Adaptive Internal Model Control Research Articles

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.

An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing

This paper investigates a practical adaptive internal model control (IMC) for an unmanned surface vehicle (USV) with unknown time-varying nonlinear model parameters and environmental disturbances. Firstly, an internal model of the USV is presented using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Then, an adaptive neural IMC controller is designed for the trajectory tracking of USV by using the IMC method. The internal model and the controller are updated by an error threshold algorithm. The proposed control scheme comprises of a trajectory guidance module via the Line-of-Sight (LOS) guidance method and a tracking control module designed by IMC theory. Under the proposed control scheme, the development processes of the vehicle platform and the control algorithms are described, and accurate tracking control can be achieved. Finally, the results of simulation and field experiments are presented and discussed to validate the effectiveness of the proposed control scheme.

Adaptive Internal Model Control for a Flexible Wing With Unsteady Aerodynamic Loads.

This article proposes adaptive internal model controls for the collocated output regulation of a flexible wing, where distributed disturbances, boundary disturbances, and references are from an exactly unknown exosystem. Observer-based tracking error feedback controls are first designed to address the robust output regulation in case of a known exosystem matrix. If the exosystem has an unknown matrix, an adaptive observer is further proposed with the observer error system converging to zero exponentially. Then, we can obtain adaptive observer-based controls by combining adaptive observers and observer-based controls, which are able to regulate the tracking errors toward zero in case of the exactly unknown disturbances and references. The corresponding closed-loop system is proved to be internally asymptotically stable. A simulation example is further provided for adaptive internal model control of the wing system.

The closed-loop control method based on dual-port adaptive internal model control for fine image stabilization of space telescopes.

In view of the complex working environment of space astronomical telescopes, the influence of various disturbance sources on the imaging quality cannot be ignored. This paper focuses on compensating for the space telescope line-of-sight (LOS) deviation and suppressing the low-frequency disturbance problem in astronomical observation. A closed-loop control method based on dual-port adaptive internal model control (AIMC) for the fine image stabilization system (FISS) was proposed. To be specific, the fine guidance sensor (FGS) as the high-precision detection unit of the FISS calculates the telescope LOS deviation and sends it to the controller unit in real time. The controller unit drives the large-aperture fast steering mirror (FSM), which performs high-precision two-dimensional rotation to compensate for the telescope LOS deviation, according to the dual-port AIMC control algorithm. Moreover, the dual-port AIMC control method adds an AIMC loop on the basis of the feedback loop and adjusts the filter parameters adaptively according to the target angular velocity of the FSM, achieving higher disturbance suppression capability. The experimental results verify that the control method proposed can effectively compensate for the LOS deviation and suppress the composite frequency disturbance. In the 0-8Hz frequency band, the power spectral density integral values of the star centroid deviation in the X and Y directions of the FGS are, respectively, suppressed by 97.38% and 98.38%.

IOT based adjustment mechanism for direct reference model adaptive IMC to support voltage sag in DFIG wind farm

Sustainable energy sources are valuable energy sources.Renewable energy production boosts the economic status of a country. Wind energy is one of the most abundant renewable energy sources, and as a result, the technology to harvest energy from the wind is growing rapidly around the world. As load centers are far away from renewable energy sources, electricity must be transferred over long distances. The most common problems with power fluctuations are caused by long-range voltage sag riding (VSRT). DFIG (Doubly Fed Induction Generator) is very popular in wind energy conversion systems due to its variable speed, high energy collection, efficiency, excellent design and unique control of phase side converters and rotor side converters. Wind speed monitoring is done with the help of Internet of Things (IOT). This paper described the use of training networks in developing adjustment algorithms for direct reference model adaptive IMC for DFIG wind farms. Here, a novel training-based neural network MIT (NNMIT) adjustment mechanism using neural network method is developed and implemented in direct reference model adaptive IMC to improve the performance of the controller during voltage sag. Direct and quadrature axis rotor current controllers are developed and the resulting DFIG is balanced with the FuzzyMIT correction mechanism in the sag ride through the conditions in the wind farm. Improvements across the voltage sag are identified and presented using NNMIT. The proposed NNMIT attain 0.15% torque ripple and 1 ms response time better than the existing FuzzyMIT method. The proposed method preserves high accuracy ranges of 97.88% than the existing method. This approach gives better performance than other control design methods which assume that the flux in the stator is constant in amplitude.

Current Harmonic Suppression of BLDC Motor Utilizing Frequency Adaptive Repetitive Controller

Compared to the proportional-integral strategy, the repetitive control strategy possesses high suppression ability for the alternating current (AC) harmonics of control signals. Thus, RC controllers are widely used in closed-loop control systems to suppress the periodic harmonics. In order to further improve the brushless DC (BLDC) motor operation performance, a frequency adaptive repetitive controller (FARC) is proposed, and then a novel current loop scheme that concatenation of proportional-integral controller (PIC) and FARC controller is established in this paper. Firstly, due to the real sampling number of the delay element in the BLDC, the motor control system may not be an integer, the designing process of the FARC parameters was studied, and an adaptive internal model controller and a novel decomposition rule for FARC were designed based on Lagrange interpolation theory. Secondly, the PIC parameters were analyzed through three-dimensional and two-dimensional images of the frequency characteristics. Furthermore, a composite controller that added a forward channel in the novel current loop was proposed, and the stability of the control system used the composite controller was analyzed through Lyapunov theory. It should be noted that the analysis of FARC mainly focused on the simplified structure and the parameter optimization, which is usually ignored in the previous studies. Finally, the BLDC motor control system model was established through Matlab/Simulink software, and the operation performances of the BLDC motor control system utilizing different current loop controllers were studied. The simulation results show that the proposed FARC can reduce current distortion and torque ripples, thus, the BLDC motor operation performances can be improved effectively.

Performance improvement for PMSM control system based on composite controller used adaptive internal model controller

The dead-zone time is inevitable during the inverter working process, thus, the relationships between the input variables and the output variables of the permanent magnet synchronous motor (PMSM) control system exhibit nonlinearity characteristic. Additionally, the PMSM control system suffers from some drawbacks, such as high amount current harmonics and large torque ripples. In order to improve the operation performance for the PMSM control system, a composite controller for the current loop is proposed in this paper, which can enhance the current harmonics suppression ability. Firstly, the components of the current harmonics are analyzed. The parameters of the proportional controller are analyzed through three-dimensional image and two-dimensional image of frequency characteristics. In view of the real sampling number in the delay element maybe not an integer, an adaptive internal model controller is designed for the repetitive controller. Thus, a composite controller consist of a proportional controller and the improved repetitive controller is proposed in this paper. The parameters of the improved repetitive controller are also studied. Furthermore, the stability of the control system used the proposed composite controller is analyzed through Lyapunov theory. Finally, the PMSM control system model is established through Matlab/Simulink software, and the operation performances of the PMSM control system used different current loop controllers are studied. And the simulation results show that the proposed composite controller can suppress the current harmonics and the torque ripples effectively.

Adaptive output regulation of the adaptive optics system

Deformable mirrors are very efficient optical devices that are used in correcting wavefront aberrations and are core elements of adaptive optics systems. Its promising success allows it to operate in a large field of applications. Hence, one of the most important problems in the control of adaptive optics systems is to design a controller that can achieve perfect wavefront correction in completely unknown environmental conditions. To achieve this goal, this study proposes an adaptive internal model control design for deformable mirrors. Considering deformable mirrors as a flexible plate equation, an adaptive internal model-based controller is designed and the effectiveness of the proposed method despite abrupt changes in disturbance is proven by simulation results.

Adaptive Internal Model Control of SCR Denitration System Based on Multi-Objective Optimization

Many thermal power plants use selective catalytic reduction (SCR) systems to complete flue gas denitration. The system has the characteristics of large inertia, large delay and nonlinearity. At the same time, it is difficult to use the controller under a single working condition to meet the control task of the system under the full working conditions. In order to solve the above control difficulties, This paper proposes a two-degree-of-freedom internal model control (2-DOF-IMC) method based on controller parameter adaptation and multi-model adaptation to realize the control of the full working conditions of the SCR system. First, the intelligent identification method based on Differential evolution quantum particle swarm optimization (DEQPSO) is used to obtain the sub-model of ammonia injection-SCR outlet Nitrogen Oxide (NOx) concentration in typical working conditions. A 2-DOF-IMC controller under each sub-model is established. Then, aiming at system set point tracking performance, anti-disturbance performance, and control energy consumption, the Pareto optimal solution of each controller is obtained by using the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm based on chaotic mutation. Finally, the controller parameter adaptive strategy is designed based on the Takagi-Sugeno (T-S) fuzzy model, and the multi-model adaptive strategy is designed based on the recursive bayesian probability weighting. Through the dynamic performance test and the anti-disturbance performance test, it is verified that the 2-DOF-IMC based on controller parameter adaptation and multi-model adaptation has good control performance and adaptability to all working conditions. The method proposed in this paper combines the advantages of multi-parameter and multi-model strategies, further improves the control quality, and provides a new solution for the control of thermal power plant SCR system.

Adaptive Internal Model Control for Automotive Steer-by-Wire System with Time Delay

Adaptive Internal Model Control for Automotive Steer-by-Wire System with Time Delay

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

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

Adaptive Internal Model Based Control of the RGF Using Online Map Learning and Statistical Feedback Law

Residual gas fraction (RGF) defined as the ratio of residual gas mass to the mass of total gas burned in one cylinder is an important variable for managing combustion quality and combustion mode. This article proposes an adaptive internal model control (AIMC) based framework that synthesizes on-board map calibration and statistical feedback criterion to track a desired RGF target. The model structure of plant dynamics (or its inverse) in AIMC is derived as a cascade combination of a steady mapping and a parameter-varying variable valve timing (VVT) dynamics (or inverse). The on-board map calibration algorithm is employed to adapt the operating-point-dependent steady mapping since the system behavior may change. Then, a VVT compensatory controller in the AIMC was designed based on the online identification of the parameter-varying VVT dynamics model and its inverse. Moreover, a statistical criterion, namely, the hypothesis test was creatively introduced in the feedback channel of the AIMC to filter out the stochastic noise of the RGF. The experiment results show that the AIMC-based framework significantly improves the RGF tracking performance in both steady and transient states.

LMI Stability-Constrained Identification for Composite Adaptive Internal Model Control

Internal model control (IMC), which explicitly incorporates a plant model and a plant inverse model as its components, has an intuitive control structure and simple tuning procedure. Within the IMC structure, we propose composite adaptive IMC (CAIMC) which simultaneously identifies the plant and the plant inverse to minimize modeling errors and further reduce the tracking error. In this paper, the design procedure of CAIMC is generalized to an $n$ -th-order SISO plant. The main challenge in the generalization is to find an identification algorithm for an $n$ -th order system that satisfies the stability constraint, while assuring closed-loop stability. In the literature, stability-constrained identification has been formulated as a convex programming problem by re-parameterizing the constraint as a linear matrix inequality, but boundedness and continuity of the estimated parameters, which are critical for closed-loop stability of an adaptive control algorithm, are not guaranteed. We propose a modified stability-constrained identification method with established boundedness and continuity properties. Closed-loop stability and asymptotic performance of CAIMC are then established under proper conditions. The effectiveness of the proposed algorithm is demonstrated with an example.

Distributed load-side control: Coping with variation of renewable generations

This paper addresses the distributed frequency control problem in a multi-area power system taking into account of unknown time-varying power imbalance. Particularly, fast controllable loads are utilized to restore system frequency under changing power imbalance in an optimal manner. The imbalanced power causing frequency deviation is decomposed into three parts: a known constant part, an unknown low-frequency variation and a high-frequency residual. The known steady part is usually the prediction of power imbalance. The variation may result from the fluctuation of renewable resources, electric vehicle charging, etc., which is usually unknown to operators. The high-frequency residual is also unknown and treated as an external disturbance. Correspondingly, in this paper, we resolve the following three problems in different timescales: (1) allocate the steady part of power imbalance economically; (2) mitigate the effect of unknown low-frequency power variation locally; (3) attenuate unknown high-frequency disturbances. To this end, a distributed controller combining consensus method with adaptive internal model control is proposed. We first prove that the closed-loop system is asymptotically stable and converges to the optimal solution of an optimization problem if the external disturbance is not included. We then prove that the power variation can be mitigated accurately. Furthermore, we show that the closed-loop system is robust against both parameter uncertainty and external disturbances. The New England system is used to verify the efficacy of our design.

Robust High Bandwidth Current Regulation for Permanent Magnet Synchronous Linear Motor Drivers by Using Two-Degree-of-Freedom Controller and Thrust Ripple Observer

This paper proposes a high-performance current regulation scheme to enhance the current bandwidth and robustness of the permanent magnet synchronous linear motor (PMSLM). First, the disturbance caused by parameter variations and thrust ripple is considered in the modeling of PMSLM. Based on the model, a two-degree-of-freedom (2-DOF) controller consisting of improved predictive current control (PCC) and adaptive internal model control is derived. The improved PCC is used to overcome time delay and increase current control bandwidth, whilst the adaptive internal model is utilized to estimate the disturbance caused by parameter variations to compensate PCC. Second, a new discrete-time Jacobian linearization observer is designed to estimate the thrust ripple. The actual thrust ripple can be suppressed by injecting the estimated value into the control system. Subsequently, a comparatively smoother output thrust and higher precision position tracking is obtained. Finally, the precise test platform based on semiphysical system AD5435 is established. The experimental results verify the validity of the proposed approach.

Design of Adaptive Internal Control Model on Binary Distillation Column

The binary distillation column is one of the most important operating units in chemical engineering. Wood & Berry binary distillation columns separate feed streams (F, XF) into top products (D, XD) and bottom products (B, XB). The control design in the binary distillation column has several constraints caused by non-linearity of the process, multivariable interactions, and presence of a disturbance. Based on that constraints, it needs a modern control method that can make the system response to track the set point change and reduce or eliminate the disturbances, so the product composition output as desired. Adaptive Internal Model Control 2 Degree of Freedom (AIMC 2 DoF) method is one of the modern control methods that are capable of controlling multivariable plants and reduce or eliminate existing disturbances. Based on all the tests performed, the AIMC 2 DoF controller using Skogestad filter parameter tuning method has the smallest IAE value compared to the other methods.

Adaptive multi-loop IMC-based PID controller tuning using bat optimisation algorithm for two interacting conical tank process

In this paper, multi-loop adaptive internal model controller (IMC)-based PID is designed for the two interacting conical tank level process (TICTLP). The nonlinear TICTLP is decomposed into linear transfer function matrix around the operating points and the effective open loop transfer function (EOTF) is developed using simplified decoupler. The IMC-based PID controller parameters are obtained for EOTF model using bat optimisation algorithm (BOA). A weighted sum of integral time absolute error is used as control design objective function for multi-loop IMC-PID design which yields faster settling time with minimum over shoot. The fuzzy-based adaptive gain scheduling is used to provide complete control to TICTLP and fuzzy-based adaptive decoupler is implemented to eliminate the dynamic interaction between control loops. The simulation results of proposed controller are compared with conventional ZN-PID, IMC controllers to show the superiority of proposed controller. The simulation response of proposed controller indicates the performance improvement control schemes interns of time domain performance indices, servo tracking, regulatory response and faster settling time.

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.

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.

Cyclic RGF regulation using adaptive IMC approach and statistical feedback criterion

Abstract Residual gas fraction (RGF) significantly affects fuel efficiency and emission of combustion engines, and usually be regulated by managing variable valve timing (VVT) system. Cyclic RGF regulation is challenging due to nonlinear parameter-varying dynamics as well as stochastic property of controlled objects. This paper addresses this issue by designing an adaptive internal model controller (AIMC) combined with statistical feedback criterion. Firstly, closed-loop RGF regulation problem based on VVT control is formulated. Secondly, AIMC is adopted to online identify plant dynamics and its inverse model. Note that the statistical criterion is creatively introduced in the feedback channel of AIMC to handle the stochastic property of RGF. Finally, experiments performed on an engine test bench show that the proposed controller not only possesses advantages of AIMC but also alleviates stochastic noise in feedback channel.