Internal Model Control Scheme Research Articles

A novel robust disturbance rejection anti-windup framework

In this article, we propose a novel anti-windup (AW) framework for coping with input saturation in the disturbance rejection problem of stable plant systems. This framework is based on the one developed by Weston and Postlethwaite (W&P) (Weston, P.F., and Postlethwaite, I. (2000), ‘Linear Conditioning for Systems Containing Saturating Actuators’, Automatica, 36, 1347–1354). The new AW-design improves the disturbance rejection performance over the design framework usually suggested for the coprime-factorisation based W&P-approach. Performance improvement is achieved by explicitly incorporating a transfer function, which represents the effect of the disturbance on the nonlinear loop, into the AW compensator synthesis. An extra degree of freedom is exploited for the coprime factorisation, resulting in an implicitly computed multivariable algebraic loop for the AW-implementation. Suggestions are made to overcome the algebraic loop problem via explicit computation. Furthermore, paralleling the results of former work (Turner, M.C., Herrmann, G., and Postlethwaite, I. (2007), ‘Incorporating Robustness Requirements into Antiwindup Design’, IEEE Transactions on Automatic Control, 52, 1842–1855), the additive plant uncertainty is incorporated into the AW compensator synthesis, by using a novel augmentation for the disturbance rejection problem. In this new framework, it is shown that the internal model control (IMC) scheme is optimally robust, as was the case in Turner, Herrmann, and Postlethwaite (2007) and Zheng and Morari (Zheng, A., and Morari, M. (1994), ‘Anti-windup using Internal Model Control’, International Journal of Control, 60, 1015–1024). The new AW approach is applied to the control of dynamically substructured systems (DSS) subject to external excitation signals and actuator limits. The benefit of this approach is demonstrated in the simulations for a small-scale building mass damper DSS and a quasi-motorcycle DSS.

Internal model control based on a novel least square support vector machines for MIMO nonlinear discrete systems

To improve the robustness of the traditional inverse system method, the internal model control based on a novel least square support vector machines (LS-SVM) is proposed. The novel LS-SVM considers general errors that include noises of input variables and output variables as empirical errors. The data of original MIMO discrete system is exploited to approximate its inverse model by the novel LS-SVM. By cascading the inverse model and the original system to constitute a decoupling pseudo-linear system, the internal model control strategy is carried out to the pseudo-linear system to realize the effective control. Simulation validates that the novel LS-SVM used in the inverse system identification is effective and shows that the internal model control of nonlinear discrete systems has better robustness of anti-interference and parameters varying than that of the open-loop system only based on inverse control.

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.

Comparing rear wheel steering and rear active differential approaches to vehicle yaw control

A comparison between two different approaches to vehicle stability control is carried out, employing a robust non-parametric technique in the controller design. In particular, an enhanced internal model control strategy, together with a feedforward action and a suitably generated reference map, is employed for the control of a vehicle equipped either with a rear wheel steering (RWS) system or with a rear active differential (RAD) device. The uncertainty arising from the wide range of operating conditions is described by an additive model set employed in the controller design. Extensive steady state and transient tests simulated with an accurate 14 degrees of freedom nonlinear model of the considered vehicle show that both systems are able to improve handling and safety in normal driving conditions. RAD devices can make the vehicle reach higher lateral acceleration values but they achieve only slight stability improvements against oversteer. On the other hand, 4WS systems can greatly improve both vehicle safety and manoeuvrability in all driving situations, making this device an interesting and powerful stability system.

Adaptive Internal Model Control with fractional order parameter

AbstractIn this paper we have introduced as a model in the adaptive Internal Model Control (IMC) structure a fractional order system to obtain a fractional adaptive IMC scheme. We have shown that this adaptive control scheme always provides theoretical guarantees of stability when a stable fractional order transfer function is used as an IMC parameter and that the use of this fractional IMC parameter can improve the performances of the control system along with its robustness against noises. A comparative simulation example is given to illustrate the efficiency of the proposed fractional order adaptive control scheme. Copyright © 2010 John Wiley & Sons, Ltd.

Multi-loop Internal Model Controller Design Based on a Dynamic PLS Framework

In this paper, a multi-loop internal model control (IMC) scheme in conjunction with feed-forward strategy based on the dynamic partial least squares (DyPLS) framework is proposed. Unlike the traditional methods to decouple multi-input multi-output (MIMO) systems, the DyPLS framework automatically decomposes the MIMO process into a multi-loop system in the PLS subspace in the modeling stage. The dynamic filters with identical structure are used to build the dynamic PLS model, which retains the orthogonality among the latent variables. To address the model mismatch problem, an off-line least squares method is applied to obtain a set of optimal filter parameters in each latent space. Without losing the merits of model-based control, a simple and easy-tuned IMC structure is readily carried over to the dynamic PLS control framework. In addition, by projecting the measurable disturbance into the latent subspace, a multi-loop feed-forward control is yielded to achieve better performance for disturbance rejection. Simulation results of a distillation column are used to further demonstrate this new strategy outperforms conventional control schemes in servo behavior and disturbance rejection.

Autopilot System Based on Color Recognition Algorithm and Internal Model Control Scheme for Controlling Approaching Maneuvers of a Small Boat

This paper designs and develops an autopilot system for guiding the trajectory of a small boat as it performs an approaching maneuver within a harbor. In the proposed approach, two canvas targets are arranged in cascade on the berth to serve as leading marks to define the leading line along which to guide the boat toward the berthing standby region. The leading marks are captured by a charge-coupled device (CCD) camera mounted on the bow of the boat, and the resulting image is processed using a color recognition scheme to identify the hue (H) and saturation (S) components. The H- and S-component images are subsequently processed using a noise filtering scheme to accurately detect the location of the two targets in the image. The centers of gravity (CGs) of the two leading marks are then calculated, and the separation distance between them is used to compute the deviated heading angle. This angle is then supplied to an internal model control (IMC) autopilot system as an error signal and is used to generate appropriate control commands to steer the boat toward the leading line. The feasibility of the autopilot system is verified by performing a series of experimental approaching maneuvers using a small boat in a real-world harbor setting.

FUZZY LOGIC AND GENETIC ALGORITHMS SUPERVISORS FOR INTERNAL MODEL CONTROL STRATEGY

This paper presents two methods allowing the online adjustment of the filter gain in the internal model control (IMC) strategy. These methods are based on fuzzy logic and genetic algorithms. The IMC strategy needs the direct model and the inverse model of the process. These models can be estimated offline from input-output data. In this work, we have used feed forward Artificial Neural Networks to determine these models. The back propagation algorithm is used to train the neural networks. The neural network internal model control with the proposed supervisors is applied to numerical examples. The performances of the proposed controller are compared to a standard PI controller and to a PI controller with an anticipation action given by the inverse model of the process.

A Phase-Locked-Loop-Assisted Internal Model Adjustable-Speed Controller for BLDC Motors

In this paper, a phase-locked-loop (PLL)-assisted internal model (IM) adjustable-speed controller for brushless DC (BLDC) motors is proposed. Major contributions include proposing a new adjustable-speed module structure of PLL controllers for BLDC motor drives and integrating an IM control strategy and a novel complete motor current sensing scheme. By employing the proposed PLL-assisted IM control method, the controller can be made more robust and accurate. In addition, due to properly integrating the motor current sensing scheme with the pulsewidth modulation control, the hardware implementation of the BLDC motor drive can be made rather compact and enable further integration into chips to reduce the cost and enhance the current regulation performance. Finally, a prototype of the proposed controller is also constructed and applied to an industrial blower to verify the feasibility of the proposed control. Experimental results show that both accurate steady state and fast transient speed responses can be achieved. Moreover, compared with the conventional induction motor drives for industrial blowers, the proposed PMBLDC drives can achieve much higher efficiency with less volume and weight.

Adaptive disturbance rejection in an optical trap

This article presents a method for characterizing the system dynamics of a trapped particle in real-time and designing a controller to minimize disturbances to the particle's position. Specifically, adaptive system identification is used to determine the trap characteristics and the actuator transfer function describing the mirror voltage to trap position path. Using an internal model control scheme combined with a filtered-x least-mean-square algorithm, adaptive control was used to create a controller that minimizes a frequency weighted mean-squared-error. The dynamics associated with multiple particle sizes and materials were experimentally determined under different power levels, each case resulting in different system dynamics and demonstrating positive control results. The adaptive system identification and the controller presented automate the process of system identification and control design, enabling the automation of optical trap controller design.

Decentralized robust PI controller design for an industrial boiler

This paper presents a new scheme to design decentralized robust PI controllers for uncertain LTI multivariable systems. Sufficient conditions for closed-loop stability and closed-loop diagonal dominance (almost decoupling) of a multivariable system are obtained. Satisfying these conditions and robust performance of the overall system are modeled as local robust performance problems. Then, by appropriately selecting the time constants of the closed-loop isolated subsystems in the IMC (Internal Model Control) strategy, the defined local robust performance problems are solved. To design a decentralized robust PI controller for a real industrial utility boiler, a control oriented nonlinear model for the boiler is identified. The nonlinearity of the system is modeled as uncertainty for a nominal LTI multivariable system. Using the new proposed method, a decentralized PI controller for the uncertain LTI model is designed. The designed controller is applied to the real system. The simulation results show the effectiveness of the proposed methodology.

SVM Approximate-based Internal Model Control Strategy

A support vector machine (SVM) approximate-based internal model control (IMC) strategy is presented for the steam valving control of synchronous generators. The proposed SVM IMC strategy includes two main parts: SVM approximate inverse controller and uncertainty compensation in the internal model structure. The SVM inverse controller is derived directly using an input-output approximation approach via Taylor expansion, and it is implemented through nonlinear system identification without further online training. Furthermore, a robustness filter is used for uncertainty compensation in the internal model structure. Simulations show the effectiveness of the SVM IMC strategy for the steam valving control.

Control of a Batch Polymerization System Using Hybrid Neural Network ‐ First Principle Model

AbstractIn this work, the utilization of neural network in hybrid with first principle models for modelling and control of a batch polymerization process was investigated. Following the steps of the methodology, hybrid neural network (HNN) forward models and HNN inverse model of the process were first developed and then the performance of the model in direct inverse control strategy and internal model control (IMC) strategy was investigated. For comparison purposes, the performance of conventional neural network and PID controller in control was compared with the proposed HNN. The results show that HNN is able to control perfectly for both set points tracking and disturbance rejection studies.

Internal model control for shape memory alloy actuators using fuzzy based Preisach model

This paper investigates a possible application of Preisach model to control shape memory alloy (SMA) actuators using an internal model control strategy. The developed strategy consists in including the Preisach hysteresis model of SMA actuator and the inverse Preisach model within the control structure. In this work, an extrema input hystory and a fuzzy inference is utilized to replace the classical Preisach model. This allows to reduce a large amount of experimental parameters and computation time of the classical Preisach model. To demonstrate the effectiveness of the proposed controller in improving control performance and hysteresis compensation of SMA actuators, experimental results from real time control are presented.

Robust control of an ill-conditioned plant using [formula omitted]-synthesis: A case study for high-purity distillation

The goal of this paper is to provide a controller design for a high-purity distillation column where the effect of uncertainties is taken into account. A structured uncertainty model has been developed which describes the dynamics of the high-purity distillation column for the entire operating range. Further, a two-point composition control scheme using the LV-configuration is applied which yields acceptable performance specifications. The structured singular value ( μ ) is used as a tool for evaluating the achievable control performance which is defined in terms of the H ∞ -norm of the weighted sensitivity function. In addition, a decentralised PID controller based on internal model control (IMC) scheme that is optimally tuned is found to be robust. The results of simulations show good disturbance rejection and set-point tracking for the controller.

Model Based IMC Controller for Processes with Dead Time

A system with varying transportation lags has been experimentally studied for modeling. Modeling is performed using a step test. The tracer is sodium chloride solution whose conductivity is measured using an online conductivity analyzer. Based on the step response, the model parameters are determined and the lag processes are represented by a first order plus dead time (FOPDT) model. For the models developed, an internal model control (IMC) scheme is designed. Performance comparison, based on rise time, settling time, and overshoot, is done among the designed IMC controllers, conventional PID controllers, and Smith Predictor controllers. The present study depicts that IMC controllers outperform PID and Smith Predictor controllers.

Neural Network Based Modelling and Control in Batch Reactor

The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better.

Analytical Design of Decoupling Internal Model Control (IMC) Scheme for Two-Input−Two-Output (TITO) Processes with Time Delays

In this paper, a new decoupling control scheme, in terms of an internal model control (IMC) structure, is proposed for two-input−two-output (TITO) processes with time delays. Noteworthy improvement of the decoupling regulation can be achieved for the nominal system output responses, and moreover, either of the system output responses can be quantitatively regulated by virtue of the analytical relationship between the adjustable parameters of the decoupling controller matrix and the nominal system transfer matrix. The ideally optimal controller matrix is analytically derived by proposing the practically desired diagonal system transfer matrix, in terms of the robust H2 optimal performance objective. Because of the difficulty of physical implementation, its practical form is carefully configured according to whether there exist any complex right-half-plane (RHP) zeros in the process transfer matrix determinant. At the same time, tuning constraints for the proposed decoupling controller matrix to hold the co...

The study of neural network-based controller for controlling dissolved oxygen concentration in a sequencing batch reactor

The design and development of the neural network (NN)-based controller performance for the activated sludge process in sequencing batch reactor (SBR) is presented in this paper. Here we give a comparative study of various neural network (NN)-based controllers such as the direct inverse control, internal model control (IMC) and hybrid NN control strategies to maintain the dissolved oxygen (DO) level of an activated sludge system by manipulating the air flow rate. The NN inverse model-based controller with the model-based scheme represents the controller, which relies solely upon the simple NN inverse model. In the IMC, both the forward and inverse models are used directly as elements within the feedback loop. The hybrid NN control consists of a basic NN controller in parallel with a proportional integral (PI) controller. Various simulation tests involving multiple set-point changes, disturbances rejection and noise effects were performed to review the performances of these various controllers. From the results it can be seen that hybrid controller gives the best results in tracking set-point changes under disturbances and noise effects.

Investigating Control Schemes for an Ideal Thermally Coupled Distillation Column (ITCDIC)

The control schemes of an ITCDIC are addressed. A modified IMC scheme (M-IMC) is proposed to overcome model/plant mismatch of the Internal Model Control scheme (IMC). Predictive PID control (P-PID) and Adaptive Predictive control (AP-PID) schemes are also presented to improve effectively the response speed of the multi-loop PID control (M-PID) and eliminate its residual error. A detailed comparative investigation on the above five control schemes was performed. Simulation results demonstrate all the schemes are able to keep two end products within their specifications. M-IMC is the best one with the fastest response speed. AP-PID is the second choice since it is better at dealing with sudden set-point transitions and complex external disturbances than P-PID. M-PID cannot compete with AP-PID and P-PID due to its slow servo response speed and large residual error. IMC ranks last as it is extremely sensitive to changes in the operating conditions.