Differential Evolution Based High-order Peak Filter Design with Application to Compensation of Contact-induced Vibration in HDD Servo Systems

Sensitivity loop shaping using add-on peak filters is a simple and effective method to reject narrow-band disturbances in hard disk drive （HDD） servo systems. The parallel peak filter is introduced to provide high-gain magnitude in the concerned frequency range of open-loop transfer function. Different from almost all the known peak filters that possess second-order structures, we explore in this paper bow high-order peak filters can be designed to improve the loop shaping performance. The main idea is to replace some of the constant coefficients of common second-order peak filter by frequency-related transfer functions, and then differential evolution （DE） algorithm is adopted to perform optimal design. We creatively introduce chromosome coding and fitness function design, which are original and the key steps that lead to the success of DE applications in control system design. In other words, DE is modified to achieve a novel design for hard disk drive control. Owing to the remarkable searching ability of DE, the expected shape of sensitivity function can be achieved by incorporating the resultant high-order peak filter in parallel with baseline feedback controller. As a result, a seventh-order peak filter is designed to compensate for contact-induced vibration in a high-density HDD servo system, where the benefits of high-order filter are clearly demonstrated.

Ballistic learning control: formulation, analysis and convergence

In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems. The iterative learning control （ILC） theory provides a suitable framework for derivations and analysis of ballistic control under learning process. To overcome the obstacles caused by uncertain gradient and redundant control input, we incorporate extra trials into iterative learning. With the help of trial results, proper control and updating direction can be determined. Then, iterative learning can be applied to ballistic control problem. Several initial state learning algorithms are studied for initial speed control, force control, as well as combined speed and angle control. In the end, shooting angle learning in the basketball shot process is simulated to verify the effectiveness of iterative learning methods in ballistic control problems.