Internal Model Control Approach Research Articles

A nonsmooth IMC method for mechanical systems with backlash

This paper proposes a discrete-time nonsmooth internal model control (NSIMC) approach for mechanical transmission systems described by so-called sandwich system with backlash. In this method, a dynamic compensator is introduced to compensate for the effect of the input linear subsystem. Thus, the sandwich systems with backlash can be simplified as a pseudo-Hammerstein system with backlash. The corresponding NSIMC strategy is designed to control this system. The design procedure of the controller is presented based on the analysis on the robust stability by considering the model errors involved with the effect of backlash as well as the compensated error of the input linear subsystem. Moreover, as the model is switched among the different operating zones, the robust filters are proposed to guarantee the robust stability and satisfactory control performance of the system.

Grey‐Box Model‐Based Vibration Isolation Using a Dielectric Elastomer Actuator

AbstractThis contribution reviews the use of a grey‐box model of a tubular dielectric elastomer actuator for active vibration isolation of a payload, subject to ground vibrations. Two different scenarios, measurable ground vibration plus varying payload and unmeasurable ground vibration with constant payload, are examined. The choice of control approach is tailored to the two scenarios. The overall aims of the work are to (i) provide insight into the possible usefulness of the grey‐box model for active vibration isolation (AVI) for the two different scenarios, and (ii) to provide a qualitative assessment of the practicalities of each control approach examined. The initial part of the paper provides a validation of the grey‐box model for three different sizes of tubular DE actuator indicating that the model is good over the frequency range of interest. For the first scenario, measurable ground vibration with time‐varying payload, adaptive feedforward control, which is an accepted approach for AVI with a measureable disturbance, is used. Here the grey‐box model is only used within the cancellation path, which filters the reference signal by the plant dynamics in order to provide stable estimator performance. As the plant dynamics change with the changing payload the possibility of the estimator becoming unstable arises. Auto‐tuning of the grey‐box model as the payload changes, and hence the cancellation path, is shown to maintain stable estimator performance, though the auto‐tuning procedure is currently taking too long. For the second scenario the ground vibrations are assumed unmeasurable though the payload is assumed constant. The internal model control (IMC) approach was chosen not only because its design is model dependent but also to generally consider the appropriateness of IMC for active vibration applications. Process/model mismatch studies provided a choice of robustness filter time constant to satisfy stability considerations for all three actuators. Compared to adaptive feedforward control the vibration attenuation performance is likely to be slightly inferior because there is always a tendency to be conservative with the choice of robustness filter. Overall, the IMC approach tends to provide a degree of transparency in the design process as well as providing greater flexibility, which implies it is a useful approach for active vibration control applications, as long as a good model is available.

Neuro-Fuzzy-Based Control for Parallel Cascade Control

Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.

Design of a multivariable internal model controller based on singular value decomposition

In this article, a novel internal model control (IMC) approach based on singular value decomposition (SVD) is proposed for the control of multiple-input–multiple-output (MIMO) systems with multiple time delays. This approach achieves decoupling using a compensation term and improves the robustness using SVD in the inverse of the steady-state gain matrix of process. Meanwhile, a novel filter is designed for decoupling and fast response speed of multivariable systems with multiple delays. The design of the controller can be extended to non-square systems where there are more inputs than outputs. Examples are included to illustrate the effectiveness of the method. © 2012 Canadian Society for Chemical Engineering

Inventory control of supply chains: Mitigating the bullwhip effect by centralized and decentralized Internal Model Control approaches

In this paper, a two-degrees-of-freedom Internal Model Control structure is incorporated in production inventory control for a supply chain system. This scheme presents an intuitive and simple parametrization of controllers, where inventory target tracking and disturbance (demand) rejection in the inventory level problems are treated separately. Moreover, considering that the lead times are known, this scheme presents a perfect compensation of the delay making the stabilization problem easier to handle. This control structure is formulated for a serial supply chain in two ways (by using a centralized and a decentralized control approach). The behavior of these inventory control strategies is analyzed in the entire supply chain. Analytical tuning rules for bullwhip effect avoidance are developed for both strategies. The results of controller evaluations demonstrate that centralized control approach enhances the behavior with respect to the inventory target tracking, demand rejection and bullwhip effect in the supply chain systems.

Inverse grey-box model-based control of a dielectric elastomer actuator

An accurate physical-based electromechanical model of a commercially available tubular dielectric elastomer (DE) actuator has been developed and validated. In this contribution, the use of the physical-based electromechanical model to formulate a model-based controller is examined. The choice of control scheme was dictated by the desire for transparency in both controller design and operation. The internal model control (IMC) approach was chosen. In this particular application, the inverse of the linearized form of the grey-box model is used to formulate the IMC controller. To ensure consistent control performance across the operating range of the DE actuator, a gain scheduling term, which linearizes the operating characteristics of the tubular dielectric elastomer actuator, is developed and implemented in series with the IMC controller. The IMC-based approach is investigated for servo control of the DE actuator position as well as its ability to provide vibration isolation of a payload subject to ground vibration.

Simultaneous automatic tuning of cascade control systems from closed-loop step response data

This study presents a novel automatic tuning method for cascade control systems in which both primary and secondary controllers are tuned simultaneously using a single closed-loop step test. The proposed technique identifies the required process information with the help of B-spline series representation for the step responses. The two proportional–integral–derivative (PID) controllers are then tuned using an internal model control (IMC) approach. Considering the rationale of cascade control, the secondary controller is designed for faster disturbance attenuation. Without requiring an additional experiment, the primary controller is designed based on an identified process model that accurately accounts for inner loop dynamics. Finally, this study includes robustness considerations in the controller tuning process, and develops explicit guidelines for the selection of the IMC tuning parameters, completing the automatic tuning procedure for cascade control systems. The proposed method is robust to measurement noise because of the filtering property of the B-splines, and can provide superior control performance for both set-point tracking and disturbance rejection. Simulation examples demonstrate the effectiveness of the proposed automatic tuning method.

Neural Internal Model Control for tracking Unknown Nonaffine Nonlinear Discrete-time Systems under External Disturbances

of nonlinear dynamic plants under external disturbances is presently an active area of research. The main difficulty felt in the tracking of nonlinear dynamic plants under external disturbances is the computational complexity in control design. This paper presents a simple neural network internal model control (IMC) approach for off-line tracking of unknown nonaffine nonlinear discrete time systems subject to external disturbances. The proposed control scheme is based on the neural network plant model and the inverse model. These models are determined using input output data. The final neural network IMC approach, including network plant model and the inverse model, is work off-line for tracking nonaffine nonlinear discrete time systems subject to external disturbances. Simulation results have been presented toward the end of the paper to illustrate the effectiveness of the proposed control strategy for tracking unknown nonaffine nonlinear discrete-time systems with and without external disturbances.

Identification and Controller Tuning of Cascade Control Systems Based on Closed-Loop Step Responses

This paper presents a new automatic tuning method for cascade control systems based on a single closed-loop step test. The proposed method identifies the required process information with the help of B-spline series expansions for the step responses. The two PID controllers are then tuned using an internal model control (IMC) approach. The secondary controller is designed for enhanced disturbance rejection, and the primary controller is designed, without requiring an additional test, based on an identified process model that accurately accounts for the inner loop dynamics. The desired levels of system robustness explicitly guide the selection of the IMC tuning parameters. The proposed method is robust to measurement noises because of the filtering property of the B-splines, and can provide satisfactory control performance. Simulation examples confirm the effectiveness of the proposed method.

Endpoint perfect tracking control of robots — A robust non inversion-based approach

This paper presents a simple and robust non inversion-based perfect tracking control (RNIBPTC) strategy for robot manipulators. The proposed approach is capable to eliminate the environmental problems arising from classic feedforward control design and so guarantees an appropriate level of robustness of control system to uncertainties including external disturbances, unmodeled dynamics, friction force and variation of payload. Extensive simulation results performed using a two degree-of-freedom actuated elbow robot prove the effectiveness of the proposed approach. The results are also compared to those obtained from Internal Model Control approach. Using free model of system in control law design is a considerable point in the field of robot manipulator control.

IMC based feedforward controller framework for disturbance attenuation on uncertain systems

This paper presents a generalization of the Internal Model Control (IMC) approach to feedforward control action generation. As is well known, one of the distinctive features of the IMC formulation is the possibility to distinguish between the nominal and uncertain situations. However the consideration of the presence of uncertainty for the feedforward part of the controller is not as simple as that for the feedback one. In this sense, what is proposed in this paper provides a more systematic way of addressing a robust feedforward design. In addition, being one of the major drawbacks of the Internal Model Control to have to deal with stable systems, the generalization presented here allows for unstable plants as well as unstable disturbance models.

Comparing Internal Model Control and Sliding-Mode Approaches for Vehicle Yaw Control

In this paper, the problem of vehicle yaw control using a rear active differential is investigated. The proposed control structure employs a reference generator designed to improve vehicle handling, a feedforward contribution that enhances the transient system response, and a feedback controller. Due to system uncertainties and the wide range of operating situations, which are typical of the automotive context, a robust control technique is needed to guarantee system stability. Two different robust feedback controllers, which are based on internal model control and sliding mode methodologies, respectively, are designed, and their performances are compared by means of extensive simulation tests performed using a realistic 14-degree-of-freedom (DOF) model of the considered vehicle. The obtained results show the effectiveness of the proposed control structure with both feedback controllers and highlight their respective benefits and drawbacks. The presented comparative study is a first step to devise a new mixed control strategy that is able to exploit the benefits of both the considered techniques.

Internal Model Control for Dynamic Systems With Preceded Backlash

A discrete-time internal model control approach for nonsmooth nonlinear systems described by a pseudo-Hammerstein model with backlash is presented. In this method, the controlled system is described by the pseudo-Hammerstein model, and the corresponding inverse model is constructed. Considering the existence of the model mismatch, the internal model control is implemented. As the model is switched among the different operating zones, the piecewise robust filters are proposed to improve the robust stability and transient performance of the control system. Finally, the simulation results based on the proposed method for a mechanical transmission system are presented.

An Internal Model Control Approach to Mid-Ranging Control

Abstract Existing tuning rules for mid-ranging control can be improved. In this paper a novel strategy for mid-ranging control based on Internal Model Control (IMC) principles is presented. The design reformulates mid-ranging control specifications in terms of classical bandwidth and sensitivity requirements. The performance of this design is demonstrated through simulation studies. The overall benefits of the IMC design are that it provides transparent and flexible tuning, and that it offers a natural framework for anti-windup. Both classical IMC and modified IMC structures are considered for anti-windup. Their performance during saturation is demonstrated through simulation studies, where minimal degradation is observed.

Model Classification Applied to the Autotuning of Industrial Proportional−Integral−Derivative Regulators

This paper presents a technique for the automatic tuning of industrial proportional-integral-derivative (PID) regulators, based on a classifier that selects an appropriate model structure for the system to be controlled starting from a simplified and normalized representation of its unit step response. The PID tuning is then performed within the well-established framework of the internal model control approach, showing that structural information is particularly beneficial for model-based synthesis. Both simulations and a physical application, based on a laboratory thermal process, are reported.

Optimal and robust feedback controller estimation for a vibrating plate

This paper presents a method to estimate the H 2 optimal and a robust feedback controller by means of Subspace Model Identification using the internal model control (IMC) approach. Using IMC an equivalent feed forward control problem is obtained, which is solved by the Causal Wiener filter for the H 2 optimal controller. The robust variant, called the Cautious Wiener filter, optimizes the average performance w.r.t. probabilistic model errors. The identification of the Causal and Cautious Wiener filters are control-relevant. The method is illustrated by experiments on a 4-inputs 4-outputs vibrating plate with additional mass variation.

Generalized Active Control of Vibrations in Helicopters

The problem of active control of vibrations in the main rotor of helicopters is dealt with in the individual blade control framework. We propose a controller valid for a generic e ight condition by resorting to a linear parametrically varying (LPV)controltechniquecoupled with theso-called internalmodelcontrol (IMC)principle. The problem can be reformulated as the one of rejecting a periodic disturbance having known frequency acting on the output of a suitable linear time-varying dynamic model. The main advantage of our control technique is the e exibility of the LPV ‐IMC approach: It not only can be used with the generic e ight condition, but it also can be adapted to comply with a rotor blade model having a complex structure. We obtain a unique controller that automatically gets adapted to the measured advance ratio. An alternative to this control rationale would be to resort to the gain scheduling of periodic controllers, one for each forward e ight condition. However, as indicated by our previous research, this would require keeping memory of a large number of control laws in the onboard computer. Furthermore, an H1 control strategy is considered to take into account the effects of both unmodeled dynamics and unmodeled disturbances.

Modified internal model control approach to the design and tuning of linear digital controllers

A new approach to the design and tuning of two degrees of freedom linear digital controllers is presented. The important properties of the proposed method are that the controller structure is directly obtained from the model used and that a small number of adjustable parameters can be easily tuned manually to obtain high closedloop system performance both in the set-point response and in the load disturbance rejection. The method is applicable on both type-0 and-1 plants and is effective in the presence of a poorly damped complex-conjugate pole pair, a non-minimum phase zero or a dead-time. The proposed design and tuning procedures are illustrated by simulation examples, including the analysis of the IFAC '93 benchmark control problem.

Run to run control in tungsten chemical vapor deposition using H2/WF6 at low pressures

Run to run control with an Internal Model Control (IMC) approach has been used for wafer state (thickness) control in the tungsten chemical vapor deposition (CVD) process. The control implementation was preceded by establishing a stable wafer state thickness metrology using in situ mass spectrometry. Direct reactor sampling was achieved from an Ulvac ERA-1000 cluster tool module during the H2/WF6 W CVD process at 0.5 Torr for temperatures 350–400 °C using a 300 amu closed ion source Inficon Transpector system. Signals from HF product generation were used for in-process thickness metrology and compared to ex situ, postprocess thickness measurements obtained by microbalance mass measurements, providing a metrology accuracy of about 7% (limited primarily by the very low conversion efficiency of the process used, ∼2%–3%). A deliberate systematic process drift was introduced as a −5 °C temperature change for each successive wafer, which would have led to a major (∼50%) thickness decrease over ten wafers in an open loop system. A robust run to run (RtR) control algorithm was used to alter the process time in order to maintain constant HF sensing signal obtained from the mass spectrometer, resulting in thickness control comparable to the metrology accuracy. The efficacy of the control algorithm was also corroborated by additional experiments that utilized direct film weight measurements through the use of the microbalance. A set of simulations in Matlab® preceded the control implementation and helped in tuning the controller parameters. These results suggest that in situ chemical sensing, and particularly mass spectrometry, provide the basis for wafer state metrology as needed to achieve RtR control. Furthermore, since the control was consistent with the metrology accuracy, we anticipate significant improvements for processes used in manufacturing, where conversion rates are much higher (40%–50%) and corresponding signals for metrology will be much larger.

Non-linear control of continuous bioreactors

Control of bioreactors has achieved importance in the recent years. This may be due to the fact that they are difficult to control which may be attributed to its nonlinear dynamic behavior. The model parameters of the bioreactor also vary in an unpredictable manner. The complexity of the biochemical processes inhibits the accurate modeling and also the lack of suitable sensors make the process state difficult to characterize. Considerable emphasis has been placed on the control of fed-batch fermentors because of their prevalence in industries. However, when production of biomass is to be optimized, continuous operation is desirable. Several procedures are available for the nonlinear control of processes, viz., differential geometric approach, internal model control approach, reference synthesis technique, predictive control design, etc., but the major disadvantage of these approaches is the computational time required to perform the prediction optimization. In this study, a nonlinear controller based on a polynomial discrete time model (NARMAX) is evaluated for its performance on a fermentor. It can be shown that a nonlinear self-tuning controller based on NARMAX model can be extended to the control of fermentors. The response is smooth for both load and setpoint changes even when process parameters are assumed to be zero and uncertainty in parameters are present and in the presence of controller constraints. The control action can be made more or less robust by changing the design parameters appropriately. Therefore, nonlinear self-tuning controller is suitable for control of industrial processes.