Modelling and control of two-link flexible manipulator

Flexible link manipulators have caught the interest of many researchers due to the limitations of their rigid counterparts. However, Flexible manipulators introduces undesired vibrations which is not easy to control due to its high-non linearity. In order to keep the advantages associated with the lightness and flexibility of the manipulators, accurate modelling of the system and efficient reliable controller have to be developed which is the focus of this study. The two-link flexible manipulator is split into 4 models, the Hub angle and endpoint vibrations of both links of the Two-Link Flexible Manipulator. Input and output data were obtained from an experimental rig. Each model was obtained through system identification techniques within MATLAB simulation environment, namely conventional Recursive Least Square and Cuckoo Search Algorithm. Comparison was made between models developed using the two algorithms and this study shows that Cuckoo Search Algorithm is superior than Recursive Least Square Algorithm based on Mean Square error (MSE). RLS developed models MSE are 5.6321×10−5,0.0018,0.0129 & 0.0078e for hub angle 1, hub angle 2, deflection 1 and deflection 2 respectively. CSA developed models MSE are 2.7164×10−5,1.1546×10−5,6.0404×10−4 & 0.0026 respectively. Correlation tests showed that the hub angle models are biased, while the deflection models are unbiased for both algorithms. Finally, controllers intelligently tuned by Cuckoo search optimization algorithm were introduced to control the hub angle position and the endpoint vibrations. The rise time and maximum overshoot are 0.5 seconds and 0 rad for hub angle 1 and 0.5 seconds and 0.2 rad for hub angle 2. The setting time and maximum overshoot are 2 seconds and 0.01 rad for deflection 1 and 2 seconds and 0.007 rad for deflection 2

Learning Throttle Valve Control Using Policy Search

Abstract. The throttle valve is a technical device used for regulating a fluid or a gas flow. Throttle valve control is a challenging task, due to its complex dynamics and demanding constraints for the controller. Using state-of-the-art throttle valve control, such as model-free PID controllers, time-consuming and manual adjusting of the controller is necessary. In this paper, we investigate how reinforcement learning (RL) can help to alleviate the effort of manual controller design by automatically learning a control policy from experiences. In order to obtain a valid control policy for the throttle valve, several constraints need to be addressed, such as no-overshoot. Furthermore, the learned controller must be able to follow given desired trajectories, while moving the valve from any start to any goal position and, thus, multi-targets policy learning needs to be considered for RL. In this study, we employ a policy search RL approach, Pilco [2], to learn a throttle valve control policy. We adapt the Pilco algorithm, while taking into account the practical requirements and constraints for the controller. For evaluation, we employ the resulting algorithm to solve several control tasks in simulation, as well as on a physical throttle valve system. The results show that policy search RL is able to learn a consistent control policy for complex, real-world systems.