Hydraulic manipulators play an irreplaceable role in many heavy-duty applications. Currently, there are stronger demands for the hydraulic manipulator to achieve high precision, as well as high force/power. However, due to the inherent nonlinearities of its high-order dynamics, the precision of the manipulator has been a common weakness compared with electrically driven ones. Thus, in this paper, a nonlinear adaptive robust control method for the hydraulic manipulator is proposed. To make the controller more applicable to practical engineering projects, this study tried to control each joint independently instead of directly based on the complicated multi-degree high-order dynamics, while guaranteeing the control precision by the adaptive nonlinear model compensation, as well as a robust feedback design. The closed-loop control performance was theoretically verified. Besides, several sets of comparative motion tracking experiments were conducted, and the proposed closed-loop system achieved high precision under different trajectories and postures.

Implementing real-time machining process control at shop floor has great significance on raising the efficiency and quality of product manufacturing. A framework and implementation methods of real-time machining process control based on STEP-NC are presented in this paper. Data model compatible with ISO 14649 standard is built to transfer high-level real-time machining process control information between CAPP systems and CNC systems, in which EXPRESS language is used to define new STEP-NC entities. Methods for implementing real-time machining process control at shop floor are studied and realized on an open STEP-NC controller, which is developed using object-oriented, multithread, and shared memory technologies conjunctively. Cutting force at specific direction of machining feature in side mill is chosen to be controlled object, and a fuzzy control algorithm with self-adjusting factor is designed and embedded in the software CNC kernel of STEP-NC controller. Experiments are carried out to verify the proposed framework, STEP-NC data model, and implementation methods for real-time machining process control. The results of experiments prove that real-time machining process control tasks can be interpreted and executed correctly by the STEP-NC controller at shop floor, in which actual cutting force is kept around ideal value, whether axial cutting depth changes suddenly or continuously.

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.

Stripe images are crucial for ensuring the measurement quality of line-structured light sensors. To improve the measurement effectiveness of objects with different shapes, materials, and colors, an adaptive exposure method is proposed based on global grayscale statistical analysis of stripe images. The logarithm sum of grayscale statistical results is calculated as the quality evaluation parameter for each stripe image. Theoretical analysis and experiments demonstrate that the proposed quality evaluation value exhibits an approximate linear relationship with a camera’s exposure time. Subsequently, an adaptive exposure control method is developed. The influence of control system parameters on measurement results is also analyzed in detail. The experimental results show that our method can adaptively adjust a camera’s exposure time according to different surface characteristics. Both the number of effective measurement points and the accuracy are improved.