Conventional Internal Model Control Research Articles

A look-up table-based processing-in-SRAM architecture for energy-efficient search applications

This paper presents an efficient in-memory computing architecture for search and logic function applications. The proposed design benefits from an SRAM cell, using two single-ended read bit-lines to enable binary and ternary content addressable memories (BCAM and TCAM) and logic functions. As our proposed cell benefits from Schmitt trigger inverters based on independent gate FinFETs. No data distraction occurs due to the half-selection issue. The results indicate no failure in the proposed method in the half-selected cells in the BCAM and TCAM functions. Moreover, the proposed cell shows 58% and 89% lower power in the write and read operations than the conventional SRAM-based IMC design. Also, by providing basic logic operations in the proposed design, applications with high-accuracy requirements can be implemented. Moreover, a neural network is also implemented to assess the proposed design in a practical application. The results show that our design consumes 41% and 50% lower energy than its counterparts.

Optimized Model Based Controller with Model Plant Mismatch for NMP Mitigation in Boost Converter

In this paper, an optimized Genetic Algorithm (GA) based internal model controller-proportional integral derivative (IMC-PID) controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase (NMP) behavior due to the presence of a right-half plane zero (RHPZ). This RHPZ limits the dynamic performance of the converter and leads to internal instability. The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameter λ. Also, this paper addresses the influences of the model-based controller with model plant mismatch on the closed-loop control. The conventional IMC PID design is realized as an optimization problem with a resilient controller being determined through a genetic algorithm. Computed results suggested that GA–IMC PID coheres to the optimum designs with a fast convergence rate and outperforms conventional IMC PID controllers.

Fractional-order model identification and indirect internal model controller design for higher-order processes

Several studies have shown that fractional-order models with dead-time can be used to capture the dynamics of a wide range of processes. Also, there is more scope to improve the existing identification and control strategies. Hence, this work revisits the identification of fractional-order models (IDFOM) approach to augment the same with a robust indirect fractional-order internal model control (IFOIMC) method to formulate a new identification and control strategy. In this strategy, the fractional-order model obtained using the IDFOM approach is shifted in the frequency domain by a frequency-shifting parameter. With the help of IMC principles, the frequency-shifted model is used to design the IFOIMC controller with the frequency-shifting parameter as the only tuneable parameter. Thus, the frequency-shifting parameter/robustness parameter replaces the only tuning parameter of conventional IMC design. This frequency-shifting parameter is tuned based on maximum sensitivity considerations to ensure the robustness of the design. The resulting IFOIMC controller is further realized as a fractional-order proportional-integral controller in series with a fractional-order filter (FOPIFOF). The effectiveness of the proposed identification and control strategy is illustrated by comparing the closed-loop performance achieved by IFOIMC controllers designed using two widely used identification strategies.

System identification and improved internal model control for yaw of unmanned helicopter

AbstractThe sea breeze is a low‐frequency disturbance that severely damages the stability of small unmanned helicopters operating over the sea, especially for the yaw control, which is highly sensitive to disturbance. General internal model control is an appropriate method for dealing with this kind of operation conditions, whereas conventional internal model control cannot eliminate the tracking errors between a nominal model and a real model. In coping with unknown dynamics and low‐frequency gust disturbances for small helicopters, this paper proposes a novel robust controller constructed with system identification and integrator‐based improved general internal model. As a refinement of the conventional frame, the proposed control scheme extends the applicable scope of a controlled plant from a priori known dynamic to an unknown dynamic. Furthermore, under the proposed controller, it is guaranteed that the tracking error between the actual model and the nominal model converges to zero asymptotically. Finally, the effectiveness and advantage of the proposed control scheme are verified through comparative practical flight tests.

Suppression of Wind Generator Speed Vibration Based on the Internal Model Control with Three Degrees of Freedom

When the wind power system is working in the maximum wind energy tracking area, or when its mechanical drivetrain system vibrates and the vibration frequency is relatively high, the system cannot suppress the speed vibration of the generator in the drivetrain system by adjusting the pitch angle. In this paper, a generator speed control system based on the three degrees of freedom internal model control (3-DOF-IMC) is established to control the generator speed. Thus, a scheme of using the feedback filter in the 3-DOF-IMC to suppress generator speed vibration caused by drivetrain shaft elasticity and gear clearance in the drivetrain system is proposed. Firstly, the vibration problem and waveform of the two-mass wind power drivetrain systems are discussed, and the generator’s vector control and speed control systems are analyzed. Secondly, the principle of the 3-DOF-IMC is described, and the influence of the three controllers on the speed tracking performance and anti-interference performance of the generator is discussed. The suppression ability of the feedback filter for different forms of disturbance signals is emphasized. Finally, the feedback filter controls the generator speed and eliminates the influence of drivetrain vibration on the generator speed. To verify the superiority of the proposed method, the vibration suppression effect, tracking performance and anti-interference performance of the proposed method are compared with the engineering design method (EDM) and conventional IMC (1-DOF-IMC) method. When the parameter α/β = 0.66, the generator speed amplitude overshoot of the proposed method is the same as the EDM. When α/β = 1, it is only 4% of the amplitude overshoot of the EDM. In addition, the tracking performance and anti-interference performance of the proposed method can be adjusted independently, and it is better than the EDM and the 1-DOF-IMC method.

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.

A novel grey decision-DE optimized internal model controller for vibration control of nonlinear uncertain aeroelastic blade system

In this paper, a novel grey decision-differential evolution (GD-DE) optimized internal model controller is proposed for the control of nonlinear aeroelastic blade system (ABS) under time-varying uncertainties and control saturations. Two-port internal model control structure is presented for saturation compensation. The filter parameter of the controller is optimally tuned by proposed GD-DE algorithm, which is designed based on the grey decision incidence theory and differential evolution algorithm. The superiority of GD-DE optimized tuning technique is verified, compared to conventional IMC tuning method and other evolutionary algorithm based techniques. The robustness is analysed by comprehensive simulations from time-invariant uncertainties to large periodic or random time-varying uncertainties. The realistic condition with input/output disturbances is also involved. Simulation results show that the proposed controller outperforms existing adaptive IMC controller and other advanced controllers with greatly improved dynamic performance, stronger robustness and better saturation compensation on various conditions.

A Novel Technique for Load Frequency Control of Multi-Area Power Systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability.

Improved Internal Model Control Technique for Position Control of AC Servo Motors

This paper focuses on the simulation analysis of the conventional Internal Model Control (IMC) technique and the development of two proposed control techniques for the position control of AC Servo Motor. Internal Model Control (IMC) technique [1] was only able to control the AC Servo Motor under static load condition. Also, it had step response problems, and it was not robust against external disturbances. For these reasons, the IMC technique was further improved to control the AC Servo Motor under dynamic load conditions by proposing Amended Internal Model Controller (AIMC). The step response and the robustness of AIMC against external disturbances were further improved by proposing AIMC+FLC. Where a Fuzzy Logic Controller (FLC) is designed and connected with the AIMC.

An improved 2-degree-of-freedom internal model proportional–integral–derivative controller design for stable time-delay processes

In this article, an enhanced 2-degree-of-freedom internal model control strategy for typical industrial processes with time-delay is developed. For the proposed controller, it is composed of an inner loop feedback controller which is designed based on the internal model control theory and a weighted set-point tracking controller. Note that the adjustment of set-point tracking performance and disturbance rejection characteristics can be decoupled by employing the developed strategy, which indicates that more degrees of freedom are obtained for the proposed controller design; thus, better ensemble performance and stronger robustness are anticipated by regulating these two controllers separately, which may not be achieved in the conventional internal model control method. Case studies on two kinds of stable processes with time-delay verify the effectiveness of the proposed scheme finally.

Modelling and control of fouling in submerged membrane bioreactor using neural network internal model control

Membrane bioreactor (MBR) is one of the best solutions for water and wastewater treatment systems in producing high quality effluent that meets its standard regulations. However, fouling is one of the main issues in membrane filtration for membrane bioreactor system. The prediction of fouling is crucial in the membrane bioreactor control system design. This paper presents an intelligence modeling system so called artificial neural network (ANN). The feedforward neural network (FFNN), radial basis function neural network (RBFNN) and nonlinear autoregressive exogenous neural network (NARXNN) are applied to model the submerged MBR filtration system. The simulation results show good predictions for all methods which the highest performance of the model given by RBFNN. Based on the developed models, the neural network internal model control (NNIMC) is implemented to control fouling development in membrane filtration process. Three different control structures of the NNIMC are proposed. The FFNN IMC, RBFNN IMC and NARXNN IMC controllers are compared to the conventional IMC. The RBFNN IMC has a superior performance both in tracking and disturbance rejections.

Signal Transformed Internal Model Control for Non-raster Scanning of Piezo-actuated Nanopositioning Stages

This paper proposes a new signal transformed internal model control (STIMC) scheme for non-raster scanning patterns of piezo-actuated nanopositioning stages. To smooth the scanning signals superimposed with a ramp or time-varying amplitudes, a signal transformation operator is calculated to convert the references into standard harmonic waveforms. An inverse transformation operator is then added in the control loop to generate the driving signals. For the contained internal model control (IMC) design, an H∞ mixed-sensitivity method is utilized for the first order internal mode synthesis. A second and a third internal modes are included in the IMC for alleviating residual high-frequency errors resulted from the nonlinearity of hysteresis. To verify the proposed STIMC scheme, comparative experiments with conventional IMC are conducted based on a nanopositioning platform. Results prove that the STIMC is effective on non-raster signals’ tracking. A same tracking precision for Lissajous scanning can be obtained by STIMC compared with IMC. An improvement of larger than 50% and 80% of root-mean-square errors can be obtained for cycloid and spiral scanning patterns, respectively.

Fuzzy rule-based auto-tuned internal model controller for real-time experimentation on temperature and level processes

Recently, internal model control (IMC) technique has been widely employed for various industrial close-loop control applications. Rewarding feature of IMC controller is that we need to tune only one parameter λ (close-loop time constant) for achieving the desired close-loop response. But, finding an appropriate value of λ is not an easy task. From the basic behaviour of IMC-based close-loop responses, it is found that when the process variable is moving very fast towards the desired value, relatively larger value of λ (smooth control) is required to reduce the possible oscillations. In contrary, smaller value of λ (tight control) is preferred when the process response is quickly moving away from the set point to restrict its further deviation. Hence, to mitigate the limitation of conventional IMC tuning with a fixed λ, a fuzzy rule-based auto-tuning scheme is proposed here for IMC-proportional integral derivative (IMC-PID) controller and its performance is validated through real-time experimentation on temperature and level control loops.

Inverse plant model and frequency loop shaping-based PID controller design for processes with time-delay

To achieve satisfactory set-point tracking and load disturbance rejection, two approaches for PID controller design is presented in this paper. With a PD type controller, conventional IMC techniques fail to provide a satisfactory regulatory response for integrating processes and use of an integral action may lead to a large overshoot in servo response. To address this issue, a modified IMC structure with a second compensation for integrating processes is proposed to achieve desired servo as well as regulatory responses. Next, a frequency loop-shaping based design is proposed and the guidelines for choosing the desired loop-shape are also presented. To obtain the controller parameters in frequency loop-shaping framework, the optimisation problem is solved with primal-dual interior point method. To demonstrate the effectiveness of the proposed controllers, simulation comparisons with some recently developed methods are included. Moreover, the proposed method is experimentally validated on a temperature control process.

Designing of Fuzzy rule based switching mechanism for IMC controller for temperature controlling process

A fuzzy rule based switching mechanism for IMC controller is reported here. The switching scheme is realized through online adjustment of the controller settings between smooth control and tight control methodologies based on nine fuzzy rules. Initial settings of IMC controller is done by SIMC [5] tuning. The tuning parameters of SIMC controller i.e. closed-loop time constant (λ) and integral time (τI) are modified depending on the instantaneous process behaviour instead of their fixed settings as with conventional IMC tuning. Thus, due to proposed switching, closed-loop time constant (λ) and integral time (τI) of the IMC controller gets varied instead of their fixed tuning. Superiority of the reported scheme is verified based on practical experimentation of a closed-loop temperature control process.

Internal model control of induction motors based on extended state observer

The conventional internal model control (IMC) has been used widely due to its advantages of less computational burden and simple implementation. Since the internal model controller has a fixed filter, disturbances created by mismatched models, parameter variations and other unstructured dynamic uncertainties in induction motors (IMs) cannot be eliminated by a fixed IMC. To solve these problems, the control strategy of an induction motor using internal model control with an extended state observer (IMC-ESO) was proposed. IM parameter variations and other unstructured dynamic uncertainties are considered in IM drives. Based on this model, an ESO is defined as a hypothetical equivocal function. Then the estimated disturbance is applied as a feed-forward compensation to accurately control the current loop. Since the designed ESO works concurrently with IMC, the fast dynamic response of the IMC is maintained. The feasibility and validity of the proposed method are validated by experimental results.

A Self-Adjusting Adaptive AVR-LFC Scheme for Synchronous Generators

An efficient self-adjusting flexible metaheuristic is introduced to develop automatic voltage and load frequency regulation schemes for multi-area power system. It employs non-linear threshold accepting algorithm to find the best combinations of PID control gains using an innovative nonlinear search function. This approach outperformed well established heuristics and other conventional internal model control approaches in terms of the dynamic response, robustness in uncertain dynamical environments, and quality of the solutions.

Modified IMC Technique for Nonlinear Uncertain Milling CNC Machine Tool System

The objective of this paper is to model a nonlinear computer numerical control machine tool (CNCMT) system for milling process. A modified internal model control (M-IMC) technique is proposed for position control of machine table. The study of milling process is mainly focused on the cutting mechanics and dynamics. The performance of CNCMT system is degraded due to the presence of disturbance like cutting force and parametric uncertainty. Considering the limitation of conventional internal model control (C-IMC) technique that is the poor performance under constant disturbance, an M-IMC technique is designed for the rejection of disturbance and position control of uncertain nonlinear CNCMT system. The performances of nonlinear uncertain CNCMT system using M-IMC technique is also compared with C-IMC and Ziegler–Nichols-tuned proportional–integral–derivative control techniques. The proposed M-IMC technique is designed to attain the better disturbance rejection capability, i.e., regulatory control with less control effort for the regulation of desired position of a machine table.

Design of an online-tuned model based compound controller for a fully automated artificial pancreas.

This paper deals with the development of a control algorithm that can predict optimal insulin doses without patients' intervention in fully automated artificial pancreas system. An online-tuned model based compound controller comprising an online-tuned internal model control (IMC) algorithm and an enhanced IMC (eIMC) algorithm along with a meal detection module is proposed. Volterra models, used to develop IMC and eIMC algorithms, are developed online using recursive least squares (RLS) filter. The time domain kernels, computed online using RLS filter, are converted into frequency domain to obtain Volterra transfer function (VTF). VTFs are used to develop both IMC and eIMC algorithms. The compound controller is designed in such a way that eIMC predicts insulin doses when the glucose rate increase detector of meal detection module is positive, otherwise conventional IMC takes the control action. Experimental results show that the compound controller performs robustly in the presence of higher and irregular amounts of meal disturbances at random times, very high actuator and sensor noises and also with the variation in insulin sensitivity. The combination of compound control strategy and meal detection module compensates the shortcomings of both slow subcutaneous insulin action that causes postprandial hyperglycemia, and delayed peak of action that causes hypoglycaemia. Graphical Abstract A fully-automated artificial pancreas system containing glucose sensor, insulin pump and control algorithm. Block diagram showing the control algorithm i.e., online-tuned compound IMC comprising enhanced IMC, conventional IMC and meal detection module, developed in the present work.

Analytical Design of Enhanced Fractional Filter PID Controller for Improved Disturbance Rejection of Second Order Plus Time Delay Processes

Abstract In this article, a modified fractional internal model control (IMC) filter structure is proposed to design a fractional filter Proportional-Integral-Derivative (FFPID) controller for improved disturbance rejection of second order plus time delay (SOPTD) processes. The proposed method aims at improving the disturbance rejection of slow chemical processes as the tuning rules for such processes are limited. The present design also considers the higher order approximation for time delay as it gives improved response for higher order processes. There is an additional tuning parameter in the proposed IMC filter apart from the conventional IMC filter time constant, which is tuned according to the derived formula. The additional adjustable parameter achieves the disturbance rejection and the closed loop stability. The simulation results have been performed for the same degree of robustness (maximum sensitivity, Ms) for a fair comparison. The results show an improved disturbance rejection for lag dominant and delay significant SOPTD processes with the proposed controllers designed using higher order Pade’s approximation of time delay than the proposed method using first order approximation and the conventional method. The closed loop robust performance is observed for perturbations in the process parameters and the performance is also observed for noise in the measurement. The robust stability analysis is carried out using sensitivity functions. In addition, the Ms range is also identified over which the system gives robust performance for the controllers designed using higher order pade’s approximation of time delay compared to conventional method.