Adaptive control of DC motor without identification of parameters

Omar F., El Mrabet A.H., Belkraouane I., Djeriri Y.

Due to the simple structure of DC motors, the natural decoupling between torque and speed, and its low cost the DC motors have been widely used in electromechanical systems, the paper deals with the experimental method of DC motor Coulomb friction identification that caused the dead nonlinear zone and proposed a nonlinear model of the DC motor, then a sliding mode strategy is developed to control the DC motor in high and low speed for bidirectional operation, The experimental implementation using Dspace 1104 demonstrate that the proposed sliding mode control can achieve favorable tracking performance against non-linearities for a DC motor.

Shah R., Sands T.

Adaptive and learning methods are proposed and compared to control DC motors actuating control surfaces of unmanned underwater vehicles. One type of adaption method referred to as model-following is based on algebraic design, and it is analyzed in conjunction with parameter estimation methods such as recursive least squares, extended least squares, and batch least squares. Another approach referred to as deterministic artificial intelligence uses the process dynamics defined by physics to control output to track a necessarily specified autonomous trajectory (sinusoidal versions implemented here). In addition, one instantiation of deterministic artificial intelligence uses 2-norm optimal feedback learning of parameters to modify the control signal, while another instantiation is presented with proportional plus derivative adaption. Model-following and deterministic artificial intelligence are simulated, and respective performance metrics for transient response and input tracking are evaluated and compared. Deterministic artificial intelligence outperformed the model-following approach in minimal peak transient value by a percent range of approximately 2–70%, but model-following achieved at least 29% less error in input tracking than deterministic artificial intelligence. This result is surprising and not in accordance with the recently published literature, and the explanation of the difference is theorized to be efficacy with discretized implementations.

Sands T.

Many research manuscripts propose new methodologies, while others compare several state-of-the-art methods to ascertain the best method for a given application. This manuscript does both by introducing deterministic artificial intelligence (D.A.I.) to control direct current motors used by unmanned underwater vehicles (amongst other applications), and directly comparing the performance of three state-of-the-art nonlinear adaptive control techniques. D.A.I. involves the assertion of self-awareness statements and uses optimal (in a 2-norm sense) learning to compensate for the deleterious effects of error sources. This research reveals that deterministic artificial intelligence yields 4.8% lower mean and 211% lower standard deviation of tracking errors as compared to the best modeling method investigated (indirect self-tuner without process zero cancellation and minimum phase plant). The improved performance cannot be attributed to superior estimation. Coefficient estimation was merely on par with the best alternative methods; some coefficients were estimated more accurately, others less. Instead, the superior performance seems to be attributable to the modeling method. One noteworthy feature is that D.A.I. very closely followed a challenging square wave without overshoot—successfully settling at each switch of the square wave—while all of the other state-of-the-art methods were unable to do so.

Sands T.

The major premise of deterministic artificial intelligence (D.A.I.) is to assert deterministic self-awareness statements based in either the physics of the underlying problem or system identification to establish governing differential equations. The key distinction between D.A.I. and ubiquitous stochastic methods for artificial intelligence is the adoption of first principles whenever able (in every instance available). One benefit of applying artificial intelligence principles over ubiquitous methods is the ease of the approach once the re-parameterization is derived, as done here. While the method is deterministic, researchers need only understand linear regression to understand the optimality of both self-awareness and learning. The approach necessitates full (autonomous) expression of a desired trajectory. Inspired by the exponential solution of ordinary differential equations and Euler’s expression of exponential solutions in terms of sinusoidal functions, desired trajectories will be formulated using such functions. Deterministic self-awareness statements, using the autonomous expression of desired trajectories with buoyancy control neglected, are asserted to control underwater vehicles in ideal cases only, while application to real-world deleterious effects is reserved for future study due to the length of this manuscript. In totality, the proposed methodology automates control and learning merely necessitating very simple user inputs, namely desired initial and final states and desired initial and final time, while tuning is eliminated completely.

Gerov R., Jovanovic Z.

The paper proposes a new method of identifying the linear model of a DC motor. The parameter estimation is based on the closed-loop step response of the DC motor under a proportional controller. For the application of the method, a deliberate delay of the measured speed was introduced. The paper considers the speed regulation of the direct current motor with negligible inductance by applying 1-DOF and 2-DOF, proportional integral retarded controllers. The proportional and integral gain of the PI retarded controllers was received by using a pole placement method on the identified model. The Lambert W function was applied for the identification and in designing the controller with the purpose of finding the rightmost poles of the closed-loop as well as the boundary conditions for selecting the gain of the PI controller. The robustness of the calculated controllers was considered under the effect of an disturbance, uncertainty in each of the DC motor parameters as well as perturbations in time delay.

Mallick S., Mondal U.

The objective of this work is to apply Model Reference Adaptive Control (MRAC) using Massachusetts Institute of Technology (MIT) rule and MRAC using Lyapunov method to control the speed of a Direct Current (DC) motor system. The speed control of DC motor is one of the widely used industrial controls due its specific characteristics. Different values of adaptation gains are taken for comparative analysis in MATLAB Simulink environment. A detail comparative performance analysis has been stated with different MRAC strategies applied to the DC motor system.

Aliev F.A., Hajieva N.S., Namazov A.A., Safarova N.A.

An identification problem is considered. It allows determining the parameters of a dynamic system in the discrete case. First, the nonlinear discrete equation is linearized by the method of quasi-linearization. Then, the quadratic functional and its gradient are derived using statistical data. A calculation algorithm is proposed to solve the problem. It is shown by way of an example that the statistical value of the coefficient of hydraulic resistance differs from the calculated value by 10–4. This is indicative of the adequacy of the mathematical model.

Bozic M., Antic S., Vujicic V., Bjekic M., Djordjevic G.

This paper describes the implementation of electronic gearing of two DC motor shafts. DC motors are drives for a mobile robot with wheels in the form of wheel - leg (Wheg) configuration. A single wheel consists of two Whegs (dWheg). The first DC motor drives one Wheg, while the second one drives another independent Wheg. One motor serves as the master drive motor, while the other represents the slave drive motor. As the motors are independent, it is necessary to synchronize the speed and adjust the angle between shafts. The main contribution of this paper is the implementation of control structure that enables the slave to follow the master drive, without mechanical coupling. Based on encoder measurements, the slave effectively follows the master drive for the given references of speed and angle. Speed and positioning loops are implemented on real time controller - sbRIO. The laboratory setup was created and comparison of realized and required angles and speeds was made.

Rodríguez-Molina A., Villarreal-Cervantes M.G., Aldape-Pérez M.

In this work, a comparative study of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. The adaptive control is established as the online solution of a constrained dynamic optimization problem. Several adaptive strategies based on Differential Evolution, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, Wolf Search Algorithm and Genetic Algorithm are proposed in order to online tune the parameters of the DC motor control. Simulation results show that proposed adaptive control strategies are a viable alternative to regulate the speed of the motor subject to different operation scenarios. The statistical analysis given in this work shows the features and the differences among strategies, their feasibility to set them up experimentally and also a new hybrid strategy to efficiently solve the problem. In addition, comparative analysis with a robust control approach reveal the advantages of the adaptive strategy based on meta-heuristic techniques in the velocity regulation of the DC motor.

Tariba N., Bouknadel A., Haddou A., Ikken N., Omari H.E., Omari H.E.

The Photovoltaic Generator have a nonlinear characteristic function relating the intensity at the voltage I = f (U) and depend on the variation of solar irradiation and temperature, In addition, its point of operation depends directly on the load that it supplies. To fix this drawback, and to extract the maximum power available to the terminal of the generator, an adaptation stage is introduced between the generator and the load to couple the two elements as perfectly as possible. The adaptation stage is associated with a command called MPPT MPPT (Maximum Power Point Tracker) whose is used to force the PVG to operate at the MPP (Maximum Power Point) under variation of climatic conditions and load variation. This paper presents a comparative study between the adaptive controller for PV Systems using MIT rules and Lyapunov method to regulate the PV voltage. The Incremental Conductance (IC) algorithm is used to extract the maximum power from the PVG by calculating the voltage Vref, and the adaptive controller is used to regulate and track quickly the PV voltage. The two methods of the adaptive controller will be compared to prove their performance by using the PSIM tools and experimental test, and the mathematical model of step-up with PVG model will be presented.

Swathi M., Ramesh P.

Normal feedback controllers may not perform well, because of the variations in process or Plant due to nonlinear actuators, changes in environmental conditions. The design of a controller for speed control of DC Motor with Model Reference Adaptive Control scheme using the MIT rule for adaptive mechanism is presented in this paper. The controller gives reasonable results, but to the changes in the amplitude of reference signal it is very sensitive. It is shown from the simulation work carried out in this paper that adaptive system becomes oscillatory if the value of adaptation gain or the amplitude of reference signal is sufficiently large. This paper also deals with the use of MIT rule along with the normalized algorithm to handle the variations in the reference signal, and this adaptation law is referred as modified MIT rule. The Modeling of MRAC is shown by means of simulation on MATLAB.

Bitar Z., Sandouk A., Jabi S.A.

By using a laboratory test desk built specially to test DC Series Motor and 3-phase IM, several practical tests could be performed. This desk include the electric motor, an electronic controller and Battery Bank with a charger, enabling the practically verification of performance and behaviour of the complete electric drive system, which contains the exact parts that would be proposed to use in converting a diesel- or petrol-powered vehicle to an electric car. The resistive loads of the car were simulated using a synchronous generator with a variable electric resistive load driven by the tested motor. The tests were carried on DC Series Motor with special design and construction suitable to be used in an electric car. An electronic car foot pedal and a battery bank were used as a main source of power with a capacity suitable to a light- or medium-weight car. The Variations of DC Series Motor rotating speed and active torque over the time were studied for these cases, when various resistive loads coupled to DC Series Motor. Some conclusions and remarks on the practical performance and behaviour of IM used in the electric car were concluded.

Stankovic M., Naumovic M., Manojlovic S., Mitrovic S.

The Implementation of fuzzy model reference adaptive control of a velocity servo system is analysed in this paper. Designing the model reference adaptive control (MRAC) and the problem of choosing adaptation gain is considered. Tuning the adaptation gain by fuzzy logic subsystem and a simple synthesis procedure of fuzzy MRAC are proposed. Several simulation runs show the advantages of fuzzy MRAC approach. Experimental validation on laboratory speed servo is realized by the acquisition system. The results confirm benefits of the proposed controller in comparison with the standard MRAC.

Li Y., Tong S., Li T.

In this paper, an adaptive fuzzy output feedback approach is proposed for a single-link robotic manipulator coupled to a brushed direct current (DC) motor with a nonrigid joint. The controller is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring the measurements of link position. Using fuzzy logic systems to approximate the unknown nonlinearities, an adaptive fuzzy filter observer is designed to estimate the immeasurable states. By combining the adaptive backstepping and dynamic surface control (DSC) techniques, an adaptive fuzzy output feedback control approach is developed. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. Three key advantages of our scheme are as follows: (i) the proposed adaptive fuzzy control approach does not require that all the states of the system be measured directly, (ii) the proposed control approach can solve the control problem of robotic manipulators with unknown nonlinear uncertainties, and (iii) the problem of “explosion of complexity” existing in the conventional backstepping control methods is avoided. The detailed simulation results are provided to demonstrate the effectiveness of the proposed controller.

Zhu Q., Yuan X., Wang H.

This paper proposes an improved chaos optimization algorithm (ICOA)-based parameter identification approach of synchronous generator. The proposed ICOA is the combination of mutative-scale parallel chaos optimization algorithm and simplex search method. The parameter identification of synchronous generator is considered as an optimization process with a fitness function minimizing the errors between the estimated values and measured values, and the proposed ICOA will search the optimal parameters values of the plant. Simulation results have show the effective performance of the proposed parameter identification approach.

Gökçe C.O., İpek M.E., Dayıoğlu M., Ünal R.

Djordjevic M., Paunovic V., Dankovic D.

This paper describes a new method for measuring and characterizing electrical parameters of ceramic materials. In addition, a comparison of experimental results obtained by manual measurement and an application of a new method for characterizing doped BaTiO3 ceramics at different temperatures and frequencies was performed. An LCR meter and a programmable testing furnace were used to measure the material’s dielectric parameters as a function of temperature. When measuring the parameters using the manual method, the results were obtained by manual control of the LCR meter, that is, they were measured by manually setting the parameters. The results were compared with those obtained using a new method for automatic control of the LCR meter, for which an application was developed. This comparison shows that feature measurement can be done fully automatically. By measuring samples of Er/BaTiO3 doped ceramics, it was possible to compare the results obtained by both methods. Based on the analysis of the measured results, it can be concluded that the new method is more precise, and the results are easier to manipulate in further work. In addition, the new method enables greater reproducibility of measurements, compared to the application of the manual method, which allows one measurement to be performed during the day. The described new method provides the possibility to measure material characteristics without the presence of a human factor.

Omar F., Semmak A., Köten H., El Djalil Djehaf M.A., Nour El Islam A.A., Mallik B.B.

Mitrović N., Veljković S., Prijić Z., Danković D.