High Precision Motion Control of Electro-Mechanical Launching Platform with Modeling Uncertainties: A New Integrated Error Constraint Asymptotic Design

For the demands of a high precision motion control of an uncertain electro-mechanical launching platform, a novel integrated error constraint asymptotic control in the presence of parametric uncertainties and uncertain disturbance is proposed, of which the barrier function method and a continuous asymptotic control design are integrated for the first time. The former technique can effectively avoid excessive tracking errors at the transient phase, which is caused by the disturbance and the large uncertain system parameters’ deviation between the initial estimated value and the actual value, by selecting a proper barrier threshold, while the latter technique can handle the uncertain disturbance to achieve asymptotic tracking. A rigorous stability analysis is given to illustrate the theoretical performance. In addition, as a supplementary measure, repetitive control is employed to estimate and compensate the possible periodic-like disturbance under certain conditions. Two experimental cases on a prototype of a launching platform demonstrate the effectiveness of the proposed controller.

Analysis of energy absorption characteristics of corrugated top beam of anti-impact hydraulic support

To solve the problem that the response of the walking platform of bricklaying robot is slow and uncertain factors such as load change and friction of hydraulic cylinder affect the adjustment accuracy, an electro-hydraulic servo system with four hydraulic cylinders is designed. The system is an improved ADRC method. The available information of the whole system is fully considered, and the piston rod position deviations of the hydraulic cylinder is taken as the input of the whole controller to reduce the phase lag caused by the extended observer and improve the response speed of the control system. The position deviations of the piston rod in a cylinder is compensated by observation, and the high-order term and uncertain disturbance in the whole control system are defined as total disturbance, so the structure of the controller is simplified appropriately. Finally, MATLAB and AMESim are used for simulation analysis. The simulation results show that the improved ADRC method can significantly improve the response speed of the system and the suppression of uncertain disturbance. This research provides a theoretical basis for the research of masonry robot mobile platforms.

Multi-layer collaborative optimization method for solving fuzzy multi-objective integrated process planning and scheduling

Research on integrated process planning and scheduling (IPPS) is of great significance to the improvement of the overall quality of machinery manufacturing system. In the actual manufacturing process, the manufacturing system is often accompanied by some unpredictable uncertain disturbance factors, for instance uncertain processing time of jobs and changes of due date, etc. These uncertain disturbance events will ultimately affect production efficiency and customer satisfaction. Consequently, this paper considers the multi-objective IPPS problem with uncertain processing time and uncertain due date. A multi-layer collaborative optimization (MLCO) method is designed for the fuzzy multi-objective IPPS (FMOIPPS) problem, including three layers. For the process planning layer, the basic genetic algorithm is used to provide various near optimal process plans for the process selection system. For the process selection layer, a multi-objective genetic algorithm (MOGA) is designed to optimize the process selection population. A sharing function method is introduced to maintain population diversity. An individual comprehensive evaluation method is introduced to evaluate non-dominated solutions. The crowded distance, fast non-dominated sorting and elite strategy based on NSGAII is adopted in the proposed MOGA. The external archive method is employed to preserve the non-dominated solutions generated during population evolution. For the scheduling layer, a MOGA with a boundary search strategy is proposed. The boundary search strategy is designed to improve the search ability of boundary solutions. Three optimization objectives are minimizing the spread of fuzzy makespan, minimizing fuzzy makespan and maximizing average customer satisfaction simultaneously. The target of scheduling layer is to make scheduling arrangements for the process information obtained by process selection layer. Through mutual cooperation among each layer, guide the overall optimization process, and finally get satisfactory solutions. Different problem examples of various scales are employed to verify feasibility and effectiveness of the MLCO method. The experimental results indicate that the MLCO method can effectively address FMOIPPS problem.