Real-time controllable (RTC) mechanisms refer to those mechanisms driven by RTC actuators or servomotors. There are many benefits to an RTC mechanism that is force balanced. A novel method called the Adjusting Kinematic Parameters method for force balancing of RTC mechanisms is more promising than the Counterweights method. The motivation of the research described in this thesis is to overcome the problems with the present Adjusting Kinematic Parameters method and further extend it to be more general and robust.
The extended Adjusting Kinematic Parameters method is presented in this thesis. This method can work for any general mass distribution of a closed-loop RTC mechanism, and is extended to incorporate the masses of adjusting sliding blocks. The extended Adjusting Kinematic Parameters method has been verified to be consistently better than the Counterweights method for closed-loop RTC mechanisms.
The generic task of an RTC mechanism is trajectory tracking. Trajectory planning is needed for trajectory tracking. A new method for trajectory planning that can achieve C3 continuity is developed in this thesis. The new method, based on quintic polynomials for trajectory planning, can insure that the trajectory has a smooth acceleration curve and a continuous jerk on the trajectory.
The dynamics of an RTC mechanism is important for developing a better controller to achieve optimal trajectory tracking performance. To study the extended AKP method, the dynamic model of a 2 degree of freedom RTC mechanism is developed with consideration of the off-line mass center of a link using the reduced order method. Stability analysis for the Proportional Derivative (PD) control applied to the closed-loop RTC mechanism is discussed.
Dynamic control is an essential part of an RTC mechanism. Based on the analysis of the existing Nonlinear PD control method and the Computed Torque Control method, a novel PD-based control method, namely, the Evolutionary PD control method, is proposed. The Evolutionary PD method incorporates plant dynamics into the control law in such a way that the control law is the result of the superposition of a series of runs of a controlled plant system.
Case studies are carried out for force balanced mechanisms using the extended Adjusting Kinematic Parameters method, the Counterweights method, and the unbalanced mechanism in terms of the joint forces, trajectory tracking performance, and fluctuation of the torques in the actuators. Three control laws (i.e., PD control, Nonlinear PD control, and Evolutionary PD control) are used to perform the feedback control for several case studies. All the simulation results show that the extended Adjusting Kinematic Parameters method is better than the Counterweights method with respect to the reduction of joint forces and trajectory tracking errors. It is also shown that the Evolutionary PD control law is a promising control law when compared with the PDlNonlinear PD control laws in terms of the selection of control gain and high trajectory tracking performance.