AN ADAPTIVE INPUT-OUTPUT FEEDBACK LINEARIZATION CONTROL METHOD AND SYSTEM FOR BRUSHLESS DC MOTOR DRIVES

Abstract

The invention relates to an adaptive system and method for controlling brushless direct current (BLDC) motors (20). The invention estimates the motor's stator resistance and rotor magnetic flux online based on the error in the motor currents. These estimated values ​​are applied to an input-output feedback linearization (IOFL) control law that compensates for the motor's nonlinear dynamics. Speed ​​control is provided by a single PI controller (31) whose gain parameters are determined by Particle Swarm Optimization (PSO). This configuration reduces torque ripple, increases efficiency, and offers superior control performance, especially in precision applications such as unmanned aerial vehicles.

Description

1 TARIFF AN ADAPTIVE SYSTEM FOR BRUSHLESS DC MOTOR DRIVERS. INPUT-OUTPUT FEEDBACK LINEARIZATION CONTROL METHOD AND SYSTEM 5 TECHNICAL FIELD The invention describes the speed and torque control of brushless direct current (BLDC) motors, in particular. in applications requiring precise and efficient operation, such as unmanned aerial vehicles (UAVs) a control aimed at improving the performance of the motor drive systems used 10 It is related to the method and the system that applies this method. STATE OF THE ART Unmanned aerial vehicles (UAVs) or drones now directly or indirectly mark human presence. It plays a significant role in every field that has an impact. Electrically powered UAVs, increasingly due to their versatile uses in industrial, military and civilian applications It is attracting more attention. In the past, aircraft were primarily used for military targets. It was used for civilian purposes. However, significant technological advancements have ended its civilian applications. It has expanded its scope. These include traffic surveillance, solar panel inspection, and infrastructure monitoring. Tasks include those such as: goods delivery, precision agriculture, medical equipment transportation, vital roles such as providing wireless coverage in remote areas and environmental monitoring They undertake this. Many academic and industrial communities have a lot of work to do to develop this technology. Time and effort have been invested, and technology has been used to control various challenging situations. Many studies are ongoing to improve its excellence and efficiency. Flight The duration is quite limited due to the limitations imposed by the equipment, weight, and space requirements of the UAVs. This is a critical design problem closely related to power sources. An efficient energy source. The use of 30 UAVs will enable them to effectively complete their upcoming missions. This is very important. This topic is generally discussed in the literature at the flight control level, for example. When considering trajectory optimization, energy loss minimization should be taken into account. are being evaluated. In addition, researchers are investigating alternative power sources and They have also examined hybridization, purpose-oriented design, and energy harvesting technologies. 2 However, propeller drivers achieve maneuverability by adjusting the propeller speed / thrust. This has a significant impact on both flight time and tracking performance. It has a BLDC motor driver, a main microprocessor, a three-phase inverter, and a power supply. It consists of its sources. A standard BLDC control or six-step control is simple. Due to position / velocity estimation techniques, 5 is commonly used in UAV actuator systems. It has been adopted. However, the six-step control method requires the motor to have back-EMF (electromotor). (force) causes high torque fluctuations when the waveform is close to sinusoidal. This occurs and leads to inefficient operation. These torque fluctuations affect UAVs. This can negatively affect flight stability. Traditional field-oriented control (FOC) methods result in less torque ripple. Although these systems enable more efficient work by offering a combination of features, they are often cascaded with each other. It includes three connected PI (Proportional-Integral) controllers. The gain of these controllers Adjusting (tuning) the parameters of the system is a complex and time-consuming process. 15 parameters (such as the change in motor winding resistance with temperature) It is sensitive to changes. This situation reduces the controller's performance, affecting the UAV's capabilities. This limits dynamic maneuverability. Furthermore, copper losses occur in both methods. Given the limited battery capacity of UAVs, shortening flight time is crucial. It is an important factor. In conclusion, the BLDC motor improves flight time and overall performance. A robust speed controller design is required for the driver. A BRIEF DESCRIPTION OF THE INVENTION The present invention eliminates the aforementioned disadvantages and the related technical Speed ​​and torque control of a BLDC motor to bring new advantages to the field. an adaptive input-output feedback linearization (AIOFL) control that provides It is related to the method and system. The main purpose of the invention is to significantly reduce torque and speed fluctuations in BLDC motor drives. The goal is to achieve more stable and precise motor control by reducing its size. 3 Another aim of the invention is to minimize copper losses in motor windings. to increase the overall efficiency of the system and thus battery-powered aircraft such as UAVs The goal is to extend the operating time of the systems. Another objective of the invention is to replace the multi-PI controller structure in traditional FOC methods with a 5 reducing the controller design and configuration process to a single PI controller It is about simplifying. Another objective of the invention is to analyze the operating characteristics of the motor, such as stator resistance and rotor magnetic flux. a 10 that estimates its critical parameters online, which may change during the process. By offering an adaptive mechanism, the system can adapt to different operating conditions and parameters. The goal is to ensure it remains robust against changes. Another objective of the invention is to optimize controller gains using Particle Swarm technology. By performing the optimization using an evolutionary computational technique such as PSO, the system's 15 The goal is to maximize both dynamic and steady-state performance. The present invention aims to eliminate the aforementioned disadvantages and the related technical To bring new advantages to the field; the speed and performance of a brushless direct current (BLDC) motor. Adaptive input-output feedback linearization providing torque control 20 (AIOFL) relates to the control method and system. The technical basis of the invention is the complex operation of the motor. and its nonlinear dynamics, a mathematical cancellation of these dynamics an input-output feedback linearization (IOFL) that compensates by generating a control voltage. It is based on this architecture. Thanks to this, the motor, which is difficult to control, is simple and linear. It acts like a system, which significantly increases the precision of torque and speed control. 25 and reduces torque fluctuations. The fundamental innovation of this architecture is its 'adaptive' nature. The system measures the stator resistance, which changes during motor operation due to factors such as temperature, and rotor magnetic flux, the error between reference currents and measured motor currents It makes predictions online and in real-time based on current trends. Thanks to this continuous adaptation, The controller maintains its performance against parameter changes, providing a robust 30. The study exhibits complexity in traditional domain-oriented control methods. Unlike PI controller architecture, in our invention speed control is achieved using a single PI (Proportional-Integral) controller. This is provided by a speed controller. This controller's proportional and integral gain parameters, dynamic and steady-state responses of the system, instead of trial and error. 4 Particle that optimizes (e.g., integral of squared error - ISE and maximum overshoot) This is determined systematically using an algorithm such as Swarm Optimization (PSO). This integrated algorithm... Thanks to this approach, the invention is more efficient, more stable, and adapts to changing working conditions. by providing more resistant motor control, especially in unmanned aerial vehicles. It aims to improve the performance and uptime of the systems. 5 The best way to utilize the advantages of the existing invention, together with its structure and additional elements. For understanding, it should be evaluated together with the figures explained below. is necessary. BRIEF DESCRIPTION OF THE FIGURES Figure 1 is a general block diagram of the system that is the subject of the invention. Figure 2 shows a schematic representation of the field-oriented AIOFL controller, which is the subject of this invention. Figure 3 shows block 15 of the six-step control method representing the known state of the art. It is a diagram. Figure 4 shows the block diagram illustrating the process of adjusting PI controller gains using the PSO technique. It is a diagram. REFERENCE NUMBERS 20 Flight Controller Brushless DC Motor Main Control Unit 31 PI Speed ​​Controller 25 32 IOFL Control Block 33 Stator Resistance Estimation Block 34 Rotor Flux Estimation Block 35 Optimization Algorithm Blocks DETAILED DESCRIPTION OF THE INVENTION This detailed explanation aims to provide a better understanding of the innovation in the invention. This is explained with examples that will not create any limiting effect. The invention is a flight controller (10) whose general system architecture is shown in Figure 1. According to the provided speed commands, a brushless DC motor (20) can be driven at high speed. It is a control system and method aimed at maintaining performance. The basis of the system is... The invention consists of the main control unit (30), the details of which are given in Figure 2. 5 Unlike traditional methods, the invention offers less torque fluctuation and higher performance. greater robustness against changes in efficiency and system parameters It offers. The basis of the control strategy is the dynamics of the brushless DC motor (20) of the rotor 10 The goal is to model and control within a rotating reference frame (dq axes). The approach enables control by converting AC motor currents into DC quantities. It simplifies the state-space current dynamics of the motor in the dq frame. It is expressed by the following equation: Here, 𝑖, [𝑖, 𝑖] is the current vector; 𝑟 is the stator resistance; 𝐿 is the stator inductance; 𝑤 the electrical angular velocity of the motor; 𝑏 rotor magnetic field resulting from permanent magnets It represents the flux and the voltage vector [V, iV] that will be applied by the controller. is doing. At the heart of the invention is the nonlinearity of the brushless DC motor (20) and 20 an input / output feedback designed to compensate for their interconnected dynamics There is a linearization control block (32). However, in practice, the stator of the motor The resistance (r) can increase with temperature, and the rotor magnetic flux (b) increases with temperature or high This can be reduced due to demagnetization currents. These parameter variations are resistant to change. Maintaining the controller's performance is one of the invention's primary objectives. This objective is 25 Accordingly, the invention consists of a stator resistance estimation block (33) and a rotor flux estimation block. (34) offers an adaptive structure containing blocks. These blocks are the actual engine It continuously estimates the parameters. These estimated values ​​are used to control the IOFL. The controller's adaptation is ensured by instantly incorporating it into the law. This The adaptive IOFL control law eliminates the nonlinear terms in Equation 1. To remove it, it is derived as follows: 6 In this equation, 𝑖 is the reference current vector; 𝑒 is the ratio of the reference current to the actual current. the error between them (𝑒 = 𝑖 − 𝑖 ) and 𝐾 determines the rate at which the current error converges to zero. It represents a positive adjustment parameter. Updating the stator resistance ?̂? and rotor magnetic flux b estimates will improve the system. adaptation derived based on Lyapunov theory which guarantees its stability This is achieved through laws. These laws use current error (e) to make predictions. It is constantly being updated and is expressed as follows: Here, c and c are positive gains that adjust the adaptation rate. This The approach is one where the time derivative is negative-defined (𝑉 = −𝑒 𝐾 ). The Lyapunov function ensures the globally stable operation of the system. theoretically guarantees it. 15 The system's overall speed control loop is a conventional FOC. Instead of the complex, cascaded three controllers in the methods, Figure As seen in 2, with only one PI speed controller (31) This controller is provided with reference 20 from the flight controller (10). between the speed and the actual speed of the brushless DC motor (20) by processing the error, the q-axis directly controls the engine torque. It produces the reference current (i). Another important innovation of the invention is that this single PI controller (31) has a gain of 25 It is the management of the adjustment of its parameters ( 𝐾 𝐾 ). The principle is shown in Figure 4. optimization algorithm block (35), in particular Particle Swarm Optimization (PSO), this It is used to automate and optimize the setup process. PSO The algorithm evaluates the system's performance in terms of velocity response (e.g., ascent time, settlement time). 7 To improve duration (overrun), minimize the objective function as follows: It is: In this function, the first term represents the integral of the squared error (ISE), minimizing the error. When aiming to download, the second term (𝑎𝑀) represents unwanted peak overshoots in the speed response. It ensures more consistent behavior by punishing. This systematic optimization... a controller that cannot be reached through traditional trial and error methods It enables it to reach its performance level.

Claims

8 REQUESTS 1. It is a method for controlling brushless DC motors (20), and its feature is; a. between a reference speed and the actual speed of a brushless DC motor (20) Generating a torque reference by processing the error; 5 b. stator resistance and rotor magnetic of brushless DC motor (20) an error (e) between the motor currents measured and the reference currents of the flux online forecasting based on; c. via an input-output feedback linearization (IOFL) technique, Nonlinear dynamics of brushless DC motor (20) 10 the terms that make up the estimated stator resistance and rotor resistance cancellation using magnetic flux and measured motor currents and motor speed generating a control voltage in such a way It includes the steps of the process.

2. It is a method according to Claim 1, and its characteristic is that the step of generating the control voltage in question, control voltage vector (𝑉 ); ?̂? predicted stator resistance, 𝑏 predicted rotor magnetic flux, 𝑖 the measured current vector, and 𝐾 a positive tuning parameter to represent it with the following IOFL control equation: is to be realized: 20 3. It is a method according to claim 1, and its characteristic is that the estimation step in question is the stator. the time derivative of the resistance estimate, 𝑏̇ the time derivative of the rotor magnetic flux estimate, Let 𝑐 and 𝑐 represent the positive tuning gains using the following equations: This is achieved through the defined laws of adaptation: 25 4. It is a method according to Claim 1, and its characteristic is that in the step of generating the torque reference in question... The proportional gain parameters 𝐾 and 𝐾 used in a Particle Swarm It is determined using the Optimization (PSO) algorithm. 9 5. It is a method according to claim 4, and its characteristic is that the determination step in question is 𝑤 reference speed, 𝑤 actual speed, 𝑀 maximum overshoot, and 𝑎 a weighting. an objective function defined as follows, to represent the coefficient: (G) is achieved by minimizing:

6. It is a system for controlling brushless DC motors (20), and its feature is;  the difference between a reference speed and the actual speed of a brushless DC motor (20) a single PI speed configured to generate a torque reference by processing the error controller (31);  stator resistance and rotor magnetic flux of brushless DC motor (20), 10 based on an error (e) between the reference currents and the measured motor currents a stator resistance estimator configured to predict online block (33) and a rotor flux estimation block (34);  Nonlinear dynamics of brushless DC motor (20) the terms that make up the estimated stator resistance and rotor 15 cancellation using magnetic flux and measured motor currents and motor speed an input configured to generate a control voltage in such a way as to Output feedback linearization control block (32) It includes a main control unit (30).

7. It is a system according to claim 6, and its feature is that the said Main Control Unit (30), PI proportional ( 𝐾 ) and integral ( 𝐾 ) gain parameters of the speed controller (31) It includes a block of optimization algorithms structured to determine (35).