Model-based Control Techniques Research Articles

Pedaling torque sensor-less power assist control of an electric bike via model-based impedance control

This paper proposes a method to assist human force acting on electric bike without using costly torque sensors via a model-based impedance control technique. In general, electric bikes are classified into two categories, i.e., pedelec electric bikes and throttle electric bikes. We focus on the system called a pedelec electric bike. It assists human pedaling force using the pedaling information, e.g., pedaling force or speed. To obtain the human’s pedaling information in real-time, it needs physical sensors such as a torque sensor and a velocity sensor. But, these sensors are expensive and weak against external loads. Also, since these sensors are fixed directly to the forced component in a bike system, there are the risks of damage. For these reasons, sensor-less control methods based on a disturbance observer have been studied so far. In this paper, we have proposed a pedaling torque sensor-less power assist method and have applied it to the experimental pedelec electric bike. A power assist control algorithm, designed by employing an impedance model, consists of a PI-type feedback controller, an inverse model-based feedforward controller, and a pedaling torque observer. Finally, we performed experiments and confirmed the effectiveness of a proposed power assist control method.

A comparative study of two model-based control techniques for the industrial manipulator

SUMMARYIn this paper, design, analysis and real-time trajectory tracking control of a 6-degree of freedom revolute spherical-spherical type parallel manipulator, actuated by six hybrid stepper motors, has been studied. Two different control approaches have been used to improve the trajectory tracking performance of the designed manipulator. The first approach considered a single input-single output (SISO) linear quadratic regulator (LQR) for trajectory tracking control of the manipulator. Another controller type based on a nonlinear sliding mode controller method has been utilized to take decoupled dynamic approximation model of the manipulator into account and to improve tracking performance of the manipulator. Real-time experimental results for the two different control techniques have been verified. Finally, according to the results, the nonlinear sliding mode controller method has improved the tracking performance of the designed manipulator.

Cascaded Dual-Model-Predictive Control of an Active Front-End Rectifier

Model-predictive control (MPC) is an advanced model-based control technique. It allows flexible control schemes with fast time responses. However, like most optimization-based control strategies, MPCs are usually computationally intensive and suffer from a lack of robustness toward parametric variations. The computational burden of these controllers is nowadays being relieved thanks to the progress of digital devices; however, the robustness is still a major issue that prevents the use of MPC controllers in some applications. Systems presenting slow and fast dynamics at the same time are usually controlled by cascaded control loops. MPC techniques are restricted to the control of inner loops, while outer loops use more robust control techniques. The aim of this paper is to provide MPC techniques for both the inner and outer loops of an active front-end rectifier. A first dual-MPC cascaded control shows the drawbacks of usual MPC techniques for outer loops. It is thereafter modified to achieve a robust cascaded dual-MPC that eliminates the variable resistive load parameter from the system dynamical model and formulates a new prediction model containing only known parameters and measurable quantities. Finally, a third method based on the previous one is also presented in order to reduce switching losses. Experimental results for the three methods are presented and compared with double precision offline software simulation results to validate the feasibility of the proposed techniques.

Port-Hamiltonian Systems in Adaptive and Learning Control: A Survey

Port-Hamiltonian (PH) theory is a novel, but well established modeling framework for nonlinear physical systems. Due to the emphasis on the physical structure and modular framework, PH modeling has become a prime focus in system theory. This has led to a considerable research interest in the control of PH systems, resulting in numerous nonlinear control techniques. General nonlinear control methodologies are classified in a spectrum from model-based to model-free, where adaptation and learning typically lie close to the end of the range. Various articles and monographs have provided a detailed overview of model-based control techniques on PH models, but no survey is specifically dedicated to the learning and adaptive control methods that can benefit from the PH structure. To this end, we provide a comprehensive review of the current learning and adaptive control methodologies that have been adapted specifically to PH systems. After establishing the required theoretical background, we elaborate on various general machine learning, iterative learning, and adaptive control techniques and their application to PH systems. For each method we highlight the changes from the general setting due to PH model, followed by a detailed presentation of the respective control algorithm. In general, the advantages of using PH models in learning and adaptive controllers are: i) Prior knowledge in the form of PH model speeds up the learning. ii) In some instances new stability or convergence guarantees are obtained by having a PH model. iii) The resulting control laws can be interpreted in the context of physical systems. We conclude the paper with notes on open research issues.

Modeling and validation of turbocharged diesel engine airpath and combustion systems

The ultimate aim of this study is the development of an engine modeling approach that would facilitate the design of model-based control techniques for diesel engines. This will allow for the development of more generalized, modular control strategies for different engine types and sizes as opposed to the commonly practiced map-based engine control strategies that depend on maps and feedforward control and require lengthy modifications every time a change is made. Also, most engine modeling studies focus on either airpath or combustion systems, treating these models and their validation individually, and not as an integrated system as is actually the case. To address the need for more realistic models suitable for model-based control design, this study develops a combined airpath and combustion model for the engine, using analytical models wherever possible and derives a model with appropriate control inputs and outputs that could be used in a control scheme. The inclusion of the actuator dynamics of the Exhaust gas recirculation (EGR), variable geometry turbine (VGT), and Throttle (THR) valves in the airpath model and the consideration of nonlinearities in the combustion model allow for the development of a more thorough engine model, as well as the validation of subsystems and the whole integrated engine model using a complete World Harmonized Transient Cycle (WHTC). This test cycle finds limited use due to its challenging transients, and yet, is the demanded test cycle for emission regulations nowadays. These are unique aspects of this modeling study, the results of which indicate that the developed engine model could be used in control design and hardware-in-the-loop simulation (HILS) based engine control prototyping.

Intelligent Fixtures for Active Chatter Control in Milling

The mitigation of chatter vibrations in milling has collected the interest of several researches in the last decades. One of the most industrially oriented alternatives is represented by active fixtures, complex mechatronic devices capable of actuating the workpiece during machining operations, with the purpose of stabilizing the process by generating counteracting vibrations. Most of the previous works show different fixture architecture and model based control techniques. This paper deals with the development and testing of such an active fixture, presenting the main design aspects and the features of the black-box control-logic used. Experimental tests are presented to show the achievable chatter mitigation.

Model-Based Predictive Control Using Differential Evolution Applied to a Pressure System

Model-Based Predictive Control techniques have been applied industrially since the 1970s, presenting favorable characteristics. Among them, it can be mentioned the treatment of process constraints and optimization considering the output error with respect to the reference and the used energy. Usually, formulations based on linear models of the plant are used. However, the use of linear models for nonlinear plants can result in control loops with limited or even reduced performance. In this paper, the use of a Model-Based Predictive Control approach using a Differential Evolution algorithm for optimization applied in a pressure control system is presented. The results show the advantages found by using such nonlinear formulation. The methodology used, with comments about the obtained results and conclusions on this line of research are presented.

Towards a generic control-oriented model for HEV predictive energy management

The increased interest in model-based control techniques for the energy management of hybrid electric vehicles has led to a focus on generic modeling in order to allow an effective integration in model-based design procedures. An architecture-free model ensures modularity with a view to control design. Under mild assumptions, a generic model for the torque and the rotational speed of a hybrid electric vehicle is proposed in this article. In addition, the mathematical formulation of the fuel consumption minimization and the intrinsic problem of torque split are addressed. In order to illustrate its ability to handle the constraints, a model predictive control formulation is presented and applied to a dual-clutch transmission hybrid architecture.

Model-Based Engine Control

Abstract This article focuses on control-oriented engine modeling and model-based engine control techniques. The engine modeling research is centered on the engine combustion process. Multi-zone, three dimensional computational fluid dynamics (CFD) models, with detailed chemical kinetics are able to precisely describe the thermodynamics, fluid and flow dynamics, heat transfer, and pollutant formation of the combustion process. The simplified one-dimensional combustion models have also been implemented into commercial codes such as GT-Power and Wave. However, these high fidelity models cannot be used for model-based control since they are too complicated to be used for real-time computing. Crank-resolved engine air handling system modeling is also important for describing the in-cylinder charge-mixing process. Therefore, for model-based control and real-time hardware-in-the-loop simulations, it is necessary to have a crank-resolved engine model with its complexity intermediate between the time-based mean-value and one-dimensional CFD models.

Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].

An Optimal Model-Based Control Technique to Improve Wind Farm Participation to Frequency Regulation

This paper presents a model-based control technique to provide the contribution of wind power generators to primary frequency regulation in electric power systems. Models of individual wind power generators and wind farm (WF) as a whole are presented and the proposed control strategy is detailed. It consists of a central controller, a central Kalman filter (KF), and some local KFs, one for each wind turbine. The central controller is disabled in normal operation conditions and its task is to set the power reference for each wind turbine, overwriting the local reference, when a disturbance occurs. Central KF is in charge of estimating the external load variation, while each local KF estimates wind speed and the wind turbines dynamical state. The key feature of this approach is that each wind turbine can react to grid disturbances in a different way, which depends on wind speed as seen by the wind turbine itself and by its dynamical conditions. Real wind data and a large WF connected to the grid in a dedicated simulation environment have been used to test the effectiveness of the proposed control strategy.

Application of the Min-Projection and the Model Predictive Strategies for Current Control of Three-Phase Grid- Connected Converters: A Comparative Study

This paper provides a detailed comparative study concerning the performance of Min-Projection Strategy (MPS) and Model Predictive Control (MPC) systems to control the three-phase grid connected converters. The MPS approach is already applied in synchronous reference frame by authors. However in this paper the algorithm is modified to be realized in the stationary reference frame which brings considerable simplicity and ease of implementation and then the MPS is compared with MPC. To do so, first, the converter is modeled as a switched linear system. Then, the feasibility of the MPS technique is investigated and its stability criterion is derived as a lower limit on the DC link voltage. The mathematical analysis reveals that the MPS is independent of the load, grid, filter and converter parameters. This feature is a great advantage of MPS over the MPC approach. However, the MPC is a mature model-based control technique, which has been already developed for controlling the VSC in the stationary reference frame. For comparing, both MPS and MPC approaches are simulated in the PSCAD/EMTDC environment. Simulation results illustrate that the MPS works well and is less sensitive to grid and filter inductances as well as the DC link voltage level. However, the MPC approach offers slightly a better performance in the steady state conditions.

ProOpter: An advanced platform for production analysis and optimization

This paper presents the prototype of an advanced platform for production analysis and optimization, referred to as ProOpter. The platform was developed to support the recently derived concept of holistic production control (HPC), which relies on model-based control. The prototype is comprised of a set of off-line and on-line modules. The off-line modules support the definition of key performance indicators (KPIs), the selection of the most influential input (manipulative) variables, and the identification of a simple production model from historical data. The on-line modules enable KPI prediction and suggest actions to the production manager, employing model-based production control and/or optimization techniques. In this way, a new decision-support reasoning based on historical production data can be introduced. ProOpter has a modular design and can be used as an add-on to existing production IT systems since it relies on established industrial communication standards. The use of the platform is validated on the well-known Tennessee Eastman benchmark simulation process and on two industrial case studies.

Drug Interaction Between Propofol and Remifentanil in Individualised Drug Delivery Systems

Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over-dosing or under-dosing of the patient. Advanced model based control techniques are able to predict and regulate the amount of drugs given to the patient but they rely heavily on patient model. This paper discusses and investigates the effects of synergistic drug interaction between Propofol (hypnotic) and Remifentanil (opioid) and its requirements on the robustness and stability of the closed loop system.

Survey of Unmanned Helicopter Model-Based Navigation and Control Techniques

Unmanned Aircraft Systems(UAS) have seen unprecedented levels of growth during the last two decades. Although many challenges still exist, one of the main UAS focus research areas is in navigation and control. This paper provides a comprehensive overview of helicopter navigation and control, focusing specifically on small-scale traditional main/tail rotor configuration helicopters. Unique to this paper, is the emphasis placed on navigation/control methods, modeling techniques, loop architectures and structures, and implementations. A `reference template' is presented and used to provide a basis for comparative studies and determine the capabilities and limitations of algorithms for unmanned/autonomous flight, as well as for navigation, and control. A detailed listing of related research is provided, which includes model structure, helicopter platform, control method and loop architecture, flight maneuvers and results for each. The results of this study was driven by and has led to the development of a `one-fits-all' comprehensive and modular navigation controller and timing architecture applicable to any rotorcraft platform.

Robust penalty adaptive model predictive control (PAMPC) of constrained, underdamped, noncollocated systems

This paper investigates the control challenges posed by noncollocated mechatronic systems and motivates the need for a model-based control technique towards such systems. A novel way of online constraint handling by penalty adaptation (PAMPC) is proposed and shown to be of particular relevance towards robust control of underdamped, noncollocated systems by exploiting the structure of such systems. Further, a new tunneling approach is proposed for PAMPC to maintain feasibility under uncertainty. The PAMPC is shown to be optimal for control of a benchmark mass–spring–damper system, which poses all the mentioned challenges.

A measurement-based technique for designing fixed-order RST controllers and application to a coupled water tank system

This paper addresses the control design problem for unknown linear single-input single-output systems using a set of measurements. In standard control methods, controllers are designed on the basis of a mathematical model. Such a mathematical model, which describes the behavior of the system, can be developed using either physical laws or measured data. However, due to the complex dynamics of many physical systems, some prior assumptions are usually made to build simplified models. The efficacy of such model-based control techniques depends greatly on the quality of models used. Hence, data-based control design methods appeared as an alternative to model-based methods. Such data-based techniques are powerful in the sense that no mathematical model is needed for controller design. In this paper, we propose an approach that uses frequency response data to directly design controllers without going through any modeling stage. The main idea of our proposed method is to design polynomial RST controllers, for which the closed-loop frequency response fits a desired frequency response that describes some desired performance specifications. This problem is formulated as an error minimization problem, which can be solved using efficient optimization algorithms. The main feature of our proposed control approach is that it enables the designer to pre-select the controller structure, which allows the design of low-order controllers. Moreover, this control design approach does not depend on the increasing order and complexity of the system. An application to water level control of a coupled tank system is presented to validate and illustrate the efficacy of the proposed approach.

Physical feasibility of robot base inertial parameter identification: A linear matrix inequality approach

Identification of robot dynamics is a key issue in boosting the performance of model-based control techniques, having also a key role in realistic simulation. Robot dynamic parameters have physical meaning, hence parameter estimations must correspond to physically feasible values. Since it is only possible to identify linear combinations of parameters (the base parameters) the physical feasibility of such combinations cannot be directly asserted. In this paper we show that feasibility conditions define a convex set and can be written as a linear matrix inequality (LMI), suitable for semidefinite programming (SDP) techniques. We propose three methods based on LMI–SDP to deal with the feasibility of base parameters. The first method checks if a given base parameter estimation has physical meaning. The second method corrects infeasible estimations, finding the closest feasible base parameters. These two methods can be applied to existing regression techniques. The third method performs parameter identification through ordinary least squares regression constrained to the feasible space, guaranteeing the optimal solution. Experiments with a seven-link robot manipulator are provided, and efficiency and scalability are discussed.

Fuzzy Stabilization for Nonlinear Discrete Ship Steering Stochastic Systems Subject to State Variance and Passivity Constraints

For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.

Model-based control of networked distributed systems with multi-rate state feedback updates

This paper presents a model-based multi-rate control technique for stabilisation of uncertain discrete-time systems that transmit information through a limited bandwidth communication network. This model-based multi-rate approach is applied to two networked architectures. First, we discuss the implementation of a centralised control system with distributed sensing capabilities and, second, we address the problem of stabilisation of networks of coupled subsystems with distributed sensors and controllers. In both cases, we provide necessary and sufficient conditions for stability of the uncertain system with multi-rate model updates. Furthermore, we show that, in general, an important reduction of network bandwidth can be obtained using the multi-rate approach with respect to the single-rate implementations. Finally, an extension is provided that addresses restricted access to the communication channel.