Internal Model Control Scheme Research Articles

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.

Decoupling control of bearingless PMSM with LSSVM inverse system and IMC

In this paper, a novel decoupling control scheme combining least squares support vector machines (LSSVM) inverse models and 2-degree-of-freedom (DOF) internal model controllers (IMC) is employed in the decoupling control system of the bearingless permanent magnet synchronous motor (BPMSM). This scheme can be used to enhance the control properties of high-precision, fast-response, and strong-robustness for the BPMSM system, and effectively eliminate the nonlinear and coupling influence. It introduces LSSVM inverse models into the original BPMSM system to constitute a decoupled pseudo-linear system. In addition, the particle swarm optimization algorithm (PSO) is used to optimize parameters of the LSSVM, which improves its fitting ability and prediction accuracy. What is more, the internal model control scheme is used to design additional closed-loop controllers, thereby improving the robustness of the entire control system. Therefore, this scheme successfully combines the advantages of the LSSVM inverse models and the internal model controller. It can enhance the stability and the static as well as dynamic properties of the whole BPMSM system while independently adjusting the tracking and interference rejection performances. The effectiveness of the proposed scheme has been verified by simulation results at various operations.

IMC-based PID control of servo motors with extended state observer

In this paper, a simple and effective internal model control (IMC) with extended state observer (ESO) is presented for the position tracking control of servo motors perturbed by uncertainties and disturbances. Firstly, a linear feedback controller is directly derived from the standard IMC principle for a prescribed tracking performance. Then, a linear ESO is introduced into the IMC-based controller for active disturbance rejection control (ADRC). However, for the ESO with a limited bandwidth, there will exist state estimation errors, which limit the tracking accuracy. To overcome this drawback, 2-degree-of-freedom IMC-based PID controller with ESO is developed for further improvement of the control accuracy. The dynamics of the tracking error is analyzed based on an order reduction technique and the ultimately bounded stability of the control system can be guaranteed. Since the IMC scheme and the ESO can be designed separately and integrated effectively to attenuate the effect of disturbances, a simple, effective and intuitive framework can be given for the analysis and synthesis of the motion control system. Finally, the effectiveness of this novel control approach is validated by comparative simulations and experiments implemented on a practical servo motor system.

Robust IMC-PID and Parameter-varying Control Strategies for Automated Blood Pressure Regulation

The present work focuses on comparing a robust and a parameter-varying control design approach for automatically regulating blood pressure in critical hypotensive patients using vasopressor drug infusion. Mean arterial pressure (MAP) response of a patient subject to vasoactive drugs is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input which limits the implementation of conventional control techniques. Two methods are examined to address the variability and the time-varying delay of the physiological response to the drug. First, a Pade approximation is used to transform the infinite-dimensional delay problem into a finite-dimensional model represented in the form of a non-minimum phase (NMP) system. A systematic parameter-varying loop-shaping control is proposed to provide the closed-loop system with stability and tracking performance in the presence of measurement noise and disturbances. Second, an internal model control (IMC) strategy is examined to design a fixed proportional-integral-derivative (PID) controller cascaded with a lag compensator by considering the time-varying model to be a perturbed uncertain system. To account for system uncertainty, the small-gain theorem is employed by which robust stability conditions are investigated. The proposed control methods are applied to critical hypotensive patient resuscitation to regulate MAP while considering the limitations posed by the time-varying parameters of the physiological response model and the large time-varying delay. Simulation results are provided and compared to evaluate the performance of the proposed control actions under various hypotensive scenarios.

Robust Trajectory Tracking in a Reactive Batch Distillation Process using Multirate Nonlinear Internal Model Control

Operating a reactive batch distillation (RBD) process in an optimal manner is of paramount importance for improving product quality and profitability in the face of changing market conditions. However, implementation of an open loop optimal control policy may lead to significant reduction in yield and amount of desired product produced when unmeasured disturbances occur during operation. In this work, an observer error feedback-based multirate nonlinear internal model control (NIMC) scheme is developed for optimal trajectory tracking in the face of unmeasured disturbances. In particular, mismatch in the initial conditions and process parameter variations that can occur during RBD operation are considered. To reduce the time required for online computations, a recently proposed reduced order RBD model [Reddy et al. Compt. Chem. Eng., 2017, 106, 40−56] is used for the controller synthesis. Because the thermodynamic model associated with the RBD process has discontinuities, a multirate version of unscented Kalman filter (UKF) has been developed on the basis of the reduced order model for solving the state estimation problem associated with the proposed NIMC formulation. The efficacy of the proposed approach has been demonstrated by simulating an optimal trajectory tracking problem in the RBD process involving production of butyl acetate. The proposed multirate NIMC formulation is found to be computationally efficient and work reasonably well when tested on the RBD process for solving the set point tracking problem in the presence of unmeasured disturbances. In particular, it is advantageous to operate the RBD process using the proposed UKF-NIMC scheme when the system is subjected to unmeasured disturbances.

Conventional and non conventional body motions modelling and control. Application to the handwriting process

AbstractThe handwriting process is one of the most important and complex activities of the human body. In recent years, modelling and analyzing and controlling it have received great attention leading to large important motion studies results. This paper attempts to present an overview of recent works about our proposed approaches and other ones to model and control this process. Hence, an overall model of the handwriting process has been proposed and developed by the co‐authors, showing the interest of using the soft computing concepts, namely neural networks and fuzzy logic. The design of this model requires several undertaken steps. Starting from an experimental approach to acquire electromyographic signals and writing positions, different conventional and non conventional models have been proposed. Besides some control strategies have been considered in particular neural and fuzzy approaches in addition to non conventional neural and neuro‐fuzzy Internal Model Control strategy. The proposed global model, inspired from the biological structure system, has been designed and validated through simulation results.

Modified fractional order IMC design based drug scheduling for cancer treatment

This paper aims to improve chemotherapy drug(s) scheduling of the drug dose injected into a patient. The existing practice has been established on experiments performed during the drug development process. In general, chemotherapeutic drugs are highly toxic in nature and directly affect the probability of a patient's survival. Therefore, a modified fractional order internal model control (MFOIMC) scheme with a minimal number of design parameters is proposed for effective drug scheduling. The proposed control scheme is a combination of fractional calculus and IMC. The fractional order IMC (FOIMC) is modified by incorporating an extra control loop with a proportional transfer function. Further, IPD and IMC control techniques are also designed for comparative analysis. The dragon fly algorithm (DA) is employed to optimize the parameters of the controller. Results show that the proposed control scheme provides a continuous and precise chemotherapeutic drug dose to the patient on a daily basis. It is also shown that MFOIMC improves IAE by 34% and 32% in comparison to IMC and FOIMC, respectively, which proves its superiority over other controllers.

A comparative study on existing and new methods to design internal model controllers for non-square systems

This paper focuses on a challenging problem in the internal model control (IMC) strategy: the model inversion to design the IMC controller for non-square systems. Several existing approaches for the synthesis of a specific inversion of the identified model will be presented in this paper to deal with the differences between the system’s inputs and outputs numbers. The non-square effective relative gain is firstly presented. It consists of the measurement of interactions between the loops of the system in order to square the system and make it invertible. The equivalent transfer function method is presented as well. It is based on tuning the pseudo-inverse of the process to design the internal model controller. These methods are then compared with a novel proposed model inversion approach based on virtual outputs method. Virtual adding is considered in order to obtain an invertible square transfer matrix to design the internal model controller. This simple yet effective method ensures robust control performance. Its efficiency and availability, as compared with other presented methods, is illustrated through simulations on an overactuated system with three inputs/two outputs.

Robust internal model control of servo motor based on sliding mode control approach

This paper proposes a robust internal model control (IMC) based on sliding mode control (SMC) approach for high-performance motion control of a servo motor subject to uncertainties and/or disturbances. The proposed control strategy considers not only the simplicity and intuition of the IMC-based controller for a prescribed tracking performance but also the effectiveness of the SMC scheme to guarantee the robustness of the servo system. Since the performance of the IMC-based controller can be analyzed via a SMC structure, a robust control law based on the SMC technique is introduced into the IMC scheme to decrease the sensitivity to uncertainties and enhance the resistance to disturbances. Moreover, the 2-degree-of-freedom IMC integrating the robust SMC scheme is developed to further improve the control performance. The stability is analyzed based on Lyapunov theory, and the theoretical results show that a prescribed transient tracking performance and a final tracking accuracy of the servo system can be guaranteed. Comparative simulations and experiments are investigated to verify the high performance nature of the proposed control strategy.

An Adaptive Internal Model for Load Frequency Control Using Extreme Learning Machine

As an important part of a power system, a load frequency control has to be prepared with a better controller to ensure internal frequency stability. In this paper, an Internal Model Control (IMC) scheme for a Load Frequency Control (LFC) with an adaptive internal model is proposed. The effectiveness of the IMC control has been tested in a three area power system. Results of the simulation show that the proposed IMC with Extreme Learning Machine (ELM) based adaptive model can accurately cover the power system dynamics. Furthermore, the proposed controller can effectively reduce the frequency and mechanical power deviation under disturbances of the power system.

Recursive orthogonal least squares based adaptive control of a polymerisation reactor

ABSTRACTA Nonlinear Internal Model Control (NIMC) scheme, based on a recursively updated Radial Basis Function Network (RBFN), is applied to control the nonlinear Polymerisation process. Recursive Orthogonal Least Squares (ROLS) algorithm recursively updates the weighting matrix of RBFN in real time, such that modelling errors are minimised. In addition to the ROLS based adaptive IMC, a non-adaptive IMC, based on the fixed Orthogonal Least Squares (OLS) algorithm and a normal PID controller are also applied to control the polymerisation process. The simulated results show the effectiveness of the ROLS based controller in terms of response time to step changes and disturbance rejection capabilities over the classical PID controller and the fixed OLS based IMC. Also, the proposed algorithm fits well in the IMC framework.

Graphic IMC-PID tuning based on maximum sensitivity for uncertain systems

Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.

Linear output regulation with dynamic optimization for uncertain linear over-actuated systems

This paper considers the linear output regulation problem for uncertain over-actuated plants. The general form of input redundancy considered in this work implies the existence of multiple control inputs and state trajectories compatible with a prescribed reference for the output. On-line selection, according to certain performance criteria, of the most suitable of these inputs-state trajectories leads to a linear output regulation problem with dynamic redundancy allocation. We present a solution that augments the well known internal model control scheme with two additional dynamical systems. The first one, named annihilator, parametrizes the inputs and the corresponding state trajectories that are invisible from the output. The second one, named redundancy allocator, dynamically selects the best solution according to a predefined performance criterion. We derive explicit solutions for the performance criterion equal to relaxed 1, 2, and ∞- norms of the plant input. This set-up is a particular case of the dynamic redundancy allocation problem named dynamic input allocation. The proposed solutions can be implemented in an error feedback form and are especially suitable for optimizing sparsity, power and amplitude of the control input. Finally, structural stability, robustness and existence of a unique steady-state are proven.

A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases.

Enhanced IMC synthesis for tracking control of magnetic levitation system

This paper presents an enhanced internal model control (EIMC) scheme for a time-delayed second order unstable process, which is subjected to exogenous disturbance and model variations. Even though the conventional internal model control (IMC) can provide an asymptotic tracking response with desired stability margins, the major limitation of conventional IMC is that it cannot be applied for an unstable system because a small exogenous disturbance can trigger the control signal to grow unbounded. Hence, modifying the conventional IMC structure to guarantee the internal stability, we present an EIMC scheme which can offer better trade-off between setpoint tracking and disturbance rejection characteristics. To improve the load disturbance rejection characteristics and attenuate the effect of sensor noise, we solve the selection of controller gains as an H∞ optimization problem. One of the key aspects of the EIMC scheme is that the robustness of the closed loop system can be tuned via a single tuning parameter. The performance of the EIMC scheme is experimentally assessed on a magnetic levitation plant for reference tracking application. Experimental results substantiate that the EIMC scheme can effectively counteract the inherent time delay in the model and offer precise tracking, even in the presence of exogenous disturbance. Moreover, by comparing the trajectory tracking performance of EIMC with that of the proportional integral velocity (PIV) controller through cumulative power spectral density (CPSD) of the tracking error, we show that the EIMC can offer better low frequency servo response with minimal vibrations.

Design of fractional PID for Load frequency control via Internal model control and Big bang Big crunch optimization

This paper presents a new technique for the design of an optimal fractional order PID controller for Load frequency control (LFC) in power systems. The proposed approach utilizes a unique combination of Big Bang Big Crunch (BB-BC) algorithm which is a recent soft computing technique and internal model control (IMC) scheme for the design of a fractional order PID controller and it also unifies the notion of order diminution with controller design. A detailed mathematical description of the proposed approach is elucidated in the paper. Since BB-BC is a stochastic search technique, hence a thorough statistical analysis of the response specifications is performed. To demonstrate the effectiveness of the proposed approach, an exhaustive comparative analysis in terms of time response specifications and performance indices is also carried out. It is inferred that the proposed approach is highly efficient tool and outperforms the recent techniques in the literature.

Non-ideal modelling and IMC based PID Controller Design of PWM DC-DC Buck Converter

This paper presents a non-ideal model of DC-DC PWM buck converter considering the parasitic elements (or non-idealities) such as equivalent series resistances (ESRs) of inductors and capacitors, parasitic resistances of semiconductor devices (diode, MOSFET) during conduction and the forward voltage drop of the diode. Incorporating this non-ideal model, a proficient PID control technique is proposed based on the internal model control (IMC) strategy. The salient features of proposed control methodology are: (i) tuning is such that the controller yields the desired bandwidth; (ii) unlike the conventional IMC-PID, the PID parameters are obtained by direct formula without trial and error. The proposed control scheme is simulated in MATLAB/SIMULINK and validated on a hardware setup using DSPACE DS1104 to confirm the superior results under variation of input voltage, reference voltage and load.

Internal-model control of vehicle chassis based on wavelet-network dynamic inversion method

An internal-model control strategy of vehicle chassis system is proposed based on wavelet-network dynamic inversion method, to eliminate the coupling effect among the chassis subsystems. By analysing the chassis system reversibility, the paired relationship of the input and output variables is determined. Then, a wavelet-network dynamic inversion model of vehicle chassis system is obtained and connected in series with the original system. The chassis system is decoupled into three independent pseudo-linear systems, and decoupling between the control loops of vehicle chassis system is realised. An internal-model controller is designed to improve the system response. Further, a simulation and a driver-in-loop test using LabVIEW PXI and veDYNA are conducted to verify the control strategy efficacy. The results show that the proposed control strategy can eliminate the interference and coupling between the active front-wheel steering, direct yaw-moment control, and active suspension system, and improve the vehicle handling and status tracking stability.

Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control

Abstract A fully-automated controller in the artificial pancreas (AP) system designed to regulate blood glucose concentration can give better lifestyle to a type 1 diabetic patient. This paper deals with evaluating the benefit of fully-automated online-tuned controller for the AP system over offline-tuned and semi-automated controller based on internal model control (IMC) strategy. The online-tuned controller is fully-automatic in the sense that it can automatically deal with intra- and inter-patient variabilities and compensate for unannounced meal disturbances without any prior knowledge of patient parameters, patient specific characteristics or patient specific input–output data. A data driven Volterra model of patients is used to design IMC algorithms. For online-tuned controller, the Volterra kernels of the model are computed online by recursive least squares algorithm. The IMC algorithms are evaluated using different scenarios in the UVA/Padova metabolic simulator for validation, comparison with a fully-automatic zone model predictive controller and robustness analysis. Unlike offline-tuned IMC and semi-automated IMC, the online-tuned IMC in the AP system performs satisfactorily for every patient condition without patients’ intervention. Experimental results show that the online-tuned IMC compensates unannounced meal disturbances with low frequency of hypoglycemic events and most importantly, with low insulin infusion even with variations in insulin sensitivity, in the presence of irregular amounts of meal disturbances at random times, and in the presence of very high noise levels in the sensors and actuators. Patients experience hypoglycemia 0.46%, 1.01% and 20% of the time using online-tuned, offline-tuned and semi-automated IMC respectively when the insulin sensitivity is increased by +20%.

IMC Based Fractional Order Controller for Three Interacting Tank Process

In model based control, performance of the controlled plant considerably depends on the accuracy of real plant being modelled. In present work, an attempt has been made to design Internal Model Control (IMC), for three interacting tank process for liquid level control. To avoid complexities in controller design, the third order three interacting tank process is modelled to First Order Plus Dead Time (FOPDT) model. Exploiting the admirable features of Fractional Calculus, the higher order model is also modelled to Fractional Order First Order Plus Dead Time (FO-FOPDT) model, which further reduces the modelling error. Moving to control section, different IMC schemes have been proposed, where the fractional order filter is introduced along with the conventional integer order filter. Various simulations have been performed to show the greatness of Fractional order modelled system & fractional order filters over conventional integer order modelled system & integer order filters respectively. Both for parameters estimation of reduced order model and filter tuning, Genetic Algorithm (GA) is being applied.