I. Functions of the controller
1. Data buffer
Because I/O devices have relatively low speeds while CPU and memory have very high speeds, a buffer must be included in the controller. During output, this buffer temporarily stores data transmitted at high speed from the host, and then transmits the data in the buffer to the I/O device at its own speed. During input, the buffer temporarily stores data sent from the I/O device, and after receiving a batch of data, transmits the data in the buffer to the host at high speed.
2. Error control
The device controller also handles error detection for data transmitted from I/O devices. If an error is detected during transmission, the error detection code is typically set and reported to the CPU. The CPU then discards the transmitted data and retransmits it. This ensures the correctness of the data input.
3. Data exchange
This refers to the data exchange between the CPU and the controller, and between the controller and the devices. For the former, the CPU writes data to the controller in parallel or reads data from the controller in parallel via the data bus; for the latter, the device inputs data to the controller or transmits data from the controller to the device. For this purpose, data registers must be configured in the controller.
4. Status Description
The status controller for identifying and reporting devices should record the device's status for the CPU to understand. For example, the CPU can only initiate the controller to read data from the device when the device is in a transmit-ready state. To this end, a status register should be set up in the controller, with each bit reflecting a specific state of the device. When the CPU reads the contents of this register, it can understand the status of the device.
5. Receiving and recognizing commands
The CPU can send various commands to the controller, and the device controller should be able to receive and recognize these commands. Therefore, the controller should have corresponding control registers to store the received commands and parameters, and to decode the received commands. For example, a disk controller can receive 15 different commands from the CPU, such as Read, Write, and Format, and some commands may also have parameters; correspondingly, the disk controller has multiple registers and a command decoder.
6. Address recognition
Just as every memory location has an address, every device in the system also has an address, and the device controller must be able to identify the address of each device it controls. Furthermore, in order for the CPU to write (or read) data into (or from) registers, these registers must each have a unique address.
II. Fundamentals and Characteristics of Intelligent Controller Technology
As the name suggests, a "smart controller" is a small device that intelligently controls a type of electronic machine. Smart controllers are widely used in home appliances, power tools, automotive electronics, and healthcare electronics, and even in today's most popular and trendy smart home architecture. In these fields, smart controllers play the role of the "nerve center" and "brain." So how are smart controllers classified? Let's explore this together!
Intelligent control is based on disciplines such as control theory, computer science, artificial intelligence, and operations research, and has expanded on related theories and technologies. Among the more widely used are theories such as fuzzy logic, neural networks, expert systems, and genetic algorithms, as well as technologies such as adaptive control, self-organizing control, and self-learning control.
An expert system is a control system that uses expert knowledge to describe specialized or difficult problems. Although expert systems have achieved considerable success in solving complex high-level reasoning problems, their practical applications are still relatively limited.
Fuzzy logic uses fuzzy language to describe systems, and can describe both quantitative and qualitative models of application systems. Fuzzy logic is applicable to the control of arbitrarily complex objects.
Genetic algorithms, as a nondeterministic quasi-natural stochastic optimization tool, have the characteristics of parallel computing and fast global optimal solution finding. They can be used in combination with other techniques for optimal control of parameters, structure or environment in intelligent control.
Neural networks are adaptive control methods that utilize a large number of neurons to learn and adjust according to a specific topology. They can exhibit a wealth of characteristics, including parallel computing, distributed storage, variable structure, high fault tolerance, nonlinear operation, self-organization, and learning or self-learning. These characteristics are system properties that people have long sought and desired. Neural networks possess unique capabilities in intelligent control, particularly in the adaptation, self-organization, and self-learning of parameters, structures, or environments.
