This work presents a novel 'Constant in gain Lead in phase' (CgLp) element using nonlinear reset technique. PID is the industrial workhorse even to this day in high-tech precision positioning applications. However, Bode's gain phase relationship and waterbed effect fundamentally limit performance of PID and other linear controllers. This paper presents CgLp as a controlled nonlinear element which can be introduced within the framework of PID allowing for wide applicability and overcoming linear control limitations. Design of CgLp with generalized first order reset element (GFORE) and generalized second order reset element (GSORE) (introduced in this work) is presented using describing function analysis. A more detailed analysis of reset elements in frequency domain compared to existing literature is first carried out for this purpose. Finally, CgLp is integrated with PID and tested on one of the DOFs of a planar precision positioning stage. Performance improvement is shown in terms of tracking, steady-state precision and bandwidth.

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The paper presents a new controller inspired by the human experience based, voluntary body action control (dubbed motor control) learning mechanism. The controller is called Experience Mapping based Prediction Controller (EMPC). EMPC is designed with auto-learning features without the need for the plant model. The core of the controller is formed around the motor action prediction-control mechanism of humans based on past experiential learning with the ability to adapt to environmental changes intelligently. EMPC is utilized for high precision position control of DC motors. The simulation results are presented to show that accurate position control is achieved using EMPC for step and dynamic demands. The performance of EMPC is compared with conventional PD controller and MRAC based position controller under different system conditions. Position Control using EMPC is practically implemented and the results are presented.

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