Industrial robots are gradually being employed in machining processes, particularly the cutting process, owing to their flexibility, mobility, and economic efficiency. However, it is difficult to make the manufacturing process intelligent owing to the complexity of robot machining process information handling and programming. In this paper, the architecture of a STEP-NC compliant closed-loop robot machining system was designed, including its function model and information stream. A methodology based on STEP-NC was established to enable the analysis of high-level information directly and automatically generating robot program according to the actual machining conditions. The STEP-NC Application Activity Model (AAM) and Application Reference Model (ARM) of closed-loop robot machining system is built to integrate the machining process data, monitoring and inspection data, mechanical equipment data, machining status data and inspection result data within a unified data flow, making it possible to realize intelligent manufacturing and adaptively adjusting the robot machining process. The proposed closed-loop robot machining system was implemented based on an open STEP-NC interpreter that interprets the high level information in STEP-NC directly to reduce machining robot programming time. An industrial camera was integrated with the robot for rawpiece positioning, then the STEP-NC interpreter can generate robot path rapidly according to the parameters of manufacturing features and position of rawpiece. The STEP-NC interpreter can generate a robot control program or communicate with a software controller using an application program interface, so it can be integrated with both existing industrial robot controllers and future open robot controllers. Finally, case studies are conducted for the functional verification of the proposed STEP-NC compliant closed-loop robot machining system.

The adaptive iterative learning control method for electro-hydraulic shaking tables based on the complex optimization algorithm was proposed to overcome the potential stability problem of the traditional iteration control method. The system identification precision’s influence on convergence was analyzed. Based on the real optimization theory and the mapping relationship between real vector space and complex vector space, the complex Broyden optimization iterative algorithm was proposed, and its stability and convergence was analyzed. To improve the stability and accelerate the convergence of the proposed algorithm, the complex steepest descent algorithm was proposed to cooperate with the complex Broyden optimization algorithm, which can adaptively optimize the complex steepest gradient iterative gain and update the system impedance in real time during the control process. The shaking tables experiment system was designed, applying xPC target rapid prototype control technology, and a series of experimental tests were performed. The results indicated that the proposed control method can quickly and stably converge to the optimal solution no matter whether the system identification error is small or large, and, thus, verified that validity and feasibility of the proposed adaptive iterative learning method.