This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system (CDPS). The designed controller, which is based on the human-simulated intelligent control (HSIC) method, builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase, a swing-up phase, a posture adjustment phase, and a balance control phase. For the approach, the original method of inequalities-based (MoI) multiobjective genetic algorithm (MMGA) is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary, the number of swings, the settling time, the overshoot of the total energy, and the control effort. The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.

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In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the endeffector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.