Neuron Control DC Speed Regulation System Based on LM3S8962 ARM-EEWORLD

Collect

DC motors have good starting and braking performance and can smoothly adjust speed over a wide range, so they are widely used in the field of controllable electric traction. However, the traditional DC speed regulation system adopts a complex PID analog control system composed of discrete components. Although conventional PID control has the advantages of simple structure, good stability, and easy engineering implementation, this method is overly dependent on the model parameters of the control object and has poor robustness. For complex systems such as the control of robots, due to the wide range of changes in its load model parameters and the influence of nonlinear factors, conventional PID control is difficult to achieve satisfactory results. This paper proposes a fuzzy control system based on the LM3S8962 ARM chip to replace the traditional PID analog control and improve the control performance of the DC speed regulation system.

1 Control scheme of control system

The system control block diagram is shown in Figure 1. It adopts cascade control and is divided into a speed loop (outer loop) and a current loop (inner loop). In order to improve the rapidity of system response and the necessity of current limiting, the current loop still uses the traditional PI regulator, while the speed uses a neuron controller to improve its robustness.

2 Single neuron PSD adaptive control algorithm

The control block diagram of the single neuron adaptive PSD algorithm is shown in Figure 2.

The input of the state converter in Figure 2 is the set value r(k) and the process output y(k). The function of the converter is to obtain the three input quantities x1(k), x2(k), and x3(k) of a single neuron. Here:

Where: Wi(k) (i=1, 2, 3) is the weighting coefficient corresponding to the neuron input xi(k).

The total output of the controller is:

z(k) is the teacher signal, and here z(k)=e(k). This is because the control effect is mainly related to e(k) and △e(k). In order to ensure the convergence of the learning algorithm and the robustness of the control, a normalized learning algorithm is generally used to form a single neuron PSD control law, so the control algorithm of the single neuron adaptive PSD is as follows:

[page]

3. Hardware Design of Control System

The control system is based on LM3S8962, which is a 32-bit RISC controller based on ARM?CortexTM-M3. It has internal memory, 4 general timers, a watchdog timer that complies with the ARM FiRM specification, a controller area network (CAN), a 10/100 Ethernet controller, a synchronous serial interface (SSI), 2 fully programmable UARTs, 4 10-bit ADCs, an analog comparator, I2C, 6 PWM outputs, and 2 QEI modules.

The main circuit of the system adopts a thyristor three-phase fully controlled bridge circuit. The control circuit is mainly composed of the LM3S8962 chip. First, it completes the sampling of speed pulses, the implementation of control algorithms, and the output of control pole pulses. Second, it completes the start and stop control, keyboard and display interface, etc. The system hardware block diagram is shown in Figure 3.

The PWM output signal from the LM3S8962 chip is driven by optoelectronic isolation and sent to the thyristor control electrode to achieve control of the full-controlled bridge.

The current detection circuit uses the Hall current sensor CSNP661 to detect the DC current Id. When it is detected that the current value exceeds the set limit value, ARM immediately interrupts the processing, blocks the PWM signal output to the thyristor, and sends out an audible and visual alarm signal.

The system uses a tachometer generator to measure the motor speed, converts the speed signal into a voltage signal, and sends it to the ADC conversion input interrupt of the ARM through a voltage divider resistor.

4 Control system software design

Software structure: This system software adopts the functional module design method. The software consists of the system, main program, interrupt service subroutine and other related subroutines.

The main program mainly completes the initialization of the chip, initialization of variables, etc.

The interrupt program mainly includes several parts such as ADC conversion end interrupt.

In the serial port interrupt, the main task is to complete the transmission of information with the host, and perform various actions according to the host's commands based on the established serial communication protocol.

In the ADC interrupt, the value converted by the ADC is calculated to obtain the current load current value, and the current loop is adjusted. After each certain number of current loop adjustments, the speed loop is adjusted to ensure that the system is controlled as required.

5 Simulation Experiment

In order to test the control performance of the system, simulation experiments of no-load starting and sudden load addition were carried out on the DC motor (rated data: 380 V, 37 A, 200 r/min), and the change curves of current and speed are shown in Figures 4 and 5.

6 Conclusion

The experimental results show that the system has a simple structure, reliable control, can maintain fast response, no static error and small overshoot and other excellent performance, using high-performance and high-precision ARM chip fuzzy controller, can achieve high control accuracy. At the same time, the system has strong expansion capabilities and can communicate with the host computer through the serial port or Ethernet.

Keywords：LM3S8962 Reference address：Neuron Control DC Speed Regulation System Based on LM3S8962 ARM

Previous article：Fetal ECG signal data acquisition system based on ARM
Next article：Hardware Design and Implementation of PDA System Based on ARM Chip

Popular Resources
Popular amplifiers

Neuron Control DC Speed ​​Regulation System Based on LM3S8962 ARM

Neuron Control DC Speed Regulation System Based on LM3S8962 ARM