Consideration should be given to the routine use of both internal thoracic arteries for coronary artery bypass grafting. When additional grafts are required, there is no evidence to suggest that either the radial artery or saphenous vein is superior.

This study presents a method of controlling robots based on fuzzy logic to eliminate the effect of uncertainties that are generated by the cutting forces in milling process. The common method to control industrial robots is based on the robot dynamic model and the differential equations of motion to compute the control values. The quantities in the differential equations of the motion of robots are complex and difficult to determine fully and accurately. The interaction forces between the cutting tool and the workpiece are the cutting forces, which are generated during the machining process. It is difficult to calculate the cutting force because it depends on many factors such as material of the machining part, depth of cut, feed rate, etc. This article presents the fuzzy rule system and the selection of the physical value domain of input and output variables of the fuzzy controller. The fuzzy rules are applied in this article to allow us to compute the driving forces based on the errors of input and output signals of the joint positions and velocities, thereby avoiding the calculation of cutting forces. This article shows the simulation results of the fuzzy controller and comparison with the results of the conventional controller when the dynamic model is assumed to be correctly determined. The achieved results are reliable and facilitate the research and application of a fuzzy controller to mechanical processing robots in general and milling machining in particular.

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Closed-form mechanisms of relative manipulation robot is an effective structure which helps to improve the accuracy and flexibility in technological processes. Unfortunately, the requirement about knowledge of exact dynamics of closed-form mechanisms of relative manipulation robot is arduous since it consists of numerous joints and links, and the identification of the kinematic relationship of closed-form structure is also complicated. This causes several shortcomings for controlling closed-form mechanisms of relative manipulation robot by using vector control algorithms because these methods require exactly dynamical equations of control systems. In contrast, the fuzzy controllers do not require knowledge of detailed mathematical equations of the control system since the fuzzy sets aim to capture the semantics of natural linguistic terms present in the fuzzy controller knowledge. Moreover, they have capability of handling uncertain and noisy signals, this helps to deal with the external environmental forces. This article proposes a fuzzy-based controller for closed-form mechanisms of relative manipulation robot to overcome mentioned problems by eliminating the identification of exact dynamics and kinematic constraints of closed-form structure. To verify the performance of the proposed method, the fuzzy-based controller is applied to a welding task by using a model of two-component mechanism which includes one closed-form manipulator and one serial manipulator. The welding task is also conducted by using conventional controllers in which the detailed dynamical equation is applied for the proportional-derivative (PD)-type and proportional integral derivative (PID)type computed torque controllers, whereas the fuzzy-based controller just uses several nominal parameters.

In this paper, the problem of controlling a human-like bipedal robot while walking is studied. The control method commonly applied when controlling robots in general and bipedal robots in particular, was based on a dynamical model. This led to the need to accurately define the dynamical model of the robot. The activities of bipedal robots to replace humans, serve humans, or interact with humans are diverse and ever-changing. Accurate determination of the dynamical model of the robot is difficult because it is difficult to fully and accurately determine the dynamical quantities in the differential equations of motion of the robot. Additionally, another difficulty is that because the robot’s operation is always changing, the dynamical quantities also change. There have been a number of works applying fuzzy logic-based controllers and neural networks to control bipedal robots. These methods can overcome to some extent the uncertainties mentioned above. However, it is a challenge to build appropriate rule systems that ensure the control quality as well as the controller’s ability to perform easily and flexibly. In this paper, a method for building a fuzzy rule system suitable for bipedal robot control is proposed. The design of the motion trajectory for the robot according to the human gait and the analysis of dynamical factors affecting the equilibrium condition and the tracking trajectory were performed to provide informational data as well as parameters. Based on that, a fuzzy rule system and fuzzy controller was proposed and built, allowing a determination of the control force/moment without relying on the dynamical model of the robot. For evaluation, an exact controller based on the assumption of an accurate dynamical model, which was a two-feedback loop controller based on integrated inverse dynamics with proportional integral derivative, is also proposed. To confirm the validity of the proposed fuzzy rule system and fuzzy controller, computation and numerical simulation were performed for both types of controllers. Comparison of numerical simulation results showed that the fuzzy rule system and the fuzzy controller worked well. The proposed fuzzy rule system is simple and easy to apply.

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