Model Reference Adaptive Algorithm Research Articles

An IPMSM Control Structure Based on a Model Reference Adaptive Algorithm

Traditional construction machinery has the disadvantages of low energy efficiency and poor emissions, which do not meet the requirements of environmentally friendly industrial development. Electric construction machinery has attracted more and more attention because of its advantages of zero emissions and high energy efficiency, which are considered to be important factors in the future development of construction machinery. Preliminary attempts to introduce electric motors into construction machinery usually only adopt the motor for simulating the working mode of the engine, with it providing power for the system. Because the output power of the motor needs to be matched with the actual load through the transmission of the hydraulic torque converter, it is difficult to maximize the advantages of high energy efficiency for the electric drive. This paper studied the direct drive technology within electric construction machinery and presents a model reference adaptive algorithm (MRAA) based on maximum torque per ampere (MTPA)-vector control of an internal permanent magnet synchronous motor (IPMSM). The reference motor model was established, and the real-time dynamic reference value of the motor was obtained based on a model with the motor voltage and current as inputs. Simulations based on MATLAB/Simulink verified the feasibility of this control method. The results indicate that the MRAA can identify the motor flux linkage value and the d-q axis inductance within 50 ms in real time, with the error controlled within 2%. Additionally, when the motor operates at low speed, compared with the traditional MTPA algorithm under fixed-parameter control, the starting torque ripple of the IPMSM control method based on reference model adaptation was reduced to 23.8%, which proves that the MRAA can achieve good low-speed response characteristics and stability.

Active disturbance rejection control based on inertia estimation and variable gain for servomechanism of industrial robot

This paper proposes an adaptive PID controller based on linear extended state observer (LESO) for the two-degree-of-freedom joint servomechanism of industrial robot with time-varying load, uncertainties of parameters and disturbance. The third-order extended state space equations of the system approximate model is established to obtain LESO which is applied to estimate the state values and the total disturbance. The model reference adaptive algorithm is used to estimate the variable moment of inertia to design the controller parameter for control law which is designed with disturbance compensation. By appropriately selecting the adaptive gain coefficient of model reference adaptive algorithm and the bandwidth of LESO, the influences of parameter uncertainty, unknown dynamics and disturbances are effectively attenuated. Simulation and experimental results show that the proposed method achieves both satisfactory disturbance rejection and tracking performances of the two-degree-of-freedom joint servomechanism.

Model Predictive Control and Position Sensorless Control Algorithm for Induction Motor

In view of the traditional finite set model predictive control of induction motor, the enumeration method is used to select only one optimal voltage vector in one control cycle, which causes the deviation of stator current in the tracking control process, and a duty cycle model predictive current control algorithm is proposed. In this algorithm, duty cycle control is introduced on the basis of traditional model prediction algorithm. In a control period, an optimal non-zero vector and a zero vector act together, and the action time of the two is calculated. Based on the duty cycle model predictive control algorithm, the model reference adaptive speed estimation algorithm is introduced to improve the robustness of the model. Finally, the dynamic and static characteristics of vector control and two model predictive control algorithms are analyzed through simulation comparison, and the feasibility of the proposed speed estimation algorithm is verified on the basis of the duty ratio model predictive algorithm, and the robustness of the two estimation methods is compared. The superiority of the prediction algorithm of duty cycle model and the improved speed estimation algorithm is proved.

Distributed synchronisation control of unknown nonlinear systems with an active leader

This paper investigates the optimal synchronisation problem for unknown nonlinear heterogeneous multi-agent systems (MASs) with an active leader and disturbances. Due to the external input signal of the active leader, the resulted uncooperative behaviours and unpredictability increase difficulty to derive the analytical solution of the coupled Bellman equation. To this end, we develop a distributed model reference adaptive critic learning algorithm with a hierarchical and distributed synchronisation control framework, which transforms the synchronisation problem of unknown nonlinear MASs into that of defined reference ones. Moreover, the algorithm extends our previous work to the distributed MASs, so that it is guaranteed that all the agents reach an agreement with the minimised transient errors and robustness against the disturbances and the neural network (NN) approximation errors. It is proved that the local neighbour tracking errors and the NN weight estimation errors are uniformly ultimately bounded via Lyapunov stability theory. A simulation example for multi-manipulator systems demonstrates the effectiveness of the algorithm.

Design of Attitude Loop Controller for Six-Rotor UAV Based on L1 Adaptive Method

As for the non-modeling dynamics of the six-rotor UAV during the modeling process, and the presence of uncertain external interferences such as crosswinds during flight, the L1 adaptive control method is used to design the attitude controller of the six-rotor UAV. The force analysis of the six-rotor UAV is performed first to establish a nonlinear dynamic model, and then the nonlinear factors and coupling terms of the model are considered as time-varying parameters and disturbances. The low-pass filter is introduced based on the traditional model reference adaptive algorithm, and the L1 adaptive attitude controller is designed to effectively suppress the high-frequency interference caused by mechanical vibrations of the aircraft. Finally, simulation results show that the algorithm has good dynamic performance and robustness while ensuring system stability.

Moment of Inertia Identification based on Particle Swarm Optimization Model Reference Adaptive for Permanent Magnet Synchronous Motor

In order to improve the dynamic performance and robustness of permanent magnet synchronous motor (PMSM) servo system, the moment of inertia identification based on model reference adaptive algorithm is studied. According to the problem that the stability and convergence response speed of the model reference adaptive identification (MRAI) algorithm can not be combined at the same time, the particle swarm optimization (PSO) algorithm is used to optimize the model reference adaptive gain coefficient to realize the fast and stable identification of the moment of inertia. The simulation results showed that the method has strong optimization ability, fast convergence speed, small oscillation amplitude and good stability.

一种基于ARM + CPLD的三相无刷直流电机模型参考自适应PI调速控制系统

本文设计并开发了一种基于ARM + CPLD的三相无刷直流电机模型参考自适PI调速控制系统：由CPLD芯片实现无刷直流电机的换相与方波驱动、快速故障保护，兼具电机转子机械转速实时测量功能；由ARM芯片实现模型参考自适应PI控制算法。ARM与CPLD之间通过SPI总线接口实现全双工通信，两块芯片联合工作实现电机转速的精确控制。通过Simulink仿真和实际试验，验证了该驱动控制系统硬件结构和控制算法的有效性。采用的模型参考自适应PI控制算法，提高了无刷直流电机闭环负反馈控制的鲁棒性，使得电机参数或者电机负载发生变化时，在不重新整定PI参数的前提下，闭环控制系统依然能够保持良好的控制性能。 A three-phase brushless DC Motor (BLDCM) speed control system is designed and developed by using ARM and CPLD: motor drive commutation, square wave drive, fast fault protection and motor speed measurement logic being realized by CPLD chip; model reference adaptive PI control algorithm is implemented by ARM chip. Full duplex communication between ARM and CPLD is conducted over SPI bus interface. Precise control of motor speed is achieved by two chips’ joint work. The effectiveness of the control system structure and control algorithm was tested through Simulink simulation and actual experiment. Model reference adaptive PI algorithm improved the robustness of the BLDCM closed loop feedback control system. Consequently, when the motor pa-rameter or the motor load changes, without adjusting PI parameters, the closed-loop control system is still able to maintain excellent control performance.

Precise angular speed control of permanent magnet DC motors in presence of high modeling uncertainties via sliding mode observer-based model reference adaptive algorithm

The major concentration of this study is on developing a control scheme with parameter- and load-insensitive features capable of precise angular speed regulation of a permanent magnet (PM) DC motor in the presence of modeling uncertainties. Towards this objective, first, an appropriate nonlinear dynamic model of friction, the modified LuGre model, is opted and incorporated into the mathematical model of a PM DC motor. Then a sliding mode observer (SMO) is designed to estimate the state variable of the friction model. Next, a model reference adaptive control system into which estimated values of the friction state and parameters are fed is designed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction. Stability of the proposed SMO-based MRAC system is discussed via the Lyapunov stability theorem, and its asymptotic stability is verified. In addition to simulation studies, the algorithm is implemented on a new variable structure test-bed which gives us the ability to simulate desired parameter variations and external disturbance changes in experiment. The main contribution of the proposed scheme is the bounded estimation of the system’s friction parameter. While similar control solutions do estimate these parameters, there is no guarantee that they will estimate the correct value of friction parameters. However, in the proposed method, by properly choosing the design parameters, if certain criteria is satisfied, the estimated friction parameters will be in the bounded vicinity of their actual values. The obtained results show the effectiveness of the proposed tracking algorithm and its robustness against load and system parameters’ variations.

Sensorless control of single voltage source converter‐based doubly fed induction generator for variable speed wind energy conversion system

This study deals with the analysis, design and control of single voltage source converter (VSC)-based grid connected doubly fed induction generator (DFIG) for wind energy conversion system (WECS) with maximum power point tracking capability. This proposed system uses stator flux-based model reference adaptive system algorithm for the sensorless rotor position estimation. In this proposed topology, grid side converter is replaced by a battery energy storage system (BESS) in the dc link. The designs of all the components such as BESS, VSC and wind turbine are explained in detail. This proposed topology is implemented using a digital signal processor. This developed WECS is tested extensively in sub-synchronous, synchronous and super-synchronous speeds regions and test results are presented in all these cases. The performance of this proposed topology is demonstrated for the dynamics in wind speed by the experimental verification. A comparative study of proposed single VSC-based WECS system is made with the conventional double VSC-based DFIG.

Model Reference Adaption Identification Control Research Based on the Asynchronous Motor Vector Control System Model

Using the easily measured stator current and voltage information of asynchronous motor to get Motor speed by the Model reference adaptive algorithm. MRAS simulation model is builded with the PSIM, thus for system control. Through simulation and experiment in the different frequency and speed condition, contrasted the given speed, actual speed, estimation speed, it shows that the method has good speed identification precision and dynamic performance.

Model Reference Adaptive Algorithm Designed for Automatic Train Braking Control

This paper investigates the automatic train braking control problem of ATC (Automatic Train Control) system under uncertain disturbances. An adaptive control algorithm is developed to ensure high precision tracking performance of the acceleration during the braking process, according to a standard reference model which has been widely used in the urban vehicles. The control parameter’s adaptive law is strictly deduced based on the Lyapunov Stability Theory. Rigorous analysis has shown that the train controlled by this method based ATO (Automatic Train Operation) system can effectively track the reference trajectory. Numerical simulation also verifies the effectiveness of this adaptive control algorithm.

RCS Control Study Based on the Model Reference Adaptive Control

For the reusable launch vehicle(RLV), in the cruise stage without power and the initial load phase, due to the aerodynamic pressure is small, and the aerodynamic surface control efficiency is relatively low, it can not achieve the attitude adjustment of the RLV. This design is based on the continuous system, and applies to the model reference adaptive control law algorithms, to achieve the design of the reaction control system(RCS). According to the control torque which is required by the RLV attitude adjustment, the RCS distribution method of mixed-integer programming is used, and the control parameters of the reaction control system is adjusted in real time,thus to achieve the adjustment of the RLV's attitude. Through the software-digital simulation analysis, this method has a good controllability and robustness.At the same time, the stability of the model reference adaptive RCS is analyzed theoretically, and getting a certain amount of stability region.

Design and Validation of a Novel Model Reference Adaptive Algorithm to Control ETB for Drive-by-wire Applications

Design and Validation of a Novel Model Reference Adaptive Algorithm to Control ETB for Drive-by-wire Applications

Integrated design of speed-sensorless and adaptive speed controller for a brushless DC motor

The study develops a design of an integrated new speed-sensorless approach that involves a torque observer and an adaptive speed controller for a brushless dc motor (BLDCM). The system is based on the vector control drive strategy. The speed-sensorless approach first employs a load observer to estimate the disturbed load torque, and then the estimated load torque is substituted into the mechanical dynamic equation to determine the rotor speed, and thus develop a speed-sensorless algorithm. Additionally, the mechanical rotor inertia constant and the friction coefficient, which are the inputs of the load observer, are estimated using the recursive least-square rule. Therefore, the proposed speed-sensorless approach is unaffected by the time-variant motor parameters nor is affected by the integrator drift problem. It also has a simpler computing algorithm than the extended Kalman filter for estimating the speed. The modified model reference adaptive system algorithm, an adaptive control algorithm, is adopted as a speed controller of the BLDCM to improve the performance of the speed-sensorless approach. Simulation and experimental results confirm that the performance of the design of a new integrated speed-sensorless approach and the adaptive speed controller is good.

Activated-carbon electric-double-layer capacitors: electrochemical characterization and adaptive algorithm implementation

An activated-carbon electric-double-layer capacitor is characterized and an adaptive algorithm is derived and implemented that is commensurate with potential traction applications of these energy storage devices. The electrochemical characterization relies on a simplified equivalent circuit interpretation extracted from a more-complete mathematical representation of the capacitor system. In addition to the high power capability and potentially low costs of this class of capacitors, we clarify the substantial invariance of the device performance with respect to temperature, a distinct advantage over battery systems. The robustness of the equivalent circuit in terms of capturing the salient features of the experimental data over the temperature and voltage range of interest enables the formulation of a model-reference adaptive algorithm. The algorithm developed and implemented in this work is fully recursive in that the only variables required for on-line regression are those of the previous time step and the current time step. Successful comparisons of the algorithm's adapted state of charge and power predictions provide an initial validation of the algorithm.

AN ADAPTIVE MODEL REFERENCE CONTROLLER WITH BOUNDED GAINS

It has been established that currently available adaptive control algorithms may become unstable in the presence of high frequency unmodeled dynamics and additive sinusoidal disturbances. In such nonlinear dynamic adaptive control system ,there exist two infinite-gain operators which can cause the loop gains to increase without bound. Such unbounded parameters would likely cause instability. These problems can be alleviated by using a proposed model reference adaptive algorithm with bounded gains. In this paper, the mechanism of disturbance instabilities is investigated. The modified algorithm uses a limiter for the controller gains to prevent the unbounded drift. The limiting values of the proposed limiter, can be estimated analytically by applying stability criteria for linear control systems,to the linearized model of the controlled plant. The proposed adaptive controller is tested by simulation. Simulation results show the algorithm to be capable of ensuring stability of the adaptive control system , in the presence of sinusoidal disturbances.

Model reference adaptive control of a continuous polymerization reactor under periodic operation

AbstractThis work proposes a cascade model reference adaptive algorithm to control a continuous polymerization reactor, which is operated under forced oscillations of its feeds. The objective is to produce a polymer with desired average values of the average chain length, of the average polydispersity and of the average polymer production; in spite of the fact that some of the model parameters may be unknown and/or may vary slowly with time. This objective is achieved by imposing the first two moments of the number chain length distribution (NCLD) to follow predetermined periodic trajectories. The proposed control scheme is an extension of that by Eliçabe, et al., when the number of independent controls is less than the number of system states. Computer simulations have shown the feasibility of the proposed technique, provided continuous state measurements are available.

Adaptive Control of a Polymerization Reactor

This work proposes a cascade model reference adaptive algorithm developed to control a continuous polymerization reactor, which is operated under forced oscillations of the feeds. The objective is to produce a polymer with desired average values of the average chain length, of the polydispersity and of the polymer production, in spite that some of the model parameters may be unknown and/or may vary slowly with time. The cascade scheme is introduced because the number of independent controls (2) is less than the number of system states (5), and because the trajectories of only two of the states must be exactly followed. In this sense, the work is an extension of a previous publication by Eliçabe and co-workers (1986, Chem. Eng. Sci., Vol. 41). Computer simulations have shown the feasibility of the proposed technique, provided continuous measurements of the states are available.

Parameter information content of measurable signals in direct adaptive control

It is postulated that available parameter information is a functional of all past history of measurable signals. From this point of view, direct model reference adaptive controllers become functional differential equations. A modified model reference adaptive control structure and functional adaptation algorithm are presented. It is shown that the system is well-defined, stable, and achieves asymptotic model transfer function matching under weaker excitation conditions than those required by earlier model reference adaptive systems. It is claimed that the information content perspective developed in this paper has implications in robust adaptive control theory.

Stability and control of a class of compartmental systems with application to cell proliferation and cancer therapy

This paper concerns a systematic application of a variety of concepts and tools of control theory to the study of the stability and control of a class of compartmental systems arising in several application areas where population kinetics are of significant importance. To provide a focus to the development, the specific area of cell proliferation kinetics is selected for detailed study. A multicompartmental model which portrays the progression of a population of cells through the different phases of the ceil cycle is developed and a model reference adaptive algorithm for estimating model parameters is presented. An exponential stability framework is developed for quantifying the control effects of an administered drug protocol and is used to design efficient treatment strategies for cancer chemotherapy by application of optimization concepts. Since the toxic effects of anticancer drugs on the normal cell populations of the body are the bottlenecks in successful chemotherapy, determination of dosing strategies that minimize toxicity while maximizing the therapeutic effects are of great importance. A mathematical formulation of this problem leads to the optimization of bilinear systems under exponential stability constraints for which a solution procedure is presented.