## 2 Introduction
### Intelligent Control Systems  
Artificial intelligence (AI) has two main schools of thought: classical AI and machine learning. In classical AI, knowledge is hard-coded into systems by domain experts in terms of domain rules. This approach requires extensive human effort to encode the domain knowledge into the computer and is restricted to well-structured applications mainly. In a more recent approach, namely the machine learning (ML) approach, knowledge is learnt automatically from data by the computer.  
In ML, there are several types of problems. In supervised learning, the training data are labeled by humans and the computer learns the relations between the data and the labels. In unsupervised learning, the training data are not labeled, so the computer tries to find clusters of data with similar properties by some criteria. In reinforcement learning, there is no label for data but only partial knowledge about the consequences of actions as positive or negative, so that the computer can learn the best actions leading to most positive results.  
One important concept in ML is the complexity of the system model that the computer uses to map data to classes or clusters. Selecting the optimal complexity of the system model is important because, if the selected model is too simple, it cannot model the underlying process in a detailed enough manner. On the other hand, if the selected model is too complex, it can learn noises in training samples instead of parameters in the underlying process. This phenomenon is called overfitting and is a major concern in solutions to ML  
Figure 1: Block diagram of a typical automatic control system.  
problems. A system with optimal complexity has high generalization performance, which is another important ML concept of performance on the unseen data.  
Intelligent control systems utilizing AI, especially ML, are extensively studied in the literature. In particular, fuzzy controllers and neural network-based controllers are utilized to increase the performance and robustness of the systems [17; 18; 19; 20; 21; 22; 23; 24; 25]. In [17], the speed control of non-holonomic robots is achieved through adaptive gain scheduling and fuzzy multi-model intelligent control and two control strategies are compared. Both implementations are found to have satisfactory performance, while that of the intelligent controller is found to be higher. In [23], the fuzzy intelligent control of servo press systems is studied. Step response experiments and sine tracking experiments are performed and the results are reported. In [24], intelligent control research for power systems is reviewed. Studies using artificial neural networks (ANNs), fuzzy logic, expert systems, and evolutionary methods are reported. In [25], an innovative optimal robust control method for mobile robots is proposed. The kinematic model of a wheeled mobile robot is derived and a Lyapunov function is used for ensuring control stability. In [26], the position tracking control of mobile robots is accomplished using neural network-based fractional order sliding mode control. A radial basis function neural network is used to deal with nonlinear dynamics.