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Kamil, Haider
Languages: English
Types: Doctoral thesis
Subjects: TA
The aim of this study is to understand the complexity and control challenges of the locomotion of a three-link mechanism of a robot system. In order to do this a three-link robot gymnast (Robogymnast) has been built in Cardiff University. The Robogymnast is composed of three links (one arm, one torso, one leg) and is powered by two geared DC motors. Currently the robot has three potentiometers to measure the relative angles between adjacent links and only one tachometer to measure the relative angular position \ud of the first link. \ud A mathematical model for the robot is derived using Lagrange equations. Since the model is inherently nonlinear and multivariate, it presents more challenges when modelling the Robogymnast and dealing with control motion problems. The proposed approach for dealing with the design of the control system is based on a discrete-time linear model around the upright position of the Robogymnast. \ud To study the swinging motion of the Robogymnast, a new technique is proposed to manipulate the frequency and the amplitude of the sinusoidal signals as a means of controlling the motors. Due to the many combinations of the frequency and amplitude, an optimisation method is required to find the optimal set. The Bees Algorithm (BA), a novel swarm-based optimisation technique, is used to enhance the performance of the swinging motion through optimisation of the manipulated parameters of the control actions. The time taken to reach the upright position at its best is 128 seconds. \ud Two different control methods are adopted to study the balancing/stablising of the Robogymnast in both the downward and upright configurations. The first is the optimal control algorithm using the Linear Quadratic Regulator (LQR) technique with \ud integrators to help achieve and maintain the set of reference trajectories. The second is a combination of Local Control (LC) and LQR. Each controller is implemented via reduced order state observer to estimate the unmeasured states in terms of their relative angular velocities. \ud From the identified data in the relative angular positions of the upright balancing control, it is reported that the maximum amplitude of the deviation in the relative angles on average are approximately 7.5° for the first link and 18° for the second link. It is noted that the third link deviated approximately by 2.5° using only the LQR controller, and no significant deviation when using the LQR with LC. \ud To explore the combination between swinging and balancing motions, a switching mechanism between swinging and balancing algorithm is proposed. This is achieved by dividing the controller into three stages. The first stage is the swinging control, the next stage is the transition control which is accomplished using the Independent Joint Control (IJC) technique and finally balancing control is achieved by the LQR. The duration time of the transition controller to track the reference trajectory of the Robogymnast at its best is found to be within 0.4 seconds. An external disturbance is applied to each link of the Robogymnast separately in order to study the controller's ability to overcome the disturbance and to study the controller response. \ud The simulation of the Robogymnast and experimental realization of the controllers are \ud implemented using MATLAB® software and the C++ program environment \ud respectively.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Anderson, B. and Moore, J. 1990. Optimal control: linear quadratic methods. PrenticeHall Inc., London, UK.
    • Farmanbordar, A., Zaeri, N. and Rahimi, S. 2011. Stabilizing a Driven Pendulum Using DLQR Control. Modelling Symposium (AMS), 2011 Fifth Asia. IEEE, 2011., pp. 123- 126.
    • Yadav, S., Sharma, S. and Singh, M. 2012. Optimal Control of double inverted pendulum using LQR controller. International Journal of Advanced Research in Computer Science and Software Engineering 2(2), pp. 189-192.
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  • Discovered through pilot similarity algorithms. Send us your feedback.

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