Explore the control field: from TV remote control to autonomous driving [Basic concept understanding, application examples]

When you think of cybernetics and control systems, you might think of television remote controls, self-driving cars, aircraft autopilot systems, and many other automated systems. But cybernetics is a broader discipline that encompasses a variety of fields, from engineering to biology, from economics to environmental science. Let us have an in-depth understanding of the basic concepts of control science and its applications in various fields.

What is cybernetics and what does it involve?

Definition of control science:

Control science is the study of how to design, analyze, and optimize systems to achieve desired performance. These systems can be physical systems (such as mechanical systems, electrical systems, chemical processes, etc.) or abstract systems (such as economic systems, ecological systems, computer systems, etc.). The goal of control science is to develop control strategies and methods to keep a system in a desired state, or to guide it from its current state to a desired state.

Control science mainly involves the following aspects:

1. Goal Settings: Control science starts with determining what you want the system to do, just like you would select your desired TV channel with a remote control. This is the starting point for control. You need to clarify the goals and performance indicators of the system.

2. Sensors: These are the eyes and ears in control, they help us understand the current state of the system, for example a temperature sensor tells us how hot a room is. Sensors are used to provide feedback information to the system, which is a very important part of control. 监测

3. Processor: This is the brain in control, deciding what actions to take based on the information provided by the sensors, just like your brain decides whether to turn on the air conditioner to lower the temperature. The processor includes control algorithms and decision rules.

4. Actuators: These are the hands in control that perform actions based on instructions from the processor, such as the controller telling the TV to turn on or off. Actuators can be physical devices such as electric motors, valves, motors, etc.

5. Feedback: Control science also involves constantly checking how your system is performing and making fine adjustments as needed, just like you might need to constantly adjust the volume on your TV to make sure it's the right volume. Feedback refers to the actual output information of the system, which is used to compare with the expected output in order to adjust the control strategy.

The core idea of ​​control science is to monitor the status of the system, compare the actual status with the desired status, and then take control measures to keep them as consistent as possible. Control systems typically make decisions based on 反馈信息 or 预测模型 to achieve various goals such as stability, performance, and safety.

In the following sections, we will explore the basic principles and applications of control systems in more depth.

Basic principles of control systems

The basic principles of control systems involve several key concepts, including feedback control and feedforward control:

Feedback control:

Feedback control is similar to how you use the steering wheel to adjust the direction of your car while driving. In a control system, we use sensors to measure the current state of the system and then feed this information back to the controller. The controller compares the current state to our desired state and then takes action to make them consistent. Feedback control constantly adjusts operation if the system strays from its target, just like you keep turning the steering wheel to keep your car on the right path.

Feedforward control:

Feedforward control is a 预测性 control method, just like you know there is a pothole in front, so slow down in advance Same. Feedforward control avoids deviation from the target by predicting the behavior of the system and taking steps in advance. It not only relies on feedback information, but also considers the characteristics of the system and environmental factors to minimize errors.

• Feedback control is adjusted according to实际观测
• Feedforward control is adjusted according to预测.

These principles play a key role in automation and engineering applications in various fields to ensure system stability, performance and safety.

The difference between open loop control and closed loop control

In control science, there are two main types of control methods, namely open-loop control and closed-loop control:

1. Open loop control:

• Definition: Open-loop control is a control system in which a controller responds to predetermined inputs to operate the operating system, and无需monitor the actual output or feedback information of the system.

• Working principle: In open-loop control, the operation of the systemDoes not rely on real-time feedback. The controller sends instructions and the system follows those instructions. However, open-loop control does not adjust operation in response to external perturbations or changes within the system, so stability and accuracy may not be guaranteed.

• Example: A simple example is a washing machine program. You select the wash mode and time for your washing machine and then start it. The machine will operate in the way you choose, regardless of the condition of the laundry during the wash cycle.

2. Closed-loop control:

• Definition: Closed-loop control is a control system in which the controller adjusts operations based on the actual output or feedback information of the system to ensure that the system reaches the desired state.

• How it works: In closed-loop control, the controller continuously monitors the performance of the system and then makes adjustments based on the difference between the actual output and the desired output. This feedback mechanism enables closed-loop control to adapt to external changes and disturbances, making it more stable and precise.

• Example: A car's cruise control system is an example of closed-loop control. The system uses sensors to monitor the distance between the vehicle and the vehicle ahead, then automatically adjusts acceleration and braking force to maintain a safe following distance. This control system adjusts to changes in actual road and traffic conditions to meet speed and distance requirements set by the driver.

The main difference is that open-loop control does not rely on feedback information, while closed-loop control is based on feedback informationReal-time adjustment.

Closed-loop control is generally more suitable for systems that require high accuracy and stability because it can proactively correct errors. Open-loop control is more suitable for some simple systems that do not require real-time adjustments or are not easily affected by external changes.

What is PID control and what are its applications in engineering and automation?

PID control is a common control method that is used to adjust the system to maintain the desired state. PID stands for Proportional-Integral-Derivative, which represent the three main parts of the controller. Let’s take a deeper look at the principles and applications of PID control.

Three parts of PID control:

1. Proportional (P) part: If the room temperature deviates from the target temperature, the proportional part will generate a control output according to the degree of deviation. If the temperature is low, it increases the power of the heater to heat the room. If the temperature is high, it reduces the heater power to lower the temperature. This is what proportional control does.

2. Integral (I) part: The integral part takes into account the cumulative deviation of the temperature over time. If the room temperature is lower than the target temperature for a long period of time, the integral part will gradually increase the power of the heater to compensate for the prolonged low temperature. This helps avoid temperature fluctuations around the target value for long periods of time.

3. Derivative (D) part: The differential part focuses on the rate of change of temperature. If the temperature starts to rise rapidly, the derivative part will reduce the power of the heater to avoid the temperature exceeding the target value. This helps prevent overshoot.

• The P (proportional) part is responsible for adjusting the heater power based on the current deviation.
• The I (integral) part is responsible for handling long-term deviations.
• The D (derivative) part handles the speed of temperature change.

They work together to ensure that the temperature remains as stable as possible around the target value without large fluctuations or overshoots.

Application example: Temperature control

PID control has wide applications in engineering and automation fields. A common application example is temperature control, such as in heating systems. PID control can ensure that the heater automatically adjusts the heating power based on the difference between the actual temperature and the target temperature to stabilize the temperature within the desired range. This is useful in a variety of situations such as ovens, climate-controlled rooms, cooling systems, and more.

In addition to temperature control, PID control is also widely used in various fields such as autonomous vehicles, robots, chemical processes, motor control, water treatment systems, and aircraft flight control. It is a flexible and versatile control method that can be adjusted and optimized according to the needs of different applications.

How do control systems deal with uncertainty and noise?

In the fields of automation and engineering, control systems must be able to handle uncertainty and noise to ensure system performance and stability. Here are some common methods and strategies for dealing with uncertainty and noise:

1. Robust control:

• Principle: Robust control is a method of designing a controller that can handle system parameter uncertainty or external disturbances. It takes into account that the system model is not perfect, so the controller is designed to have some degree of tolerance in response to system changes.

• Application examples: In autonomous driving, road and traffic conditions may constantly change, while vehicle parameters may also change slightly. Robust control ensures that autonomous vehicles can drive stably and safely in uncertain environments.

2. Filter:

• Principle: Filters are used to remove noise from sensor data to improve data accuracy. A common filter is the Kalman filter, which uses historical measurement data and a system model to estimate the most likely state and reduce the impact of measurement error.

• Application examples: Autonomous vehicles use sensors, such as lidar and cameras, to sense their surroundings. These sensor data may be affected by noise, which can be used to reduce noise and improve the accuracy of environmental perception. 卡尔曼滤波器

3. Feedback control:

• Principle: A feedback control system continuously monitors the vehicle's status and then adjusts the vehicle's operation based on the difference between the actual state and the desired state. This allows the vehicle to correct trajectory deviations and maintain a safe following distance in real time.

• Application examples: In autonomous driving, feedback control is used to adjust the speed, steering, and braking of vehicles in real time to ensure that they follow a predetermined path while maintaining alignment with other vehicles. safe distance.

4. Saturation control:

• Principle: Saturation control is a method of preventing system operation beyond acceptable limits. It ensures that the control output remains within a controllable range, preventing system overload or instability.

• Application example: In autonomous driving, saturation control can be used to ensure the vehicle's speed and steering angleWill not exceed the safe range, to prevent accidents or out-of-control situations.

5. Adaptive control:

• Principle: Adaptive control is a method that can adjust controller parameters according to the dynamic characteristics of the system. It can cope with changes in system parameters over time.

• Application examples: In autonomous driving, vehicles may encounter different road and weather conditions, and adaptive control can adjust controller parameters according to these changes to ensure that the vehicle performance and security.

6. System modeling:

• Principle: In system modeling, developers establish a mathematical model to describe the dynamic behavior of the vehicle. This model can be used to predict and control vehicle behavior.

• Application examples: In autonomous driving, system modeling can be used to predict the vehicle's response in different driving situations, thereby optimizing the control algorithm to achieve a safe and comfortable driving experience. .

These methods and strategies can be used individually or in combination to handle uncertainty and noise based on specific application and system requirements to ensure control system performance and stability.

What are the principles and applications of MPC?

Model Predictive Control (MPC) is an advanced control strategy that is widely used in automation systems to achieve high performance and precise control. The principles and applications of MPC are as follows:

principle:

The core idea of ​​MPC is to use the system model in each control cycle to predict the system response in the future period and select the current control input to meet the performance indicators to the greatest extent. Specifically, MPC performs the following steps:

1. System modeling: First, the dynamic behavior of the system is modeled into a mathematical model, usually a difference equation or a state space equation. This model is used to describe the state changes and responses of the system.

2. Performance metric definition: Determine the performance metric, that is, the desired system behavior. This can include minimizing errors, maximizing efficiency, satisfying constraints, etc.

3. Future prediction: During each control cycle, MPC uses the system model to predict the system status and output for a period of time in the future. This prediction can take into account different control inputs in order to select the best control strategy.

4. Optimization problem solving: Based on predictions, MPC solves an optimization problem to find the current control inputs to best meet the performance indicators. This can be a nonlinear optimization problem that needs to be solved iteratively during each control cycle.

5. Implement control inputs: Once the optimal control inputs are determined, the system implements these inputs to control the system, and then the control cycle ends.

6. Repeat iteration: This process is repeated during each control cycle to achieve dynamic, adaptive control.

Application examples:

In the fields of automation and engineering, MPC has a wide range of applications, including but not limited to the following:

1. Trajectory tracking: In autonomous vehicles, MPC is used to track predetermined vehicle trajectories to ensure that the vehicle remains on the planned path under complex road conditions. It predicts vehicle trajectories and adjusts control inputs based on highly accurate models and sensor data.

2. Dynamic obstacle avoidance: When there are dynamic obstacles (such as other vehicles) on the road, MPC can be used to predict their trajectories and plan avoidance based on these predictions Strategy. This allows autonomous vehicles to intelligently avoid obstacles when encountering them to ensure safe passage.

3. Speed ​​and Comfort Control: MPC can be used to control the speed and comfort of the vehicle. It can dynamically adjust vehicle speed and steering based on road conditions and vehicle dynamics models, providing a smoother and more comfortable driving experience.

4. Energy Management: In electric vehicles, MPC can optimize energy management to maximize battery range. It can adjust motor power and energy recovery strategies by taking into account the vehicle's power needs, battery status and road conditions.

5. Vehicle Stability Control: MPC can be used to improve vehicle stability, especially in extreme driving situations. It can predict potential out-of-control situations based on vehicle models and sensor data and take steps to maintain stability.

Overall, the application of MPC in automation can improve the accuracy and efficiency of control decisions, allowing the automation system to better adapt to changing environmental and system conditions. It is a powerful control strategy that balances real-time performance and security for reliable automation.

In the fields of automation and engineering, what are some specific cases or projects where control theory has been used to solve problems?

Control theory has many specific application cases and projects in the fields of automation and engineering, here are some examples:

1. Aerospace: Control theory is widely used in the automatic driving, navigation and flight attitude control of aircraft. Autonomous aircraft, missiles, satellites, and space probes use control theory to maintain stable flight, achieve precise orbital control, and perform precise missions.

2. Manufacturing Automation: Automated factories and production lines use control theory to control robots, robotic arms, conveyor belts and production processes. This helps improve production efficiency, reduce production costs and improve product quality.

3. Power Systems: Power systems use control theory to monitor and control power distribution, grid frequency, power loads, and power generation. Automatic generator control, power plant dispatch, and smart grids all rely on control theory to ensure stable operation of power systems.

4. Self-driving cars: Self-driving cars use control theory to achieve autonomous navigation, trajectory tracking and obstacle avoidance of the vehicle. The application of control theory enables autonomous vehicles to intelligently perceive the surrounding environment and make decisions.

5. Medical Devices: Medical devices, such as ventilators, pacemakers, and surgical robots, use control theory to monitor patients’ vital signs, deliver treatments, and perform precise medical procedures .

6. Traffic Management Systems: Urban traffic management systems use control theory to optimize signal control, traffic flow monitoring and traffic planning to reduce traffic congestion, increase road safety and improve traffic efficiency.

7. Environmental Control: Greenhouses, building automation systems, and energy management systems use control theory to maintain temperature, humidity, and other environmental conditions to increase agricultural yields, improve indoor comfort, and save money energy.

8. Communication Systems: Communication systems use control theory to optimize data transmission, signal processing, and network management. This helps ensure reliable communications, maximize network performance, and optimize bandwidth allocation.

9. Water Resources Management: Water resources management systems use control theory to monitor and control reservoirs, water plants and drainage systems to ensure sustainable water supplies and reduce flood risks.

10. Food Processing and Pharmaceuticals: The food processing and pharmaceutical industries use control theory to control production processes, mixing, packaging, and quality control to ensure that products comply with standards and regulations.

These are some typical examples of applications of control theory in automation and engineering. The principles and methods of control theory provide effective tools for these applications and help improve the performance, reliability and efficiency of automation systems.