Internal Model Control System Research Articles

Modified modeling and internal model control method of thrust ripples in PMLSMs for ultraprecision air-bearing linear feed systems

With the aim of attenuating thrust ripples in ultraprecision air-bearing linear feed systems, a simple and effective internal model control (IMC) system is herein presented based on a modified modeling and identification method of permanent magnet linear synchronous motors (PMLSMs). First, flux linkage equations were used to describe the volt–ampere characteristics of PMLSMs and provide a thrust expression in a differential form based on the principle of virtual work. Then, an analytical formula was obtained through derivation based on the condition of field-oriented control. On this basis, an IMC compensator was designed using an inverse model to attenuate nonlinear thrust effects, and an extended Kalman filter (EKF) was adopted to estimate the actual current in an analog linear amplifier. The boundedness of the nonlinear system was analyzed based on the convergence condition; furthermore, the stability of the proposed IMC compensator with EKF could be guaranteed. Then, an accurate model and thrust parameters were obtained using an identification experiment on an air-bearing linear platform driven by a linear analog amplifier. To demonstrate the coupling effect of current on thrust and confirm the effectiveness of the proposed method, both simulations and experiments were performed at a uniform velocity under extra loads of different weights; then, an extended state observer (ESO) was introduced as a comparative method. Finally, the results of the following error indicated that the proposed method demonstrated improved performance in terms of overcoming the influence of nonlinearity and that it results in smooth velocities in steady states.

Youlaパラメータをもつ制御器に対するVirtual Internal Model Tuning

In this paper, we propose tuning methods of free parameters for control systems with stabilizing compensators. Here, we study a generalized internal model control system (GIMC). This control systems are generally designed based on a nominal models. However, it is difficult to redesign them in case of a nominal model have modeling errors between the original one. Therefore, we consider tuning the free parameters in this control systems using Virtual Internal Model Tuning (VIMT) which is one of the data-driven control. At the end of this paper, we verify the usefulness of the proposed method using numerical examples.

Optimisation of Internal Model Control Performance Indices for Autonomous Vehicle Suspension

Autonomous vehicles (AVs) have grown in popularity and acceptability due to their unique capacity to reduce pollution, road accidents, human error, and traffic congestion. Vehicle suspension is an important component of a car chassis since it affects the performance of vehicle dynamics. As a result, enhancing suspension performance and stability is critical in order to achieve a more pleasant and safer car. Although there are several suspension control methods, they all suffer from fixed gain characteristics that are prone to nonlinearities, disturbances, and the inability to be tuned online. This research provides a comparison of Internal Model Control (IMC) performance metrics for vehicle suspension control. The IMC approach was tuned using the Genetic Algorithm and the Particle Swarm Optimisation algorithms. The performance of each of these schemes was analysed and compared in order to determine the approach with the best performance in terms of AV suspension control. The performance of the system response was compared to that of the traditional IMC. According to the comparison analysis, the optimized IMC systems had lower IAE, ITAE, ISE, rising time, and settling time values than the traditional IMC. Furthermore, there were no overshoots in any of the controllers.

Music Psychological Meta-Analysis and Real-Time Measurement Algorithm Based on Nonlinear Dynamic Incremental Internal Model Algorithm

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.

Optimal System for Improved Internal Model Control of Argon-Oxygen Decarburization Process Based on the Piecewise Linear Model and Time Constant of Filter Optimization

This paper presents an improved internal model control system to raise the efficiency of refining low-carbon ferrochrome. This control system comprises of a piecewise linearized transfer function and an improved internal model controller based on optimized time constant of the filter. The control system is mainly used to control the oxygen supply rate during the argon-oxygen refining for controlling the smelting temperature. The regulatory performance and servo of two closed-loop control schemes are compared between the improved internal model controller based on the optimized filter time 0000-0002-7606-6546and the internal model controller based on the fixed filter time constant. The simulation analysis shows that the piecewise linearized model and the optimization of the time constant of the filter improves the response time, stability, and anti-interference ability of the controller. Then, the proposed improved internal model controller is used to adjust the gas supply flow in 5 ton AOD furnace to control the smelting temperature. Ten production tests performed the effectiveness of the controlling refining optimal system. The analysis of the experimental data shows that the improved internal model control system can shorten the melting time and improve the melting efficiency. Thus, the application of the improved internal model control system in low-carbon ferrochrome refining is an interesting potential direction for future research.

Two-harmonic Periodic Disturbance Compensation for Dead-Time Processes with Online Identification of Input Delay

As in the case of the Smith predictor, when applied to dynamical systems with delay in the input, the stability of the internal model control (IMC) system can be very sensitive to the mismatch that can occur between the plant delay and the identified delay. In this paper, we propose an online identification technique for the plant with input delay, which is used to adapt an IMC controller designed to reject periodic disturbances consisting of two harmonics. The proposed method is based on wavelet analysis and cross-correlation of the measured output. Moreover, the proposed controller structure introduces a novel analytical approach which makes it suitable for the adaptive application. The practical motivation of the study is to address the delay mismatch that can be caused by approximating time-varying high-order systems by first-order models with input delay. The approach is numerically validated.

Improved internal model control method for carbon content of argon–oxygen refining ferrochromium alloys

We propose an improved internal model control method for the carbon content in the production process of argon–oxygen refining ferrochromium alloys. In the actual process, the control output is restricted and cannot fully demonstrate the control effect. Therefore, by decomposing the traditional internal model controller, we achieved saturation compensation, overcoming this limitation. In addition, by referring to the current production process, the smelting stages with different carbon-content ranges were determined. Combined with the actual equipment conditions, an anti-saturation internal model controller structure and a controller saturation numerical expert system were constructed, forming a fully improved internal model control system. The proposed method fully utilizes the steady-state error-free characteristic of the internal model control, achieving the preset control goal and significantly shortening the smelting time and improving the smelting efficiency.

Design of an Internal Model Control System based on the Database-Driven Modeling for a Hydraulic Excavator

In recent years, the Digital Transformation (DX) has been promoted by the Japanese government. New value is expected to be created by utilizing new digital technologies and data through DX. In particular, the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) is developing a data integration platform that allows easy access to data and models in the cloud. On the other hand, Internal Model Control (IMC) based on the memory-based modeling (MBM) using data and model has been studied. It can be able to adjust control and internal model parameters adaptively using real-time data of the control system. However, the control performance might be deteriorated by this modeling method, because it can not calculate appropriate parameters when new system input and output data is not involved in the database. This paper presents a new design manner of an IMC system based on the database-driven modeling. The effectiveness of the proposed method is verified by the simulation that is assumed work of the hydraulic excavators.

A multiple-model based internal model control method for power control of small pressurized water reactors

Small pressurized water reactors (PWRs) usually have variable operating conditions and complex operating environments. It is significant to design a robust control system to ensure stable and satisfactory reactor power maneuvers under large uncertainties. This paper purposes a multiple-model based internal model control (IMC) method for power control of small PWRs. Six local transfer function models are first developed from a nonlinear reactor core model over the entire operating range. Then, six local power controllers are designed for a small PWR based on these local models using the IMC scheme. The IMC filter time constant is determined by the genetic algorithm based multi-objective optimization to keep good balance between the control performance and cost. The local controllers are connected by a probability-weighted multiple model approach to generate a global IMC system for the reactor. Dynamic simulation results of the small PWR under the IMC scheme as well as performance assessments of the IMC scheme against the PID control scheme during typical transient operations have shown good reference tracking performances and strong disturbance rejection capabilities of the multiple-model based IMC system, demonstrating the feasibility and effectiveness of the proposed method for the power control of small PWRs.

Active noise control for a washing machine

An actively controlled thin barrier may provide better noise reduction results than common passive solutions. The performance of such active method has been confirmed by the authors using a dedicated noise-cancelling laboratory casing. Now, such approach is developed and applied to a real device casing, what is a significant step toward commercialisation of the active casing method. An adaptive FXLMS algorithm with practical modifications is proposed for the active control of the casing to provide noise reduction. A feed-forward structure is used, with a reference microphone located inside the real device. Additionally, an Internal Model Control system with reference signal estimation is also implemented for a comparison. The performance of the resulting control systems is experimentally verified for a real washing machine (an off-the-shelf product), using a loudspeaker placed inside the washing machine to provide reproducible noise. Obtained results are reported and discussed.

Process Parameters Estimation, Performance Assessment and Controller Retuning Based on the Final Value Theorem: Some Extensions

Abstract New techniques based on the application of the final value theorem to suitably devised signals have been recently proposed for the process parameters estimation, performance assessment and retuning of industrial (PID) controllers. The main features of these methods are that they can be applied in closed-loop and they can exploit routine operational data. In this paper we extend this methodology to two relevant cases, that is, those related to the implementation of an internal model control system and of a feedforward control architecture for the set-point following task. Simulation results confirm the effectiveness of the overall strategy also in these cases.

An efficient soft switching buck converter scheme for charging lead acid battery from PV source

In this paper, a high efficiency soft switched two channel interleaved buck converter scheme for charging a lead acid battery bank is presented. This scheme uses the incremental conductance (INC) maximum power point tracking (MPPT). An internal model control system (IMC) is used for controlling the charging of the battery in the constant current mode, constant voltage mode, and trickle mode depending upon the state of charge (SOC) of the battery. Various subsystems of simulation and the results are presented. The simulation results are validated experimentally by using a 125 W photovoltaic (PV) panel unit and a 36 A H lead acid battery. The real time experiment is performed under climatic conditions of Madurai which is a city located in Tamil Nadu, India. The result of the proposed system has been compared against the Perturb and observe MPPT scheme. The results of both simulation and the experiment reveal that the proposed soft switched, internal model controller incorporated INC scheme of MPPT offers more close tracking of the PV power and is more efficient.

An Internal Model Controller for Three-Phase APF Based on LS-Extreme Learning Machine

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, good generalization performance and so on. In order to improve the controller’s dynamic responses, a least squares extreme learning machine for internal model control is proposed. A least squares ELM regression (LS-ELMR) model for the three-phase APFS on-line monitoring was built from external factors with in-out datum. Moreover, the relative stable error is presented to evaluate the system performance and the features for the internal model control system based on extreme learning machine, neural network, kernel ridge regress and support vector machine. The experimental results show that the LS-internal model control system based on extreme learning machine has good dynamic performance and strong filtering result.

Research on the Application of Wavelet Neural Network in Temperature Control System

A rapid learning algorithm was put forward to realize complex system modeling and self-adaptive control with uncertainty, high nonlinear and lame time-delay. Merits of internal model control were combined, such as simple design, food regulation capacity, high robustness and the ability to eliminate the unknown disturbance to construct a internal model control system based on wavelets neural network, which was characterized by high robustness and quick response speed and then it can brim food control performance when controlled objects vary in a wide range. Finally, it was triumphantly used in simulation of the superheated steam temperature reduction control system of 500 MW unit and food performances are obtained.

The high performance control for small rotary-wing unmanned aircraft based on composite control method

To improve flight performance of small rotary-wing unmanned aircraft under complex environment, a composite control method based on internal model control (IMC) and adaptive radial basis neural network (RBFNN) is proposed. With the analysis of the characteristics of system disturbance, an IMC system is constructed to eliminate system errors. Furthermore, an adaptive RBFNN without prior training is proposed to eliminate residual estimation errors to augment the control performance. The effectiveness of the composite control method is validated by a series of flight tests. Compared with the feedback control method, the composite control method can yield good tracking performance under wind disturbances.

Control of indoor CO2 concentration based on a process model

Abstract Air-tight buildings need to have ventilation systems, although the use of these systems results in heavy energy consumption within the building sector. For this reason they have to be adequately regulated in order to achieve good indoor air quality and lower operation costs. The main challenge is to optimize regulation in office buildings, theaters, museums, and schools, where there are large fluctuations and heavy operating costs. Each further optimization leads directly to a better indoor climate and a reduction in energy consumption. In the case of ventilation systems, PI or PID regulators are usually used. In the described research an internal model control (IMC) system was designed with an internal loop, which constantly checks the momentary CO2 concentration, and makes the necessary adjustments to the air flow. The results showed a significant improvement in the CO2 level when using an IMC controller, in comparison with PI controller. The desired indoor air quality is achieved more than 80% of the time, with lower operating costs.

The Internal Model Control of Greenhouse System Based on LS_SVM

The greenhouse environment has the characteristics of nonlinear, time-varying, large time delay, so it is difficult to obtain satisfactory result using conventional control method. The internal model control system method and the algorithm based on LS_SVM is proposed for the problem. The simulation experiment was performed on the greenhouse environment control system. Results shows that the controlled object internal mode and inverse model have very high precision and generalization ability and the control system has not only good control performance but also strong anti-interference ability and robust performance which has the great value to application and extension

Technological constraints as method of improving control quality

The article describes the methods of using means for account technological constraints in automatic control system. The results are used in a control system with an internal model control system

The Design of Combustion Control System Based on Fuzzy Immune Internal Model Controller

The controlled object of boiler combustion system in power plant is a complex system with nonlinear, timing change, large lagging and multi-variable coupling, and does not have precise mathematical model, so it is difficult to obtain the satisfactory control effect adopting the traditional PID control. Advanced control strategies are adopted to improve the performance of the boiler combustion control system, and it has been more and more the concern of the majority of electricity production enterprises. Internal model control is a very practical control method, and its main characteristic is simple structure, intuitive design and few online adjustment parameter, and easy adjustment policy. And it is especially particularly significant to improve the control effect of large delay system. The internal model control system is used in power station boiler combustion system, it can effectively solve the large delay, large inertia and other shortcomings, but there is the contradiction between the fast response and robustness in internal model control system. The fuzzy immune control has advantages, such as, fast response, fast stable and good robust, etc. The fuzzy immune control is introduced into internal model control system, this paper designs fuzzy immune internal model controller, which integrates speed and robustness of the internal model control. The fuzzy immune internal model control is applied to combustion control system, and we compare it with ordinary internal model control method. The simulation result shows that fuzzy immune internal model control can greatly improve the characteristics of the control system with time delay. And this effectiveness of the fuzzy immune internal model controller has been verified.

A Transductive Neuro-Fuzzy Controller: Application to a Drilling Process

Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage.