# Practical Control of a Cold Milling Machine using an Adaptive PID Controller
[3] designed novel cutters of the cold milling machines to reduce the power consumption. [PERSON] et al. [4] and [PERSON] [5] studied the theoretical methods of the power control for a cold milling machine, but specific power control algorithms were not proposed for the machine power control. [PERSON] et al. [6] and [PERSON] [7] mainly conducted the hardware design of the power control system for a cold milling machine. [PERSON] et al. [8; 9] investigated the vehicle dynamics to realize the power control of a cold milling machine, but only numerical simulations were used to validate the proportional-integral-derivative (PID) controller and the PID parameters were fixed. A traditional PID controller with the fixed-control gains cannot achieve the optimal control performance and is not adaptive to environment variations for the cold milling machine power control featured with a nonlinear and uncertain system [10; 11]. There are several alternative control methods, such as neural network control [12; 13; 14; 15], fuzzy control [16; 17; 18], robust control [19; 20; 21; 22], and adaptive control [17; 18; 19; 20]. The neural network control has strong self-study ability to control a nonlinear system, but it needs lots of computation. It is not feasible as the cold milling machine is equipped with a 16 bit single chip microcomputer, which has a very limited computation capability. The performance of fuzzy controllers depends on the fuzzy rules which are difficult, if not possible, to be identified completely from our work experience at present. Therefore the fuzzy control is not selected to be an appropriate alternative for cold mining machines. Most of the robust control needs a reference model or sensitive coefficients [19; 20; 21; 22]. However, it is not possible for us to establish a reliable reference model and determine sensitivity coefficients that are needed in the controller design of the cold milling machine. Obviously, the robust control is not the option in the power control of a cold milling machine. Two typical adaptive control methods are STC (self-tuning control) and MRAC (model reference adaptive control). The MRAC needs a reference model which is not available for cold mining machines [23; 24]. The STC needs online identification and synthetic decision [25; 26]. There are little such synthetic decision algorithms and strategies for a cold milling machine at present. Therefore, the typical adaptive control is ruled out in this work. The single neuron control is a proper option as it possesses the strong ability of self-studying and self-adapting. The single neuron is used to adjust the control gains of the PID controller online, and hence it can overcome the shortage of the fixed control gains in traditional PID controllers. This single neuron PID not only has the characteristics of the neural network control but also has the characteristics of the PID control. Therefore, the single neuron PID can be implemented more easily in engineering application than the neural network control, the fuzzy control, the robust control and adaptive control. Furthermore, the strong ability of self-studying and self-adapting of the single neuron PID control can improve the performance and robustness of the adaptive power control. In order to address the practical control challenge for cold mining machines, this paper proposes a supervised Hebb learning single neuron PID method. The contributions of this work include: (1) this work attempts to develop and implement the proposed controller on a real machine with field testing; (2) in this work the control gain is tuned by a supervised Hebb learning algorithm, so the proposed controller can adapt to the environment variations; (3) the supervised Hebb learning based PID controller does not need any Sign functions to adjust the PID gains; therefore, the control performance is much more steady and accurate than that of the Sign function-based PID controllers. Field testing result demonstrates the performance of the proposed method. This paper is organized as follows.