The parameterization of all robust stabilizing Internal Model Controllers for multiple-input/multiple-output plants

This paper presents an Internal model controller (IMC) for temperature control of heating, ventilation and air conditioning (HVAC) system. Firstly, the temperature of an air-conditioning system of the test room is controlled by using PID controller. The performance and stability of the control process are degraded due to the significant time delay of air-conditioning process. So, smith-predictor scheme is used to compensate the time delay. But when the process has larger system parameters and longer time delay, smith-predictor does not provide better results. To overcome this problem, an internal model (IMC) controller is developed. Simulation results compare the performance of internal model (IMC) controller with conventional PID controller and smith predictor controller. Simulation result shows that an internal model controller gives satisfactory performance with less overshoot and less settling time.

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.

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

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.

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

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).

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.

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.

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.

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.

In this study, we will present a novel Internal Model Control (IMC) based Self-Tuning (ST) mechanism to tune the Scaling Factors (SFs) of the fuzzy PID controllers in an online manner. Moreover, we will present a fuzzy PI-D (FPI-D) structure in order to eliminate the derivative kick and the effect of noise on the control signal. The proposed IMC based fuzzy ST mechanism is constructed by two Fuzzy Inference Systems (FISs) and an IMC based SF (IMC-SF) parameter regulator. The two FISs will predict the current values of the system parameters by using the system output value and then the IMC-SF parameter regulator will tune the SFs of FPI-D with respect to presented tuning method. The performance of the proposed Self-Tuning FPI-D (ST-FPI-D) will be evaluated on a realtime laboratory scale extruder process with its discrete implementation via the ABB PLC PM573 industrial controller. We will compare and examine the control system performance of the proposed ST fuzzy control structure with an IMC based tuned ABB-PID and FPI-D structures. The real-time experimental results will show that the proposed ST-FPI-D structure enhanced significantly the control performance for various operating points and in the presence of uncertainties and nonlinearities when compared to the ABB-PID and FPI-D structures.

In this paper, a single stage conversion method is used for the integration of the Solar Photovoltaic (SPV) with the utility grid. The three phase Voltage Source Converter (VSC) is used to extract the maximum power from the SPV. The power produced by the SPV changes with change in solar irradiance, hence a Maximum Power Point Tracking (MPPT) scheme is necessary to obtain the maximum power at a particular irradiance. In earlier works the conventional dq current control method is used. In this work the Internal Model Control (IMC) based current control is implemented. The IMC based controller has a simple design procedure and can be implemented using PI controllers. The Power produced by the SPV is used to supply the local load demand and the extra power, if any, is fed to the utility grid. If the load demand is more than the power produced by the SPV, the deficit power can be supplied by the grid. The SPV with IMC control will be validated under different irradiance level. The validation of the scheme is done using MATLAB/Simulink under different irradiance values.

This paper adopts the adaptive neural internal model control strategy to design a control system for complex and uncertain process with delay. Because of the shortage of the classic internal model control system the performance of the control system will slip back if there exist large errors between the real plant and the model, an adaptive mechanism based on single neuron which can tune the parameters of the internal model and that of controller in the control system on line is designed. The multimodel approach is exploited to represent the direct model by a model basis made up of five linear models, and to construct a controller trained by the corresponding reverse models. The simulation results shows that the adaptive internal model control system designed in this paper has good performance of overcoming deviations of model parameters.

Combining neural network identification technique and internal model control (IMC) strategy, a novel nonparametric design for nonlinear plants is presented. Based on this idea, a multivariable adaptive decoupling internal model controller (DIMC) is developed to deal with multivariable nonlinear coupling systems with unknown structure and parameters. A neural network is used to detect the unknown nonlinear internal model. One advantage is that the design does not require the computation of the inverse model of the IMC parameters, but only depends on system input-output data and neural network output.