Advanced process control (APC) is a broad term within the control theory. It is composed of different kinds of process control tools. Advanced process control is often used for solving multivariable control problems or discrete control problems.
An adaptive control system can be defined as a feedback control system intelligent enough to adjust its characteristics in a changing environment in order to operate optimally according to some specified criteria. In general terms, adaptive control systems have achieved great success in aircraft, missile and spacecraft control applications. It can be concluded that traditional methods of adaptive control are mainly adequate for:
• Mechanical systems that do not have significant delays; Y
• Systems that have been designed so that their dynamics are well understood.
However, in the applications of control of industrial processes, the traditional adaptive control has not been very successful.
Robust control is a controller design method that focuses on the reliability (robustness) of the control algorithm. Robustness is generally defined as the minimum requirement that a control system must meet to be useful in a practical environment. Once the controller is designed, its parameters do not change and control performance is guaranteed. The robust control methods are suitable for applications in which the stability and reliability of the control system are the main priorities, the dynamics of the process is known and the ranges of variation for the uncertainties can be estimated. The controls of aircraft and spacecraft are some examples of these systems.
Predictive control, or predictive control of the model (MPC), is one of the few advanced control methods that are used successfully in industrial control applications. The essence of predictive control is based on three key elements:
• Predictive model,
• Optimization in the range of a time window, and
• Feedback correction.
These three steps are usually carried out continuously by online computer programs. Predictive control is a control algorithm based on the predictive model of the process. The model is used to predict the future product based on the historical information of the process and future information. Emphasizes the role of the model, not the structure of the model. Predictive control is an optimal control algorithm. Calculate future control actions based on a penalty function or performance function. The optimization of the predictive control is limited to a mobile time interval and is carried out continuously online. The mobile time slot is sometimes called a time window. This is the key difference compared to the traditional optimal control that uses a performance function to judge global optimization
Predictive control is also a feedback control algorithm. If there is a mismatch between the model and the process, or if there is a problem of control performance caused by system uncertainties, predictive control could compensate for the error or adjust the parameters of the model based on online identification.
Optimal control is an important component in modern control theory. Its great success in space, aerospace and military applications has changed our lives in many ways. The statement of a typical problem of optimal control can be expressed as follows: “The equation of state and its initial condition of a system to be controlled is given, and the set of defined objectives is also provided.” Find a feasible control such that the system that starts from the given initial condition transfer its status to the set of objectives, and minimize an index of performance. In practice, optimal control is well suited for space, aerospace and military applications, such as the lunar landing of a spacecraft, the control of a rocket flight and the blocking of a defense missile.
Intelligent control is another major field in modern control technology. There are different definitions regarding intelligent control, but it is referred to as a control Para diagram that uses various artificial intelligence techniques, which may include the following methods:
• Learning control,
• Expert control,
• Fuzzy control, and
• Neural network control.
Learning Control: Learning control uses pattern recognition techniques to obtain the current status of the control loop; and then makes control decisions based on the loop status as well as the knowledge or experience stored previously.
Expert Control: Expert control, based on the expert system technology, uses a knowledge base to make control decisions. The knowledge base is built by human expertise, system data acquired on-line, and inference machine designed. Since the knowledge in expert control is represented symbolically and is always in discrete format, it is suitable for solving decision making problems such as production planning, scheduling, and fault diagnosis. It is not well suited for continuous control issues.
Fuzzy Control: Fuzzy control, unlike learning control and expert control, is built on mathematical foundations with fuzzy set theory. It represents knowledge or experience in a mathematical format that process and system dynamic
characteristics can be described by fuzzy sets and fuzzy relational functions. Control decisions can be generated based on the fuzzy sets and functions with rules.
Neural Network Control: Neural network control is a control method using artificial neural networks. It has great potential since artificial neural networks are built on a firm mathematical foundation that includes versatile and well
understood mathematical tools. Artificial neural networks are also used as one of the key elements in the model-free adaptive controllers