayesian inference methods. Since the hardened part of the bar will shift the force coefficient values, an online estimation strategy (Bayesian recursive least square estimator) is used to learn the new coefficients as well as satisfying the control objective. With the newly learned coefficients passed downstream, the subsequent operation experiences no compromise of control objective as well as reduces the maximum values of force encountered. Numerical analyses presented show the adaptation and control scheme performance. Finally, the experimental analysis show the open-loop and closed-loop model adaptation and effective force set point regulation using experimental apparatus and partially hardened MS (AISI1045) bar.

Monitoring and control interface based on virtual sensors.

In this article, a toolbox based on a monitoring and control interface (MCI) is presented and applied in a heat exchanger. The MCI was programed in order to realize sensor fault detection and isolation and fault tolerance using virtual sensors. The virtual sensors were designed from model-based high-gain observers. To develop the control task, different kinds of control laws were included in the monitoring and control interface. These control laws are PID, MPC and a non-linear model-based control law. The MCI helps to maintain the heat exchanger under operation, even if a temperature outlet sensor fault occurs; in the case of outlet temperature sensor failure, the MCI will display an alarm. The monitoring and control interface is used as a practical tool to support electronic engineering students with heat transfer and control concepts to be applied in a double-pipe heat exchanger pilot plant. The method aims to teach the students through the observation and manipulation of the main variables of the process and by the interaction with the monitoring and control interface (MCI) developed in LabVIEW©. The MCI provides the electronic engineering students with the knowledge of heat exchanger behavior, since the interface is provided with a thermodynamic model that approximates the temperatures and the physical properties of the fluid (density and heat capacity). An advantage of the interface is the easy manipulation of the actuator for an automatic or manual operation. Another advantage of the monitoring and control interface is that all algorithms can be manipulated and modified by the users.

Mathematical modelling of a diesel common-rail system

In diesel common-rail systems, the exact knowledge of the injection pressure is important to accurately control the injected diesel mass and thus the combustion process. This paper focuses on the mathematical modelling of the hydraulic and mechanical components of a common-rail system in order to describe the dynamics of the diesel rail pressure. Based on this model, an average model is derived to reduce the model complexity and to allow for a fast calculation of the mass flow into the rail for different crank shaft revolution speeds and openings of the fuel metering unit. The main purpose of this average model is to serve as a basis for a model-based (non-linear) controller design. The stationary accuracy of the models is validated by means of measurement data.

Nonlinear intelligent DC grid stabilization for fuel cell vehicle applications with a supercapacitor storage device

Abstract This paper presents an intelligent DC link control using a fuzzy logic controller based on the differential flatness control theory for hybrid vehicle applications supplied by a fuel cell (FC) (main source) and a supercapacitor (auxiliary source). The energy in the system is balanced by dc bus energy stabilization (or indirect voltage regulation). A supercapacitor module functions by supplying energy to regulate the dc bus energy. The FC, as a slow dynamic source in this system, supplies energy to the supercapacitor module to maintain its charge. The FC converter combines four-phase parallel boost converters with interleaving, and the supercapacitor converter employs four-phase parallel bidirectional converters with interleaving. These two converters are called a multi-segment converter for high power applications. Because the model of the power switching converters is nonlinear, it is preferable to apply model-based nonlinear control strategies that directly compensate for the nonlinearity of the system without requiring a linear approximation. Using the intelligent fuzzy control law based on the flatness property, we propose straightforward solutions to hybrid energy management and to the dynamic and regulation problems. To validate the proposed method, a hardware system is developed with analogue circuits, and a numerical calculation is generated with a dSPACE controller DS1104. Experimental results for a small-scale power plant (a polymer electrolyte membrane FC (PEMFC) of 1200 W and 46 A with a supercapacitor module of 100 F, 500 A, and 32 V) in the laboratory corroborate the excellent performance of this control scheme during vehicle motor drive cycles.

Nonlinear model-based control of the Czochralski process IV: Feedforward control and its interpretation from the crystal grower׳s view

The paper proposes a model-based feedforward control for the Czochralski crystal growth process. It completes the system for nonlinear model-based control presented by the authors in previous publications. From a system theoretical point of view such a feedforward control requires a dynamic lumped parameter model of the hydrodynamical–geometrical as well as of the thermal subsystem of the process. For the heater feedforward control one lacks the latter one with the necessary accuracy. A static approach circumventing this problem is presented here, based on a mathematical description regarding the most important qualitative dependencies between relative changes of manipulated and controlled variables during growth. In combination with feedback control, high precision tracking of the most important system variables, crystal diameter and crystal growth rate, is ensured. Additionally, a comprehensive discussion of the dependencies of the heater feedforward control on technological parameters, growth regimes and crystal shapes is given.

Nonlinear Control of Wind Turbines with Hydrostatic Transmission Based on Takagi-Sugeno Model

A nonlinear model-based control concept for wind turbines with hydrostatic transmission is proposed. The complete mathematical model of a wind turbine drive train with variable displacement pump and variable displacement motor is presented. The controller design takes into consideration the nonlinearity of the aerodynamic maps and hydrostatic drive train by an convex combination of state space controller with measurable generator speed and hydraulic motor displacement as scheduling parameters. The objectives are the set point control of generator speed and tracking control of the rotor speed to reach the maximum power according to the power curve in the partial-load region.

Nonlinear gearshifts control of dual-clutch transmissions during inertia phase

In this paper, a model-based nonlinear gearshift controller is designed by the backstepping method to improve the shift quality of vehicles with a dual-clutch transmission (DCT). Considering easy-implementation, the controller is rearranged into a concise structure which contains a feedforward control and a feedback control. Then, robustness of the closed-loop error system is discussed in the framework of the input to state stability (ISS) theory, where model uncertainties are considered as the additive disturbance inputs. Furthermore, due to the application of the backstepping method, the closed-loop error system is ordered as a linear system. Using the linear system theory, a guideline for selecting the controller parameters is deduced which could reduce the workload of parameters tuning. Finally, simulation results and Hardware in the Loop (HiL) simulation are presented to validate the effectiveness of the designed controller.

Design Serial Intelligent Modified Feedback Linearization like Controller with Application to Spherical Motor

Fuzzy logic controller (FLC) is an important nonlinear controller in an uncertain dynamic parameters. This controller is used to control of nonlinear dynamic systems particularly for spherical motor, because it has a suitable control performance and it is a stable. Conversely pure fuzzy logic controller is a high-quality intelligent nonlinear controller; it has two important problems; reliability and robustness in uncertain dynamic parameter. To increase the reliability and robustness, this research is focused on applied feedback linearization method in pure fuzzy logic controller. In this research the nonlinear equivalent dynamic (equivalent part) formulation problem in uncertain condition is also solved by combine pure fuzzy logic control and feedback linearization method. In this method feedback linearization theorem is applied to fuzzy logic controller to increase the stability, reliability and robustness, which it is based on nonlinear dynamic formulation. To achieve this goal, the dynamic-based formulation feedback linearization method is design. This method is robust and model-based nonlinear control therefore can reduce the nonlinearity term of system and reduce the effect of coupling. In this research MAMDANI fuzzy inference system is used as a main controller. It has minimum rule base to practical implementation. This technique was employed to obtain the desired control behavior with a number of information about dynamic model of system and a feedback linearization control was applied to reinforce system performance.

Affine Neural Network-Based Predictive Control Applied to a Distributed Solar Collector Field

This paper presents experimental results concerning the control of a distributed solar collector field, where the main objective concerns the regulation of the outlet oil temperature by suitably manipulating the oil flow rate. This is achieved by means of a constrained nonlinear adaptive model-based predictive control framework where the control action sequence is obtained by solving an open-loop optimization problem, subject to a set of constraints. The plant dynamics is approximated by an affine state-space neural network, whose complexity is specified in terms of the cardinality of dominant singular values associated with a subspace oblique projection of data-driven Hankel matrices. The neural network is first trained offline and subsequently improved through a recursive updating of its weights and biases, based on a dual unscented Kalman filter. The control scheme is implemented on the Acurex field of the Plataforma Solar de Almeria, Spain. Results from these experiments demonstrate the feasibility of the proposed framework, and highlight the ability to cope with time-varying and unmodeled dynamics, under the form of disturbances, and its inherent capability for accommodating actuation faults.

Nonlinear model-based control of combustion timing in premixed charge compression ignition

One of the major challenges in the control of advanced combustion modes, such as premixed charge compression ignition, is controlling the timing of the combustion event. A nonlinear model-based controller is outlined and experimentally shown to be capable of controlling the engine combustion timing during diesel premixed charge compression ignition operation on a modern diesel engine with variable valve actuation by targeting the desired values of the in-cylinder oxygen mass fraction and the start of injection. Specifically, the experimental results show that the strategy is capable of controlling the start of combustion and the intake oxygen mass fraction to within 1° crank angle and 1% respectively. A stability analysis also demonstrates that this control strategy ensures asymptotically stable error dynamics.

Non-linear model-based Control for the Human-inspired Robotic Exoskeleton (HuREx) gait trainer

Abstract A non-linear model-based control system for the Human inspired Robotic Exoskeleton (HuREx) gait trainer was developed. The model used for the controller was derived using a hybrid PMA model and dynamic model of the device. The performance of the control system was compared against a closed-loop PID control system in simulation. The trajectory tracking results show that the model-based control system improved the speed and accuracy of the system as well as being robust against modeling errors and disturbances.

H∞ fault detection for a class of T-S fuzzy model-based nonlinear networked control systems

In this paper, a robust H ∞ fault detection filter is designed for a class of discrete-time nonlinear networked control systems via T-S fuzzy model with multiple bounded state delay and random packet dropout induced by the limited bandwidth of communication networks. Our aim in this paper is to analyze and design a robust H ∞ full-order fault detection filter, such that the filtering error dynamics is asymptotically mean-square stable with a prescribed H ∞ performance level. Sufficient conditions for the existence of the desired filter are presented. Finally, an example is given to illustrate the effectiveness and applicability of the proposed new design method.

Feedforward-feedback control of an industrial multicomponent distillation column

The problem of regulating the distillate impurity content in a two-feed (paraffin-olefin) industrial multicomponent distillation column (iso-normal butane splitter) by manipulating the heat duty according to olefin-to-total feedflow load disturbance and tray temperature measurements is addressed. First, the detailed model-based nonlinear state-feedback robust control problem is addressed, identifying the solvability conditions and attainable closed-loop behavior. Then, the behavior of this controller is recovered with an output feedback PI temperature controller with dynamic feedforward setpoint compensation and antiwindup protection. The proposed approach is illustrated and tested through numerical simulations with a detailed model calibrated with industrial data, establishing the feasibility of improving the existing control scheme with PI temperature control plus manual setpoint compensation.

Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation

The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed.

Comparison of Nonlinear Flatness-Based Control of two Coupled Hydraulic Servo Cylinders

This paper presents two nonlinear model-based control designs for a hydraulic system that consists of two mechanically coupled hydraulic cylinders actuated each by a separate servo-valve. Based on a physically-oriented nonlinear mathematical model of the test rig, a further model simplification results in a completely controllable MIMO system. Two different control structures are discussed and compared to each other in this paper: First, a cascaded flatness-based control is designed, where fast inner control loops determine the difference pressure in each hydraulic cylinder, while the position as well as the generated force is controlled in the outer loop. In the second approach, a centralised flatness-based control, with the same outputs as in the outer loop of the cascaded approach, is developed for the MIMO system. Model parameter uncertainties are estimated by a reduced-order disturbance observer and compensated by the control algorithm. The efficiency of the proposed control structures is demonstrated by experimental results from a dedicated test rig.

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.

승용디젤엔진의 EGR, VGT 시스템을 위한 비선형 정적 모델 기반 피드포워드 제어 알고리즘 설계

This paper presents a feedforward control algorithm for the EGR and VGT systems of passenger car diesel engines. The air-to-fuel ratio and boost pressure are selected as control indicators and the positions of EGR valve and VGT vane are used as control inputs of the EGR and VGT controller. In order to compensate the non-linearity and coupled dynamics of the EGR and VGT systems, we have proposed a non-linear model-based feedforward control algorithm which is obtained from static model inversion approach. It is observed that the average modeling errors of the feedforward algorithm is about 2% using stationary engine experiment data of 225 operating conditions. Using a feedback controller including proportional-integral, the modeling error is compensated. Furthermore, it is validated that the proposed feedforward algorithm generates physically acceptable trajectories of the actuator and successfully tracks the desired values through engine experiments.

Multi-structural fast nonlinear model-based predictive control of a hydronic heating system

Buildings consume a significant amount of energy to maintain the indoor thermal comfort. One way to reduce the energy consumption in buildings is to improve the overall energy efficiency through integrated advanced controls. It is undoubted that incorporating a model and utilizing future information in real-time building operation offers great energy saving potentials. However, there are many barriers preventing this from happening. Building systems are nonlinear and multiple-input-multiple-output (MIMO) in nature. Conventional approach with a global search solver and a detailed model simulator or direct nonlinear model predictive control (MPC) incurs prohibitive computational cost. This paper proposes a multi-structural fast nonlinear model predictive control (MPC) for handling nonlinear building systems and applied it on a hydronic heating system. The methodology remains the advantages of linear classical MPC and solves the nonlinearity issues involved in thermal comfort and energy conservation oriented control. The simulation shows that the controllers can achieve the optimal solutions in less than five minutes for five days simulation in a full look-ahead scenario with a Duo T6400 2.0 GHz computer. The fastest scenario takes only thirty more seconds to accomplish, which makes the approach feasible for online implementation. The techniques of using MPC for achieving smooth day-night switch, band control, dynamic constraints, and dynamic weighting are discussed. The energy saving potential of applying the proposed MPCs is found to be between six to forty two percent. In addition, the techniques decouple the building system from the mechanical system and are applicable to other space conditioning systems as well.

Fast NMPC scheme of a 10 kW commercial PEMFC

This work highlights the gains of a fast nonlinear model-based predictive control (NMPC) scheme applied to a 10 kW proton exchange membrane fuel cell (PEMFC). The freshness of the approach is based on a particular parameterization of the control action to decrease the optimization problem dimension. Due to its short computational time, its reliability and its low sensitivity to noise, an artificial neural network (ANN) model is designed and used as a predictive model.The performance of the proposed control strategy is confirmed thanks to simulations through varying control scenarios. Results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. Moreover, for similar setpoint tracking accuracy, the proposed NMPC scheme appears to be thirty times faster than a classical NMPC strategy. Therefore, the fast NMPC scheme proposed in this work appears to be a promising candidate to achieve real-time implementation.