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Intelligent Adaptive Motion Control for Ball Robots Using Digital Signal Controllers
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This dissertation presents intelligent adaptive controllers using recurrent-interval-type-II-fuzzy-neural-networks (RIT2FNN) and their hardware implementations for station keeping, position control and trajectory tracking of two kinds of ball robots, ballbot and ball-riding robot. For the purpose of achieving the control goals for each robotic system, nonlinear model of the robotic system with parameter variations modeling uncertainties and other non-linear characteristics are decomposed into nominal and unmodeling parts, two RIT2FNN based intelligent adaptive controllers are proposed by using backstepping techniques and hierarchical aggregated sliding-model control approaches, and their performance and robustness properties are well investigated. The two RIT2FNN-based controllers fall into two categories: nonlinear control augmented with the RIT2FNN, and direct adaptive RIT2FNN-based control. Simulations and experimental results are conducted on the experimental ballbot and ball-riding robots, which are respectively equipped with a new driving mechanism and a control system; the control system is composed of a digital signal controllers, one tilt sensor and one rate gyro with interfacing circuits, and DC servomotors driving modules. The comparative studies via simulations show that the proposed controllers outperform other existing controllers. Experimental results exemplify the performance and applicability of the proposed controllers together with the built robotic systems.
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