Internal Model Control Research Articles

Lightweight Two-Layer Control Architecture for Human-Following Robot

(1) Background: Human detection and tracking are critical tasks for assistive autonomous robots, particularly in ensuring safe and efficient human–robot interaction in indoor environments. The increasing need for personal assistance among the elderly and people with disabilities has led to the development of innovative robotic systems. (2) Methods: This research presents a lightweight two-layer control architecture for a human-following robot, integrating a fuzzy behavior-based control system with low-level embedded controllers. The system uses an RGB-D sensor to capture distance and angular data, processed by a fuzzy controller to generate speed set-points for the robot’s motors. The low-level control layer was developed using pole placement and internal model control (IMC) methods. (3) Results: Experimental validation demonstrated that the proposed architecture enables the robot to follow a person in real time, maintaining the predefined following distance of 1.3 m in each of the five conducted trials. The IMC-based controller demonstrated superior performance compared to the pole placement controller across all evaluated metrics. (4) Conclusions: The proposed control architecture effectively addresses the challenges of human-following in indoor environments, offering a robust, real-time solution suitable for assistive robotics with limited computational resources. The system’s modularity and scalability make it a promising approach for future developments in personal assistance robotics.

State Machine and Internal Model Control-based Hybrid Controller for Improved Transient Performance of Microgrids

The transient performance of microgrids is measured by their transient stability and transient response indices. The quality of these indices depends upon the design of the microgrid’s closed-loop control scheme, which is a cascaded combination of outer power controller and inner voltage-current (VA) controllers. Generally, the power controller is realized by droop control logic (fixed droop or adaptive droop), and VA controllers by conventional proportional-integral (PI) control. Control schemes with fixed droop coefficient logic and PI-based VA controllers suffer from stability and response issues. In this scheme, when these PI-based VA controllers are replaced with internal model control (IMC) based VA controllers, the response aspect is improved, while the stability aspect shows no improvement. Therefore, when fixed droop logic is replaced with adaptive droop control logic, stability is improved. However, adaptive droop coefficient schemes with PI-based VA controllers provide a limited improvement in stability. Moreover, conventional machine learning-based adaptive droop coefficient logic suffers from severe computational difficulties. It is verified in this work that IMC-based VA controllers which cannot improve stability with fixed droop logic, will contribute to stability enhancement when a suitable adaptive droop control logic is deployed. Therefore, to enhance both stability and response aspects with reduced computational effort, this paper proposes a hybrid control scheme made of a state machine-based droop controller (SMD) supported by IMC-based VA controllers. From the simulation results, while the conventional schemes could not withstand load power factors lesser than 0.707, the proposed hybrid controller scheme maintained stability at a power factor of 0.47 with a total harmonic distortion of less than 5%.

A GWO-Based Indirect IMC-PID Controller for DC-DC Boost Converter

A PID controller design using an internal model control (IMC) approach is a well-established method for controller tuning in a DC-DC boost converter. This study introduces an innovative implementation of a novel indirect Internal Model Control (IMC) strategy for PID controller design, tailored specifically for a DC-DC boost converter. While the indirect IMC approach has been documented in prior research, its application to boost converters signifies a substantial contribution to the field. The proposed method simplifies the tuning process by focusing exclusively on the plant shifting parameter ψ, thereby eliminating the need for an IMC filter. Optimal tuning is achieved through the Grey Wolf Optimization (GWO) method, which enhances the controller’s stability, robustness, and transient response in the presence of disturbances commonly encountered in boost converter operation. Extensive simulations are performed in a MATLAB Simulink environment to compare the performance of the GWO-based indirect IMC-PID controller with traditional PID and IMC-PID designs. Performance is assessed based on transient response parameters and performance indices, such as IAE, ISE, ITAE, and ITSE. Results reveal that the GWO-optimized indirect IMC-PID controller significantly outperforms conventional controllers, demonstrating enhanced servo and regulatory behaviors.

Simplified internal models in human control of complex objects.

Humans are skillful at manipulating objects that possess nonlinear underactuated dynamics, such as clothes or containers filled with liquids. Several studies suggested that humans implement a predictive model-based strategy to control such objects. However, these studies only considered unconstrained reaching without any object involved or, at most, linear mass-spring systems with relatively simple dynamics. It is not clear what internal model humans develop of more complex objects, and what level of granularity is represented. To answer these questions, this study examined a task where participants physically interacted with a nonlinear underactuated system mimicking a cup of sloshing coffee: a cup with a ball rolling inside. The cup and ball were simulated in a virtual environment and subjects interacted with the system via a haptic robotic interface. Participants were instructed to move the system and arrive at a target region with both cup and ball at rest, 'zeroing out' residual oscillations of the ball. This challenging task affords a solution known as 'input shaping', whereby a series of pulses moves the dynamic object to the target leaving no residual oscillations. Since the timing and amplitude of these pulses depend on the controller's internal model of the object, input shaping served as a tool to identify the subjects' internal representation of the cup-and-ball. Five simulations with different internal models were compared against the human data. Results showed that the features in the data were correctly predicted by a simple internal model that represented the cup-and-ball as a single rigid mass coupled to the hand impedance. These findings provide evidence that humans use simplified internal models along with mechanical impedance to manipulate complex objects.

Flatness-based nonlinear internal model control with disturbances compensation via improved extended state observer for remotely operated vehicle

This paper proposes an improved Active Disturbance Rejection Control (ADRC) scheme for nonlinear control-affine systems, achieving superior robustness/ performance trade-offs. By leveraging the flatness property within a closed-loop structure, Nonlinear Internal Model Control (NLIMC) is employed as a model-based controller utilises partial plant knowledge to enhance control performance and eliminate static tracking errors. Subsequently, an improved Extended State Observer (IESO) is incorporated with NLIMC to address unmodelled dynamics, disturbances, and parameter uncertainties in addition to the full state's estimation. The IESO's added hyperbolic function dynamic ensures higher sensitivity and accuracy compared to conventional linear ESOs, especially in case of large errors. Control performances are evaluated through a Remotely Operated Vehicle (ROV) application. Numerical simulations have been carried out and the results confirm its effectiveness and superiority compared to Ocean Disturbance Observer-based Double-loop Sliding Mode Controller (ODSMC) and the linear ADRC.

Mechanism and kinetics of efficient removal of as from a high arsenic-bearing ZnSO4 solution using ultrasound enhanced oxygen

The traditional lime method for arsenic (As) removal from high arsenic-bearing ZnSO4 solution has disadvantages such as high cost, large amount of wet residue, significant secondary hazards, and long process flow. This study developed a new technology for efficient As removal using ultrasound enhanced oxygen. The effects of zinc roasting dust (ZRD) addition, reaction temperature, oxygen flow rate, and ultrasound power on As removal efficiency were investigated. Under the optimal conditions, the removal rate of As reached 97.90 %. The1O2, O2∙- and •OH radicals generated during the ultrasound-enhanced oxygen process significantly enhance the oxidation efficiency of As(III) and Fe2+. Ultrasound can also refine particle size and improve the removal efficiency of As. Compared with traditional lime method, the reaction time and wet residue amount have been reduced by 40 % and 70.37 %, respectively. Field emission scanning electron microscopy (FE-SEM-EDS), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) analyses indicate that As in ZnSO4 solution was primarily removed through by Fe(OH)3 colloidal adsorption and amorphous FeAsO4. Kinetic analysis revealed that the neutralization and As removal process using ZRD under ultrasound and oxygen action conforms to the internal diffusion control model. Ultrasound-enhanced oxygen As removal can reduce the reaction activation energy from 26.94 to 21.02 kJ/mol, effectively lowering the reaction barrier for As removal and demonstrating faster reaction rates under ultrasound conditions. Ultrasound-enhanced oxygen removal of As from high arsenic-bearing ZnSO4 solution provides an efficient and low-cost potential solution for enterprises.

Coordinative optimization for wind farms considering improved fatigue load index

In the wind farm(WF), there exists a serious uneven distribution of damage equivalent load(DEL) on the wind turbine(WT), which increases the maintenance cost of wind power generation. Previous researches focus on a single objective such as the total or the standard deviation of fatigue loads for optimal control. To address these, this paper proposes a coordinative optimization by considering an improved fatigue load index. The coordinative optimization is achieved on both WF level and WT level. For WF level, a state-space model is built for the active power dispatch by utilizing small signal linearization. Then, an improved sensitivity analysis is performed to evaluate the fatigue load on the component of the tower and shaft. Further, a coordinative multi-objective function is designed, which minimizes the total DEL of the WF, and balances the individual fatigue load distribution of the WT. For WT level, an adaptive internal model control is proposed to suppress both tower vibrations and torque fluctuation of the main shaft. Several experiments are performed to verify the significance of the proposed method with the weight analysis of power and load coefficient. The comparison results reveals the superiority of the coordinative optimization in restraining the total DEL of the WF, balancing WF fatigue distribution, decreasing the DEL of the single WT, and satisfying the power reference tracking simultaneously.

Control of an integrated first and second-generation continuous alcoholic fermentation process with cell recycling using model predictive control

This study explores controlling a first- and second-generation alcoholic fermentation process with cell recycling, modeled through algebraic differential equations (DAE) with constraints. It evaluates seven controller types: PI, PID, Model Predictive Control (MPC), Neural Network Model Predictive Control (NNMPC), Mixed Model Predictive Control (MMPC), Internal Model Control (IMC), and Linear Quadratic Regulator (LQR). Results show that, while PI and PID controllers can track setpoints, they exhibit slow responses and oscillations. In contrast, MPC controllers respond faster, with both MPC and MMPC demonstrating more robust dynamics, achieving a significant reduction in Integral of the Absolute Error (IAE) across various disturbances, notably an 87% reduction in regulatory scenarios. NNMPC outperforms PI and PID but exhibits overshoot and oscillations, and lacks robustness for servo-type problems. However, MMPC showcases comparable or superior performance to MPC, surpassing other controllers in robustness, especially the IMC and LQR in the regulatory problems. NNMPC maintains a simulation time of under 4 seconds, whereas MPC incurs a computational cost 1,000 times higher. Integrating NNMPC’s optimal increment as an initial estimate in MPC reduces computational time by up to 79.7%. These findings highlight the ANN’s effectiveness in addressing complex control challenges, especially when integrated with MPC. MMPC offers a superior balance between accuracy, robustness, and computational efficiency, serving as a promising solution for reducing computational costs in MPC-type controllers.

Assessment of the performance and robustness of a new PID tuning technique for AVR systems

ABSTRACT In this work, a hybrid PID controller, combining Zeigler–Nichols (ZN) theory and the dominant pole placement, has been developed for the automatic voltage regulator (AVR) systems. Furthermore, the reduced order models (ROMs) of AVR systems, namely, AVR-1, AVR-2, and AVR-3, have been established using Particle Swarm Optimization (PSO), Big Bang Big Crunch (BBBC), and Pade’s approximation, respectively. Then, the proposed PID controller has been applied to both the original and developed lower order models of AVR to check the efficacy and robustness of the developed hybrid controller. It is shown that the proposed hybrid PID controller provides a very small value of the percentage peak overshoot (%M p ) and settling time (t s ). Also, the gain margin (GM) and phase margin (PM) are found to be in the desired range. Finally, the sensitivity analysis has also been performed to check the robustness of the proposed controller. The sensitivity function, complementary sensitivity function, and various performance indices have been evaluated for the proposed controller based on Bode’s Integral (BI), Tyreus–Lyuben (T–L), and Internal Model Control (IMC) approaches. The comparative studies of the results show that the proposed controller has smaller values of performance indices and sensitivity function.

Implementation of Skogestad’s Internal Model Control Rule in Tuning Proportional-Integral Controllers for Two-Input and Two-Output Systems Considering Individual Controller Performance Trade-off

Abstract In industrial plants, tuning of decentralized Proportional-Integral-Derivative feedback (PID) controllers in Multiple-Input and Multiple-Output (MIMO) systems is a complex problem, due to input and output interactions, that must be done to ensure individual control objectives are met, while system being sufficiently robust. In this study, a two-input and two-output (TITO) PI controller tuning algorithm was developed considering controller performance prioritization given performance trade-offs, expressed as the novel Individual Controller Performance Trade-off Coefficient (ICPTC), and system robustness, expressed as sensitivity peaks from the generalized Nyquist stability plot. Simplified effective open-loop transfer functions (EOTFs) of the two input-output channels were derived and used along with Skogestad’s Internal Model Control (SIMC) to calculate PI controller gains. Extensive simulations using Wood-Berry (WB) and Vinante-Luyben (VL) distillation column models were made to observe how the sensitivity peaks behave across the simulations, which was then used as the basis of the developed tuning algorithm. A simplified tuning algorithm, which requires less information compared to the main algorithm, was also derived. A mapping algorithm was also created, allowing specific controller gains from other studies to be mapped to an equivalent set of controller gains in the algorithm’s search field. The effectiveness of the three algorithms were tested using Wardle-Wood distillation column (WW) and Xiong and Cai (XC) process models. It was demonstrated using the main algorithm that there is a trade-off between controller performances when ICPTC is varied, at different values of design sensitivity peaks between 1.96 to 4. The usefulness of the simplified tuning and mapping algorithms were also illustrated in the study. This study is potentially useful in tuning TITO PI controllers in industrial plants, where meeting individual control objectives while maintaining robustness is more important than optimizing overall control performance.

Cortical activity during online motor control in children with and without developmental coordination disorder: a cross-sectional functional near-infrared spectroscopy study

BackgroundChildren with developmental coordination disorder (DCD) have impaired online motor control. Researchers posit that this impairment could be due to a deficit in utilizing the internal model control process. However, there is little neurological evidence to support this view because few neuroimaging studies have focused specifically on tasks involving online motor control. Therefore, the aim of this study was to investigate the differences in cortical hemodynamic activity during an online movement adjustment task between children with and without DCD.MethodsTwenty children with DCD (mean age: 9.88 ± 1.67 years; gender: 14M/6F) and twenty age-and-gender matched children with typical development (TD) (mean age: 9.87 ± 1.59 years; gender: 14M/6F) were recruited via convenience sampling. Participants performed a double-step reaching task under two conditions (with and without online adjustment of reaching). Cortical hemodynamic activity during task in ten regions of interest, including bilateral primary somatosensory cortex, primary motor cortex, premotor cortex, superior parietal cortex, and inferior parietal cortex was recorded using functional near-infrared spectroscopy. In the analyses, change in oxyhemoglobin (ΔHbO) concentration was used to characterize hemodynamic response. Two-way analyses of variance were conducted for each region of interest to compare hemodynamic responses between groups and conditions. Additionally, Pearson’s r correlations between hemodynamic response and task performance were performed.ResultsOutcome showed that children with DCD required significantly more time to correct their reaching movements compared to the control group (t = 3.948, P < 0.001). Furthermore, children with DCD have a significantly lower ΔHbO change in the left superior parietal cortex during movement correction, compared to children with TD (F = 4.482, P = 0.041). Additionally, a significant negative correlation (r = − 0.598, P < 0.001) was observed between the difference in movement time of reaching and the difference in ΔHbO between conditions in the left superior parietal cortex.ConclusionsThe findings of this study suggest that deficiencies in processing real-time sensory feedback, considering the function of the superior parietal cortex, might be related to the impaired online motor control observed in children with DCD. Interventions could target this issue to enhance their performance in online motor control.

A Novel Simplified PID Controller Tuning Method for Exact Maximum Sensitivity Specification

Many PID (Proportional plus Integral plus Derivative) controller tuning methods are proposed in the control literature based on different closed-loop specifications. Also, tuning techniques, based on the maximum sensitivity specification, have been proposed in the literature. In this paper, a novel PID controller design method, based on maximum sensitivity, has been proposed. In this work, the PID controller is viewed as a gain and dynamic part and tuned separately. The PID controller dynamic part is tuned using one of the existing popular tuning methods. The tuned dynamic part is cascaded with the process. After that, the PID controller gain is tuned for the maximum sensitivity specification. The maximum sensitivity versus controller gain graph can be generated. The user can choose the controller gain for the desired maximum sensitivity specification. The popular Ziegler & Nichols and IMC (Internal Model Control) methods tune the dynamic part of the system in this work. These proposed techniques can achieve the desired maximum sensitivity by tuning the controller gain in the subsequent stage. Many illustrative instances are considered to display the applicability and the proposed technique’s simplicity.

Adaptive Control of Ships’ Oil-Fired Boilers Using Flame Image-Based IMC-PID and Deep Reinforcement Learning

The control system of oil-fired boiler units on ships plays a crucial role in reducing the emissions of atmospheric pollutants such as nitrogen oxides (NOx), sulfur dioxides (SO2), and carbon dioxide (CO2). Traditional control methods using conventional measurement sensors face limitations in real-time control due to response delays, which has led to the growing interest in combustion control methods using flame images. To ensure the precision of such combustion control systems, the system model must be thoroughly considered during controller design. However, finding the optimal tuning point is challenging due to the changes in the system model and nonlinearity caused by environmental variations. This study proposes a controller that integrates an internal model control (IMC)-based PID controller with the deep deterministic policy gradient (DDPG) algorithm of deep reinforcement learning to enhance the adaptability of image-based combustion control systems to environmental changes. The proposed controller adjusts the PID parameter values in real-time through the learning of the determination constant lambda (λ) of the IMC internal model. This approach reduces computational resources by shrinking the learning dimensions of the DDPG agent and limits transient responses through constrained learning of control parameters. Experimental results show that the proposed controller exhibited rapid adaptive performance in the learning process for the target oxygen concentration, achieving a reward value of −0.05 within just 105 episodes. Furthermore, when compared to traditional PID tuning methods, the proposed controller demonstrated superior performance, achieving a target value error of 0.0032 and a low overshoot range of 0.0498 to 0.0631, providing the fastest response speed and minimal oscillation. Additionally, experiments conducted on an actual operating ship verified the practical feasibility of this system, highlighting its potential for real-time control and pollutant reduction in marine applications.

Drone Swarm Robust Cooperative Formation Pursuit through Relative Positioning in a Location Denial Environment

This paper considers the pursuit problem of a moving target by a swarm of drones through a flexible-configuration formation. The drones are modeled by second-order systems subject to uncertain damping ratios, whereas the moving target follows a polynomial-type trajectory whose coefficient vectors are fully unknown. Due to location denial, drones cannot obtain their absolute positions, but they can obtain their positions relative to other neighboring drones and the target. To achieve a robust formation pursuit, a robust cooperative control protocol is synthesized, which comprises three key components, namely, the pseudo drone position estimator, the pseudo target position estimator, and the local internal model control (IMC) law. The pseudo drone position estimator and the pseudo target position estimator aim to recover for each drone the position of itself and the target, respectively, but are subject to some common unknown constant bias in a distributed manner. By subtracting the pseudo target position from the pseudo drone position, each drone can acquire its position relative to the target, which facilitates the design of a local IMC law to fulfill formation pursuit in the presence of system parametric uncertainties. Both pure numerical simulation and hardware-in-the-loop (HIL) simulation are performed to verify the effectiveness of the proposed control protocol.

Data-Driven Dynamic Internal Model Control.

A data-driven dynamic internal model control (D3IMC) scheme is proposed for unknown nonlinear nonaffine systems bypassing modeling steps. Different from the traditional internal model constructed by either a first-principle or an identified model, a dynamic internal model (DIM) is developed in this work using I/O data where a compact form dynamic linearization approach is introduced for addressing the nonlinearity and nonaffine structure. Then, the D3IMC is proposed with both a nominal control algorithm and an uncertainty compensation control algorithm. The former can quickly respond to the feedback errors and the latter can compensate the model-plant mismatch and external disturbances. Meanwhile, the adaptive parameter updating law in the proposed D3IMC method inherits the robustness against uncertainties. A nominal D3IMC is also designed without including the compensator when there is no exogenous disturbance since the adaptive mechanism can handle system uncertainty. Further, the results are extended and a full-form dynamic linearization-based D3IMC is developed to address control of nonlinear systems with more complex dynamics. All the proposed D3IMC methods are data-driven without need of an explicit model, and thus they are significant extensions from the traditional model-based IMC. Simulation study verifies the results.

Identification of nonlinear system model and inverse model based on conditional invertible neural network

In applications such as adaptive inverse control, internal model control, and active noise control, the identification accuracy of the system model and the inverse model directly affects the performance. However, it is not easy to identify inverse models for nonlinear systems. Moreover, existing methods require two identification calculations to obtain the system model and the inverse model. Therefore, an identification method of nonlinear system model and inverse model based on conditional invertible neural network (cINN) is proposed. The invertible structure of cINN enables simultaneous approximation of complex nonlinear functions and simultaneous acquisition of the corresponding inverse functions. Consequently, both the nonlinear system model and the inverse model can be identified concurrently through cINN. Moreover, the identification performance of the cINN-based method is validated and applied to disturbance cancellation in a classical nonlinear simulation system. Finally, the inverse model of the actuator is identified by cINN, and the inverse model is applied to the nonlinear compensation of the actuator.

Combustion Control of Ship’s Oil-Fired Boilers Based on Prediction of Flame Images

This study proposes and validates a novel combustion control system for oil-fired boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs and applies internal model control (IMC)-based proportional-integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC filter constant tuning through experimentation is essential for achieving effective control performance. Experimental results showed that optimal control performance was achieved when the filter constant λ was set to 1.5. In this scenario, the peak overshoot Mp was reduced to 0.19245, and the Integral of Squared Error (ISE) was minimized to 10.1159, ensuring a stable response with minimal oscillation and maintaining a fast response speed. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

Risk and conditioning factors in the public internal control. Weaknesses in public management concerning the procurement of expenditure files

IMPACT The article provides new insights in the analysis of risks to improve the efficiency and effectiveness of public management. Public auditors and internal control professionals in public institutions should be looking for continuous improvement. Transparency, delimitation of competences, contractual procedures, budget management and special payment procedures are identified by the authors as the main areas of management risk faced by public control professionals. The case study country, Spain, like many other EU countries, has a combined internal control model, using an ex-ante control complemented by ex-post control. The main conclusions reached in this article can be applied to other European countries that use similar control models to improve risk management and internal control in their public sectors.

Practical output regulation of uncertain Euler–Lagrange systems

This paper deals with the problem of robust output regulation of uncertain Euler–Lagrange systems. An internal model controller is synthesized by a systematic framework that considers polynomial mappings of the steady-state trajectory. The closed-loop stabilization is guaranteed by using a descriptor differential–algebraic representation of the system. This methodology allows the controller design problem to be cast as a convex optimization problem subject to linear matrix inequalities. Simulations considering a two-link robotic manipulator illustrate the method.

Adaptive model predictive control scheme for partially internal thermally coupled air separation column

Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and cost-effective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the operating stability of this technology. This article, therefore, proposes an adaptive model predictive control (AMPC) scheme for the P-ITCASC process. The controller incorporates an auto-regressive and exogenous inputs (ARX) model, a linear time-varying Kalman filter, and a recursive polynomial model estimator (RPME) algorithm. Within RPME, ARX polynomial models are identified and utilized to estimate the time-varying parameters and update the prediction model of the process. The process states are observed through the Kalman filter, and the constrained receding horizon optimization problem is solved using quadratic programming. This control scheme ensures the closed-loop system's feasibility and stability in the presence of output/input constraints. An adaptive generic model control, model predictive control, and adaptive internal model control schemes were also designed for benchmarking study. Numerical simulations show that AMPC is more efficient in handling nonlinear dynamics and maintaining product concentration to desired set points compared to other control schemes.