The main problem of wheeled soccer robots is the low level controller gains regulation particularly in competition. The low level control task is tracking the desired angular velocities of the robot wheels which are generated by the high level controller. Since the robot's model and environment have many uncertainties, traditional controller gains must be adjusted before every match along the competition. In this paper, a linear quadratic tracking (LQT) scheme is designed to solve this problem. The controller can regulate the parameters on-line by policy iteration reinforcement learning algorithm. The output paths of the four-wheeled soccer robot with the adaptive LQT are compared with traditional LQT and the results show that the proposed method can provide superior performance in presence of uncertainties and nonlinearities.

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In this paper, a self-adaptive PD (SAPD) is employed for motion control of omni-directional robots. The method contains a PD controller that can be tuned online using a fuzzy logic system (FLS). Fast and accurate positioning is one of significant challenges in robot platforms. In addition, some uncertainties have adverse effects on traditional control system's performance during the robot's motion. Slow responses, low accuracy and instability are the most important drawbacks of widespread controllers in presence of uncertain dynamics. Since the fuzzy algorithm can deal with uncertainties and nonlinearities, the proposed method can tackle the mentioned problems. The controller is designed based on an uncertain model and implemented on a four wheeled omni-directional fast robot. The novelty of this article is proposing an enhanced version of well-known gain scheduling PD controller to improve positioning performance of the robot in different circumstances. Experimental results show that the method can provide a desirable performance in the presence of uncertainties.

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In this paper, online policy iteration reinforcement learning (RL) algorithm is proposed for motion control of four wheeled omni-directional robots. The algorithm solves the linear quadratic tracking (LQT) problem in an online manner using real-time measurement data of the robot. This property enables the tracking controller to compensate the alterations of dynamics of the robot's model and environment. The online policy iteration based tracking method is employed as low level controller. On the other side, a proportional derivative (PD) scheme is performed as supervisory planning system (high level controller). In this study, the followed paths of online and offline policy iteration algorithms are compared in a rectangular trajectory in the presence of slippage drawback and motor heat. Simulation and implementation results of the methods demonstrate the effectiveness of the online algorithm compared to offline one in reducing the command trajectory tracking error and robot's path deviations. Besides, the proposed online controller shows a considerable ability in learning appropriate control policy on different types of surfaces. The novelty of this paper is proposition of a simple-structure learning based adaptive optimal scheme that tracks the desired path, optimizes the energy consumption, and solves the uncertainty problem in omni-directional wheeled robots.