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This paper develops a kinematic path-tracking algorithm for a nonholonomic mobile robot using an improved iterative learning control (I-ILC) technique. The proposed algorithm produces a velocity command to the wheeled robot, in addition, the state disturbances and measurement noises are taken into consideration. In the learning rule, an adjustable parameter is defined and by adjusting this parameter we can obtain the best learning rule. From the simulation results we find that the system outputs and control inputs are bounded to converge to the desired values. The MATLAB software is used for simulation to verify the feasibility and validity of the proposed learning algorithm.

In this paper, a novel electromagnetic micropositioner is designed from an orthogonal 3-P(4S) parallel mechanism through the substitution method and modular design techniques. Preliminary prototype experiments show that the micropositioner possesses an excellent decoupling performance. Thus an independent control strategy is carried out for the motion control of the micropositioner. An RBF neural networks based adaptive backstepping terminal sliding mode controller is designed according to the nonlinearity characteristics of the actuator. Parameters of the system are identified with a genetic algorithm. Finally, the performances of the micropositioner and the developed control strategy are verified. Experimental results demonstrate that satisfactory performances can be achieved.

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