Advanced Control Methods Overview

1. Adaptive Control

An adaptive control system can be defined as a feedback control system intelligent enough to adjust its characteristics in a changing environment so as to operate in an optimal manner according to some specified criteria.

Generally speaking, adaptive control systems have achieved great success in aircraft, missile, and spacecraft control applications. It can be concluded that traditional adaptive control methods are mainly suitable for (1) mechanical systems that do not have significant time delays; and (2) systems that have been designed so that their dynamics are well understood.
In industrial process control applications, however, traditional adaptive control has not been very successful. The most credible achievement is probably the above-described PID self-tuning scheme that is widely implemented in commercial products but not very well used or accepted by the user.

Traditional adaptive control methods, either model reference or self-tuning, usually require some kind of identification for the process dynamics. This contributes to a number of fundamental problems such as (1) the amount of off line training required, (2) the tradeoff between the persistent excitation of signals for correct identification and the steady system response for control performance, (3) the assumption of the process structure, and (4) the model convergence and system stability issues in real applications.

In addition, traditional adaptive control methods assume the knowledge of the process structure. They have major difficulties in dealing with nonlinear, structure variant, or large time delayed processes.

2. Robust Control

Robust control is a controller design method that focuses on the reliability (robustness) of the control algorithm. Robustness is usually defined as the minimum requirement a control system has to satisfy 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, either in time domain or frequency domain, usually assume the knowledge of process dynamics and its variation ranges. Some algorithms may not need a precise process model but then require some kind of off-line identification.

The design of a robust control system is typically based on the worst case scenario, so that the system usually does not work at optimal status in sense of control performance under normal circumstances.

Robust control methods are well suited to applications where the control system stability and reliability are the top priorities, process dynamics are known, and variation ranges for uncertainties can be estimated. Aircraft and spacecraft controls are some examples of these systems.

In process control applications, some control systems can also be designed with robust control methods, especially for those processes that are mission critical and naturally have (1) large uncertainty ranges, and (2) small stability margins.

However, the design of a robust control system requires high level expertise. Once the design is properly accomplished, the system should work well without the need of much operator attention. But on the other hand, if upgrades or major modifications are required, the system has to be redesigned.

3. Predictive Control

Predictive control, or model predictive control (MPC), is one of only a few advanced control methods used successfully in industrial control applications.

The essence of predictive control is based on three key elements: (1) predictive model, (2) optimization in range of a temporal window, and (3) feedback correction. These three steps are usually carried on continuously by computer programs on-line.

Predictive control is a control algorithm based on the predictive model of the process. The model is used to predict the future output based on the historical information of the process as well as the future input. It emphasizes the function of the model, not the structure of the model. Therefore, state equation, transfer function, and even step response or impulse response can be used as the predictive model. The predictive model has the capability of showing the future behavior of the system. Therefore, the designer can experiment with different control laws to see the resulting system output, using computer simulation.

Predictive control is an algorithm of optimal control. It calculates future control action based on a penalty function or performance function. The optimization of predictive control is limited to a moving time interval and is carried on continuously on-line. The moving time interval is sometimes called a temporal window. This is the key difference compared to traditional optimal control that uses a performance function to judge global optimization. This idea works well for complex systems with dynamic changes and uncertainties since there is no reason in this case to judge the optimization performance based on the full time range.

Predictive control is also an algorithm of feedback control. If there is a mismatch between the model and process, or if there is a control performance problem caused by the system uncertainties, the predictive control could compensate for the error or adjust the model parameters based on on-line identification.

Due to its essence of predictive control, the design of such a control system is very complicated and requires high level expertise although the predictive control system works well in controlling various complex process control systems. This expertise requirement appears to be the main reason why predictive control is not used as widely as it deserves to be.

Due to its nature, predictive control is well suited for advanced process control (APC) and supervisory control applications, where the control outputs are mainly a trajectory of setpoints. Predictive control is not well suited to deal with regulatory control problems.

4. Optimal Control

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 optimal control problem can be expressed in the following: The state equation and its initial condition of a system to be controlled are given. The defined objective set is also provided. Find a feasible control such that the system starting from the given initial condition transfers its state to the objective set, and minimizes a performance index.

In principal, optimal control problems belong to the Calculus of Variations. Pontryagin’s Maximum Principal and Bellman’s Dynamic Programming are two powerful tools to solve closed set constrained variation problems, which are related to most optimal control problems.

In practice, optimal control is very well suited for space, aerospace, and military applications such as the moon landing of a spacecraft, flight control of a rocket, and the missile blocking of a defense missile.

In industrial systems, there are some optimal control related issues such as the control of bacteria content in a bioengineering system, etc. However, most process control problems are related to the control of flow, pressure, temperature, and level. They are not well suited to the use of traditional optimal control techniques.

5. Intelligent Control

Intelligent control is another major field in modern control technology. There are different definitions regarding intelligent control, but it is referred to a control paradigm that uses various artificial intelligence techniques, which may include the following methods: (1) learning control, (2) expert control, (3) fuzzy control, and (4) 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. Since learning control is limited by its stored knowledge, its application has never been popular.

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.

Although fuzzy control has great potential for solving complex control problems, its design procedure is complicated and requires a great deal of specialty. In addition, fuzzy math does not belong to the Field of Mathematics since many basic mathematical operations do not exist. For instance, the inverse addition is not available in fuzzy math. Then, it is very difficult to solve a fuzzy equation, yet solving a differential equation is one of the basic practices in traditional control theory and applications. Therefore, lack of good mathematical tools is a fundamental problem for fuzzy control to overcome.

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.