Internal Model Controller Design for HVAC System

A two degree freedom internal model control (IMC) is implemented for the temperature control of a single tubular heat exchanger system. The experimental heat exchanger system is a very simple structure, however, the characteristics are regarded as a distributed parameter system and time delay made by software; the plant is regarded as complex system. With the aim of applying IMC structure, we approximate the heat exchanger system to a time delay model from a frequency response examination. In the laboratory experiment, it is clear that: (1) the two degree of freedom IMC is effective for the time delay system; (2) the two degree of freedom performance of IMC was superior to one degree freedom IMC; the disturbance was reduced compared to a PI controller or one degree of freedom IMC design; (3) since the controller had two degrees of freedom, the one tuning filtering parameter of the IMC controller effected the tracking of the reference and the other tuning parameters of IMC reduce disturbance; (4) the design method was as simple as an ordinary one degree of freedom IMC design; and (5) since the IMC controller was constructed from the system model, there were some problems to set up the modeling and identification.

Combined internal model and inferential control of a distillation column via closed-loop identification

This chapter considers the design of a fractional order (FO) internal model controller (IMC) for first order plus time delay (FOPTD) processes to satisfy a given set of desired stability margins in terms of gain margin \((A_m)\) and phase margin \((\phi _{m})\). The highlight of the design is the choice of a fractional order (FO) filter in the IMC structure which has two parameters (\(\lambda \) and \(\beta \)) to tune as compared to only one tuning parameter (\(\lambda \)) for traditionally used integer order (IO) filter. These parameters are evaluated for the controller so that \(A_m\) and \(\phi _{m}\) can be chosen independently. This is the first time when the IMC controller is designed without any approximation of the delay in the IMC which has always been approximated using Taylor’s series approximation or Pade approximation. Simulation studies are carried out for three different FOPTD processes and the proposed methodology is validated experimentally on a DC servo-motor system. The results are compared with different well-known IMC techniques and the performance of controllers is evaluated in terms of rise time (\(T_r\)), settling time (\(T_s\)), maximum overshoot (\(\% M_p\)) and integral square error (ISE).

This review paper aims to present a comprehensive overview of the evaluation, simulation, and control of heating, ventilation, and air conditioning (HVAC) systems in nuclear power plants (NPPs), specifically highlighting their importance in maintaining operational safety, thermal performance, and energy efficiency. The study’s authors summarize recent developments in HVAC technologies, such as passive cooling systems, data-driven energy management frameworks, and intelligent control strategies, to cope with the specific challenges of NPPs. Various passive cooling systems, including heat pipes, thermosyphons, and loop heat pipes, have proven themselves by their ability to remove residual heat from spent fuel pools and reactors power plants with high efficiency. Through experimental studies, they have shown their ability to eliminate operational vulnerability to accidents or guarantee any desired long-term cooling. Intelligent sensor networks allow a more data-driven approach to HVAC control, enabling online energy management frameworks and advanced intelligent control systems. These exhibit considerable promise for optimizing HVAC performance, decreasing energy consumption, and improving operational flexibility in multi-zone systems. Such capabilities are ideal for addressing the dynamic and safety-critical nature of NPPs. They are first enabled by the use of these technologies for real-time monitoring, predictive maintenance, and adaptive control. When applied with advanced HVAC control systems, passive cooling techniques provide an exciting route to improve safety and energy efficiency. An overview of the key findings is that robust thermal management solutions combined with intelligent control and intelligent adaptation are essential when addressing the rapidly evolving demands of nuclear energy systems. This work highlights the priorities in the next generation of nuclear power plants, which should actively pursue seamless integration of out-of-system technologies into existing NPP infrastructures, enabling scalable, cost-effective, and resilient solutions.

In the present paper, we examine the parameterization of all robust stabilizing Internal Model Controllers (IMCs) for multiple-input/multiple-output plants with uncertainties. The parameterization problem is the problem in which all stabilizing controllers for a plant are sought. Since this parameterization can successfully search for all proper stabilizing controllers, it is used as a tool for many control problems. For stable plants, the parameterization of all stabilizing controllers can be represented by the IMC structure. The IMC structure has advantages such as closed-loop stability is assured simply by choosing a stable IMC parameter. Additionally, closed-loop performance characteristics are related directly to controller parameters, which make online tuning of the IMC very convenient. However, there exists a question whether or not, stabilizing controllers for unstable plants can be represented by the IMC structure. The solution to this problem, Morari and Zafiriou, Chen et al. and Mai et al. clarified that any stabilizing controller for unstable plants can be represented by the IMC structure by the parameterization of all stabilizing IMCs for unstable plants. However, their IMCs cannot guarantee the stability of control system for plants with uncertainties. In this paper, we clarify the parameterization of all proper robust stabilizing IMCs for multiple-input/multiple-output plants such that the IMC and the internal model are proper.

As an important part of aesthetic education, music is the artistic expression of human emotions. It has a crucial impact on the cultivation of individual music perception ability, music aesthetic ability, music creation ability, etc. The degree of influence of emotions and values in music runs through the individual. This study introduces a method to independently design a predictive controller using the theoretical framework of the state-space method. Since the same results as DMC can be derived, it can be regarded as an understanding of DMC in the time domain. We take the asymptotically stable object of SISO as the model and use the step response coefficient to directly model, but this method makes the control uncontrollable, so we use the method of adding the correction parameter to make the observable process more effective. The advantages of one-time online optimization can reduce the amount of calculation and greatly enhance the robustness in the control process. In order to study the dynamic characteristics of the system, an internal model control system is introduced. By studying the closed-loop stability of the internal model system, the internal model structure of the dynamic matrix control system is studied, and its dynamic characteristics are further studied. When analyzing the dynamic characteristics, we can know that the dynamic characteristics of the system only depend on the object characteristics and control strategies, and have nothing to do with the model length. This study introduces the basic structure, main properties, and design method of the internal model controller. On the basis of conventional internal model control, an enhanced internal model control method is introduced. Through the analysis of the sensitivity function and the complementary sensitivity function, the robust performance and robust stability of the enhanced internal model control system are studied. Music learning psychology is related to the effect of music teaching. Since individual psychological development is dynamic and controllable, by grasping students’ music learning motivation and music psychological ability, we can well practice the concept of contemporary new curriculum standards and implement high-quality music courses. This study mainly discusses the music learning psychology itself, the analysis of real-time measurement results, and the analysis of teaching countermeasures, and does some research on music theory and teaching practice. Through real-time measurement research, it can be concluded that the real-time measurement results are relatively optimistic, which shows that teachers can maintain students’ learning well as long as they use teaching methods that conform to students’ learning psychology. The nonlinear dynamic incremental internal model algorithm can improve students’ various music psychological abilities and achieve the expected music teaching goals.

In this paper, a new grid current control strategy based on internal model control and repetitive control is designed. The performance characteristic and operation principle are analyzed according to the basic principle of internal model control and the dynamic mathematical model of three-phase photovoltaic grid-connected inverters. Because the harmonic current caused by the dead-time effect and the periodic disturbance of the grid can not be eliminated by internal model control, a composite control strategy with internal model control and repetitive control is proposed in the paper. Internal model control can simplify the controller’s parameters and ensure the dynamic quality of the system. Repetitive control is able to restrain the dead-time effect and the periodic disturbance of the grid. It can also reduce the total harmonic distortion of the grid-connected current. The simulation results show that the proposed control strategy has good dynamic and steady-state performance and can restrain the periodic disturbance as well.KeywordsThree-phase grid-connected invertersInternal model controlRepetitive controlDead-time effect

It is a challenge to design a satisfactory controller for a complex multivariable industrial system with minimal offsetting and a slow response. An internal model control method is proposed for rank-deficient systems with a time delay based on a damped pseudo-inverse. An internal model control was designed to obtain the desired dynamic characteristics of the system by transforming the time-delay system into a system without a time delay, following the Pade approximation approach. By introducing a damping factor, the internal model controller was designed based on a damped pseudo-inverse, since the inverse matrix of the rank-deficient system does not exist. Furthermore, a singular value decomposition was used to analyze the steady-state performance of the system. The selection of the damping factor was also presented, and a μ analysis was made to evaluate the stability of the system. To demonstrate the effectiveness of the proposed method, a crude distillation process with five inputs and four outputs was considered as an example. The simulation results illustrate that not only can the proposed strategy guarantee the system’s stability, but it also has a relatively good dynamic performance.

The internal model control (IMC) scheme has been widely applied in the field of process control. This is due to its simple and straightforward controller design procedure as well as its good disturbance rejection capabilities and robustness properties. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of the linear design procedures can be exploited directly. The resulting controllers are comparable to gain-scheduled PI or PID controllers which are the standard controllers in process industry. In practice, the tuning of conventional PI or PID controllers can be very time-consuming. In this paper, the design effort of the nonlinear IMC and conventional controller design methods are discussed and the control results are compared by applying it to a Hammerstein process and nonlinear temperature control of a heat exchanger.

In order to improve the servo accuracy of hydraulic quadruped robot electro-hydraulic servo actuators during operation, the load characteristic of equivalent model of electro-hydraulic servo actuators with swing phase and support phase was analyzed, respectively, the compound control strategy including flow compensator, velocity compensator, internal model controller, and improved internal model controller was proposed, the dynamic performance of system was guaranteed by internal model controller, the accuracy was further improvement by improved internal model controller. The composite control strategy was verified by robot single leg test platform, the experiment results show that the deviation and phase lag of response curve with flow compensation, velocity compensation and internal model controller was less than ±5% and 18°, respectively, the value of phase lag can be further reduced to 7°by the improved internal model controller, and the efficiency of the proposed control strategy was verified.

Notice of Violation of IEEE Publication Principles<br><br>"Internal model control based on locally linear model tree (LOLIMOT) model with application to a PH neutral process"<br>by M. Jalili-Kharaajoo, A. Rahmati and F. Rashidi,<br>in proceedings of the 2003 IEEE Internationa Conference on Systems, Man, and Cybernetics, vol 4, pp. 3051-3-55.<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"Nonlinear Internal Model Control for MISO Systems Based on Local Linear Neuro-Fuzzy Models"<br> by A. Fink, O. Nelles and R. Isermann,<br>in 15th IFAC World Congress, Barcelona, Spain, 2002.<br><br> <br/> The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to conventional multi layer perceptron (MLP) networks. In practice, the tuning of conventional MLP based controllers can be very time-consuming whereas the IMC design procedure is very simple and reliable. The design effort of the IMC based on locally linear model tree (LOLIMOT) algorithm are discussed and the control results are compared by application to nonlinear control of an industrial-scale PH neutralization process. Simulation studies of a PH neutralization process confirm the excellent nonlinear modeling properties of the proposed locally linear network and illustrate the potential for set point tracking and disturbance rejection within an IMC framework.

A systematic internal model control (IMC) controller design methodology has been developed for various types of multivariable processes. When we try to apply IMC to various systems several implementation problems are encountered. In this paper, we resolve these problems and suggest a systematic IMC controller design methodology. IMC shows very good performance and is easy to tune for open-loop stable systems. For unstable systems we apply IMC after stabilizing the systems using the pole placement technique. A combination of quadratic programming and IMC can handle constraints on manipulated and controlled variables.

The internal model control (IMC) proportional integral derivative (PID) controller tuning rules provide an excellent tracking of set-point, but sluggish disturbance rejection, as the conventional IMC filter introduces slow process pole. Disturbance rejection is significant than set-point tracking in many industrial applications. This paper proposes the design of IMC-PID controller, cascaded with lead–lag filter, with an improved IMC filter to provide effective disturbance rejection and robust operation of the first-order processes (FOPs). The simulation study was conducted to show the effectiveness of the proposed method on various structures of the FOPs, calculating the controller parameters, maintaining similar robustness in terms of maximum sensitivity MS. Perturbations were incorporated in the parameters of the plant model simultaneously, for the robustness analysis of the controller. The closed-loop performance was evaluated with the integral error criteria, namely, integral absolute error, integral square error, and integral time absolute error. The proposed IMC filter provided good disturbance rejection. The results of recently published design methods were compared with that obtained from the proposed method, validating the usefulness of the proposed method.

ABSTRACTThe paper proposes a novel method for the nonlinear redundantly actuated parallel robot based on force/position hybrid control structure. In order to solve the limitation of making a compromise for internal model controller, a two-DOF fractional order internal model control algorithm combining the internal model control principle and the fractional order theory is proposed for the position branch of the parallel robot redundantly actuated. This algorithm can realise the adjustment of the dynamic performance and anti-interference of 6PUS-UPU respectively. Aiming at the big force control error fractional order internal model, fuzzy control theory and the fractional order internal model controller are integrated into a new controller-fuzzy fractional order internal model(FFOIM) force control algorithm. Then Admas/Matlab simulation results demonstrate that the proposed algorithm can further reduce the driving force error of the system, and also retain the strong anti-interference of fractional order internal model controller.

A stable Kalman filter predictor (KFP) is developed which generates minimum variance estimates of the future outputs {y(t + i | t), i = 1, … d} of stochastic, single‐input/single‐output processes with time delay, d. The predicted outputs are used for time delay compensation and in the design of a predictive feedback controller. An innovation model analysis is used to convert the state space formulation to transfer function form and to show the relationship between the KFP, the Smith predictor, and the internal model controller. A modified KFP includes a disturbance model, and eliminates offset due to deterministic disturbances (e.g., steps) and modeling errors. Simulation results show that the modified KFP also predicts the disturbances and gives significantly better performance than the Smith predictor, particularly in the presence of process and measurement noise.