Desired Reference Model Research Articles

An IMC decentralized PID controller retuning method based on frequency domain data

In this work, a method is proposed for retuning decentralized proportional-integral-derivative controllers. It consists in applying the internal model control approach in order to ft the closed-loop frequency response to that of a desired reference model. Then, an optimization problem is solved to adjust the increments on the controller parameters in order to meet the closed-loop requirements. To guarantee stability and robustness, linear margin constraints are applied to each loop, using the concept of effective loop transfer functions to take the coupling into account. The procedure is validated through simulations studies, by comparing it with other decentralized and centralized tuning methods.

A new on-line self-adapting fuzzy controller design using unidimensional input-output with dynamical membership functions

Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems.

Direct Model Reference Adaptive Control of a Boost Converter for Voltage Regulation in Microgrids

In this study, we present a Direct Model Reference Adaptive Control (DMRAC) algorithm in a boost converter used in islanded microgrids (MG) with a solar photovoltaic (PV) system. Islanded types of microgrids have very sensitive voltage and frequency variability; therefore, a robust and adaptive controller is always desired to control such variations within the MG. A DC–DC boost converter with a modified MIT rule controller is proposed in this paper, which stabilizes output voltage variations in islanded MG. Since the boost converter is a non-minimum phase, the controller design that relies only on output voltage feedback becomes challenging. Even though output voltage control can be achieved using inductor current control, such current mode controllers may also require prior knowledge of the load resistance and more states, such as output and inductor currents in feedback. Here, two control loops are used to achieve a stable output voltage; a PID controller can regulate the output voltage at a fixed level, and the outer loop is designed to implement the MIT rule for a DMRAC. To ensure that the actual system is following the desired reference model, using only an output voltage feedback sensor, a DMRAC is devised to update the PID controller parameters in real-time. Compared to a DC–DC boost converter connected to the MG, a controller, such as the one introduced in this paper, is more successful in dealing with unknown parameter fluctuations and disturbance changes. The MATLAB/SIMULINK is used to design and simulate the controller with different load disturbances and input voltage variances. The hardware validation is also carried out to show the performance of the proposed controller. Our results suggest that the DMRAC provides robust regulation against parameter variations.

Adaptive control of oxygen excess ratio in a proton exchange membrane fuel cell system

Proton exchange membrane fuel cell (PEMFC) has drawn extensive attention as alternative energy with high power generation efficiency and low pollution. The dynamic performances of the PEMFC air system in a city bus, including oxygen excess ratio (OER) tracking and compressor movement, are essential for its stability and economy. In this paper, the study is concentrated on the control of OER under current disturbance and parameter uncertainty. To this end, a Lyapunov-based model reference adaptive controller (Lyapunov-MRAC) with PD controller is employed. Lyapunov-MRAC adjusts the controller in time to ensure the output of the plant following the desired reference model, and the PD controller further improves the dynamic performance of the controller. The proposed strategy, traditional Lyapunov-MRAC, and proportional–integral (PI) controller are implemented on a widely accepted nonlinear model describing the dynamics of the air supply system. The simulation results indicate that the proposed strategy outperforms traditional methods in OER tracking and overshoot restriction under current disturbance, and improves the robustness under the perturbation of compressor rotational inertia. In addition, the proposed strategy can reduce the control effort of the compressor so as to prolong the lifetime of the compressor.

Robust adaptive reference model control of nonlinear wind-induced oscillations of floating offshore wind turbine blade in finite-time

In this research, to overcome the challenging problem of the vibration suppression in the wind turbine blade, an active robust adaptive finite-time model reference controller is introduced. First, edgewise vibrations of the wind turbine blade, with consideration of the platform heave displacements, is modeled by Euler-Bernoulli beam theory. Then, the appropriate adaptation laws and control laws are introduced to compensate the uncertainties and guarantee the convergence to the desired reference model. The robustness and convergence of the designed adaptation and control laws are mathematically proved via finite-time Lyapunov theory. Performance of the designed robust adaptive finite-time model reference controller is evaluated by its implementation on NREL 5-MW wind turbine. Finally, the advantages of the proposed controller are demonstrated over the classic control methods by comparison the performance of the proposed controller with the performance of the linear quadratic controller.

Design of a Data‐Driven Control System Simultaneously Estimating Disturbance Signal Based on Ms Robustness

The control system design scheme for improving disturbance response have been proposed in recent years. In general, system identification is required to estimate disturbance, and the control performance depends on an identification accuracy. In contrast, the data‐driven control scheme has been proposed in which the control parameters are directly calculated from operational data without system identification. This paper proposes the data‐driven control scheme that simultaneously designs a controller and a disturbance estimator. In the proposed scheme, the control parameters are calculated by using the least‐squares method. Specifically, the disturbance is suppressed only using the desired reference model, controller and the adjustable parameter . Finally, the effectiveness of the proposed scheme is numerically demonstrated. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Dynamic model estimating and designing controller for the 2-DoF planar robot in interaction with cable-driven robot based on adaptive neural network

This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively.

A combined model reference adaptive control law for multirotor UAVs

Model reference adaptive control (MRAC) offers the potential to adapt in real-time to changes in the performance of small unmanned air vehicles. There are significant challenges with their use, however, primarily in the implementation and assurance of long-term system stability. This paper presents flight test results for a combined model reference adaptive control (CMRAC) law applied to the height control loop of a multirotor. Key features include the implementation of CMRAC with a baseline controller allowing for in-flight switching between the two; the use of an augmented state to improve the tracking performance and a CMRAC implementation that provides a shorter transient phase, faster parameter convergence, and closer tracking of the desired reference model response when compared with standard MRAC. With the current exponential growth of interest in unmanned air vehicles, the potential benefits of using CMRAC for control system development are significant, particularly for new vehicles with short development and testing phases and in cases where there are significant configuration changes in flight or prior to rapid deployment.

Data-driven disturbance observer-based control: an active disturbance rejection approach

In this paper, a data-driven method for disturbance estimation and rejection is presented. The proposed approach is divided into two stages: an inner stabilization loop, to set the desired reference model, together with an outer loop for disturbance estimation and compensation. Inspired by the active disturbance rejection control framework, the exogenous and endogenous disturbances are lumped into a total disturbance signal. This signal is estimated using an on-line algorithm based on a data-driven predictor scheme, whose parameters are chosen to satisfy high robustness-performance criteria. The above process is presented as a novel enhancement to design a disturbance observer, which constitutes the main contribution of the paper. In addition, the control strategy is completely presented in discrete time, avoiding the use of discretization methods for its digital implementation. As a case study, the voltage control of a DC-DC synchronous buck converter affected by disturbances in the input voltage and the load is considered. Finally, experimental results that validate the proposed strategy and some comparisons with the classical disturbance observer-based control are presented.

Robust model reference adaptive PI controller based sliding mode control for three-phase grid connected photovoltaic inverter

In this paper, the design of a new robust model reference adaptive PI (MRAC-PI) current controller is proposed for a two-stage grid connected photovoltaic (PV) inverter. Perturb & observe (P&O) algorithm is implemented in the boost part in order to extract the maximum power from the PV array. Firstly, the current dynamics with considering the system uncertainties are written in dq frame and are simplified by employing a decoupling system. Then, the MRAC-PI controller is designed based on a sliding mode control (SMC) to improve the robustness of the controller under system uncertainties. The parameters of PI controller are tuned based on SMC laws to track the desired reference model and provides fast and desirable dynamic response. The stability of the robust MRAC-PI is proven by using Lyapunov's theory and Barbalet's lemma. Finally, the performance of the proposed current controller and the conventional PI based voltage oriented control (VOC) during variations in filter inductance and solar irradiance are compared through simulation results. These results show the effectiveness of the proposed controller.

A closed-loop frequency domain PI retuning technique for multivariable systems

Abstract A Proportional-Integral (PI) controller frequency domain retuning technique for square multivariable process is presented in this paper. Assuming knowledge of the current PI controller, the controller gains increments are computed in such that the new closed-loop matches with a desired reference model. This is performed using only the closed-loop response at certain frequencies, without knowledge of the process model. Effectiveness of the proposed method is shown in the simulation results. The maximum singular value is used to show the robustness. Finally, an experimental result is presented.

Neural Network based Direct MRAC Tech-nique for Improving Tracking Performance for Nonlinear Pendulum System

This paper investigates the application of a neural network-based model reference adaptive intelligent controller for controlling the nonlinear systems. The idea is to control the plant by minimizing the tracking error between the desired reference model and the nonlinear system using conventional model reference adaptive con-troller by estimating the adaptation law using a multilayer backpropagation neural network. In the conventional model reference adaptive controller block, the controller is designed to realize the plant output converges to reference model output based on the plant, which is linear. This controller is effective for controlling the linear plant with unknown parameters. However, controlling of a nonlinear system using MRAC in real-time is difficult. The Neural Network is used to compensate the nonlinearity and disturbance of the nonlinear pendulum that is not taken into consideration in the conventional MRAC therefore, the proposed paper can significantly improve the system behaviour and force the system to behave the reference model and reduce the error between the model and the plant output. Adaptive law using Lyapunov stability criteria for updating the controller parameters online has been formulated. The behaviour of the proposed control scheme is verified by developing the simulation results for a simple pendulum. It is shown that the proposed neural network-based Direct MRAC has small rising time, steady-state error and settling time for a different disturbance than Conventional Direct MRAC adaptive control.

A unified approach of PID controller design for unstable processes with time delay

This paper addresses a unified approach of the PID controller design for low as well as high order unstable processes with time delay. The design method is based on the direct synthesis (DS) approach to achieve the enhanced load disturbance rejection. To improve the servo response, a two-degree of freedom control scheme has been considered. A suitable guideline has been provided to select the desired reference model in the DS scheme. The direct synthesis controller has been approximated to the PID controller using the frequency response matching method. A consistently better performance has been obtained in comparison with the recently reported methods.

A PID and PIDA Controller Design for an AVR System using Frequency Response Matching

A proportional integral derivative (PID) and proportional integral derivative acceleration (PIDA) controller have been designed for voltage regulation in power system. The controller (i.e. PID and PIDA) has been proposed via frequency response matching of desired reference model with that of system model transfer function. The proposed PID controller has been designed using one point frequency response matching as well as pole placement technique, while PIDA controller has been designed using two point frequency response matching by equating desired set-point closed loop reference model with that of closed loop transfer function of system model. The response of the proposed PIDA controller shows improved performance for automatic voltage regulator (AVR) system in comparison with recently available literature. The proposed PID and PIDA controllers provide fast and smooth response for an AVR system. The advantages associated with the PIDA controller for an AVR system is to reduce rise time, percentage overshoot and improved robustness, stability margin.

Voltage and current control augmentation of islanded microgrid using multifunction model reference modified adaptive PID controller

Future grids will consist of many small and large microgrids that will require complex control of grid interaction as well as islanding operation. This paper addresses the control of islanded microgrids under dynamic system conditions. In this paper, a robust model reference modified adaptive proportional integral derivative (PID) controller is designed for voltage and current control augmentation of islanded microgrid against various load dynamics. The variation of load dynamics may cause the unsafe operation of the microgrid. The objective of the designed controller is to confirm the safe operation against voltage and current control under different load dynamics. The controller is designed based on selecting the desired reference model and controlling parameters. The effectiveness of the proposed controller is evaluated against the presence of load dynamics, harmonic sources, asynchronous machines, nonlinear and unknown loads. The results show that the proposed controller is capable of providing high tracking performance and safe operation for both the single and three phase islanded microgrid system.

Alternative Approach to Use RLS Algorithm in Multivariable Online Adaptive PID Controllers for MIMO Systems

ABSTRACTThe proportional-integral-derivative (PID) is widely used in industrial control systems due to its simplicity. For multi-input multi-output (MIMO) systems, choosing the PID gain values is a difficult task especially that in most applications system parameter variations and changes in operating conditions occur. Thus, there is necessity to find parameters adjustment method in which PID gains should be adapted to handle such changes. This paper is concerned with the design of a multivariable adaptive PID (APID) controller in which recursive least square (RLS) algorithm is used as an adaptation mechanism. The RLS algorithm, on the contrary of its usual application as an identification method, is used in the proposed controller to update the PID gains online forcing the system to behave like a desired reference model. Unlike other techniques, the proposed multivariable APID controller has the advantage that it does not impose restrictions on the system structure such as being stable, square, minimum phase, nor almost restrict positive real. Since stability is a vital issue in the evaluation of control systems, therefore stability analysis of the proposed approach is developed using Lyapunov stability theory. Comparative simulation results demonstrate the superiority of the proposed multivariable APID controller over another two different controllers when applied to MIMO systems especially for unstable systems when the controlled systems suffer from parameter variations or uncertainties.

Design of an Implicit Self-tuning PID Controller Based on the Generalized Output

PID controllers have been widely employed in real processes. Since PID parameters strongly affect the control performance, lots of schemes for tuning PID parameters have been proposed. Fixed PID controllers are applied mainly. However, it is impossible to obtain good control performance for time-variant systems by the fixed PID controllers. Therefore, it is important to tune PID parameters in an on-line manner. In addition, especially in process systems, it is difficult to disturb the systems to identify. Since, it is important to determine PID parameters directly from the closed-loop operating data. In this paper, in order to overcome these problems, an implicit self-tuning PID control scheme is proposed. In the proposed method, a new parameter tuning law using obtained closed-loop data is introduced, and recursive least squares method is applied to the scheme. This scheme has a reference model which can be designed by users. As a result, the system output can track the desired reference model output. The new parameter tuning law can calculate PID gains directly from closed-loop data, so the proposed method belongs to implicit schemes. Therefore, a system identification can be avoided. In addition, procedures of the scheme are very simple. Hence, the computation cost becomes low. The effectiveness of the control scheme is evaluated by an experimental example.

Realization of Prefilter for Virtual Reference Feedback Tuning Using Closed-Loop Step Response Data

[abstFig src='/00280005/13.jpg' width='300' text='VRFT model matching problem with prefilter F(z)' ] Prefilters for Virtual Reference Feedback Tuning (VRFT) in the time domain are realized by the least-squares method with step reference response data acquired from the closed-loop system to be tuned and are used to obtain controller parameters for making closed-loop systems as close as possible to the desired reference model. The usefulness of this proposal is shown in numerical examples in which fifth-order and PID controllers for a flexible transmission system are tuned by VRFT with prefilters.

Application of Clustering Methods for Online Tool Condition Monitoring and Fault Diagnosis in High-Speed Milling Processes

Tool condition monitoring (TCM) is a necessary action in a high-speed milling (HSM) process. As a worn milling tool might irreversibly damage a workpiece, there is a vital demand for a TCM system to evaluate the tool wear progress, or equivalently the resultant surface roughness, nonintrusively. To build up a condition monitoring system for HSM processes, sensor signals are to be utilized to form a reference model that reflects the performance of the system. Therefore, a desired reference model has to apply more efficient feature extraction and artificial intelligence techniques to be more repeatable and generalizable. This paper illustrates the performance of clustering techniques on high-speed end milling experimental data. Studied clustering methods are applied to the wavelet features of force and vibration signals to illustrate the repeatability of their results. It is shown that clustering methods can coarsely capture the status of the process and can be applied for fault diagnosis and TCM purposes. It is also discussed how the application of clustering methods may improve the performance of existing reference models toward the more efficient utilization of available experimental data and to develop easily generalizable reference models. Finally, a possible application of clustering results is discussed comparing with state-of-the-art papers.

Sequential fuzzy clustering based dynamic fuzzy neural network for fault diagnosis and prognosis

In recent years, increasing demands for more efficient high speed milling (HSM) processes have propelled the application and development of more effective modeling methods and intelligent machining approaches. Hence, a desired reference model has to incorporate more efficient and sophisticated feature extraction and artificial intelligence (AI) techniques aiming for more repeatability and generalizability.In our work, wavelet analysis is applied for feature extraction to provide a better insight into time–frequency changes of the process. Considering the high dimension of extracted wavelet features, dimension reduction methods, such as clustering techniques are inevitable. They are applied as an interpretation layer between the feature extraction and artificial intelligence subsystems to form a new model structure for HSM with generalizability and sequential learning capability.We introduce a new architecture that incorporates the advantages of fuzzy clustering into well known dynamic fuzzy neural networks (DFNN) to form an online condition monitoring system which is tolerant to slight drifts in process dynamics and adaptable to variations in parameters and device. Sequential Fuzzy Clustering Dynamic based Fuzzy Neural Networks (SFCDFNN) is developed and successfully applied for monitoring of an HSM process. It is able to sequentially learn the model and adapt itself to variations and also provide an estimation or prediction on the status of the process. It facilitates for nonintrusive fault diagnosis and prognosis. Lastly, its performance on modeling of experimental data is practically illustrated and compared to its other counterparts.