Optimization of Sliding Mode Control to Save Energy in a SCARA Robot

Sliding mode control is a robust technique that is used to overcome difficulties such as parameter variations, unmodeled dynamics, external disturbances, and payload changes in the position-tracking problem regarding robots. However, the selection of the gains in the controller could produce bigger forces than are required to move the robots, which requires spending a large amount of energy. In the literature, several approaches were used to manage these features, but some proposals are complex and require tuning the gains. In this work, a sliding mode controller was designed and optimized in order to save energy in the position-tracking problem of a two-degree-of-freedom SCARA robot. The sliding mode controller gains were optimized usinga Bat algorithm to save energy by minimizing the forces. Finally, two controllers were designed and implemented in the simulation, and as a result, adequate controller gains were found that saved energy by minimizing the forces.

Output-Feedback Position Tracking Servo System with Feedback Gain Learning Mechanism via Order-Reduction Speed-Error-Stabilization Approach

This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of observers without system parameter information for estimating the position reference derivative and speed and acceleration errors and (b) an order reduction exponential speed error stabilizer via active damping injection to enable the application of a feedback-gain-learning position-tracking action. A hardware configuration using a QUBE-servo2 and myRIO-1900 experimentally validates the closed-loop improvement under various scenarios.

A Driver and Control Method for Primary Stator Discontinuous Segmented-PMLSM

In recent years, with the development of the permanent magnet linear synchronous motor (PMLSM), the application of PMLSM has not been limited only to the high-end equipment field; the primary stator discontinuous segmented-PMLSM (DSPMLSM), which consists of multiple primary stators and one mover, has also been applied in long-distance transportation systems, such as electromagnetic launch, high precision material transport, etc. Compared with the symmetry phase parameters of conventional PMLSM, the stationary electrical parameters vary when the mover enters and leaves the primary stators (the inter-segment region). At the same time, due to the sectional power supply, there will be primary suction or pulling force when the mover enters and exits the inter-segment region, which will lead to large thrust fluctuation and result in lager position error. This paper proposed a related drive and control strategy about the DSPMLSM system, which improved the position tracking accuracy during the full range of DSPMLSM. First, the parameter variation between stator segments has been analyzed through finite element simulation of DSPMLSM. Then, a double closed-loop series control structure of position-current is designed, in which a PI-Lead controller was adopted for the position loop and a PI controller was adopted for the current loop. In order to improve the position tracking accuracy of DSPMLSM, a thrust fluctuation extended state observer (TFESO) was adopted to observe and compensate the complex thrust disturbances such as cogging force, friction and other unmodeled thrust fluctuation. At last, the DSPMLSM experimental stage was established, and the experimental results show that the proposed driver and control theory can effectively improve the position tracking accuracy of the whole stroke of DSPMLSM.

Nonlinear Optimal Position Control with Observer for Position Tracking of Surfaced Mounded Permanent Magnet Synchronous Motors

Previous control methods were designed based on cascade structure and consist of position and current controllers for permanent magnet-synchronous motors (PMSMs). Thus, the structures of the previous methods are necessarily complex although the stability is guaranteed. Thus, the gain tuning is difficult to obtain for the desired control performance for the PMSMs. To overcome this problem, this paper proposes a nonlinear optimal position control method with an observer to improve the position tracking performance of PMSMs. The proposed method consists of a desired state generator, controller, and nonlinear observer. The desired states and inputs are derived using the PMSM model. Then, the state feedback controller is designed based on the whole tracking error dynamics including both mechanical and electrical dynamics. The nonlinear observer is designed to estimate the velocity and load torque. The closed-loop stability is proven using the input-to-state stability. The proposed method is not designed based on the cascade structure. Furthermore, the control and observer gains are chosen using an optimal control method to obtain the desired performance for the PMSMs. This approach simplifies the design process such that the control algorithm is suitable for real-time control. The performance of the proposed method is validated via simulations and experiments.

A robust bitmap-based real-time position tracking algorithm for rats in radial arm maze tests

This paper aims to develop a position tracking algorithm by which a rat in a radial arm maze can be accurately located in real time. An infrared (IR) night-vision camera was hung above the maze to capture IR images of the rat. The IR images were binarized and then duplicated for subsequent intersection and opening operations. Due to simple operations and a high robustness against the noise spots formed by the droppings of the rat, it took just minutes to process more than 9000 frames, and an accuracy above 99% was reached as well. The maze was intruded by an experimenter to further test the robustness, and the accuracy slightly fell to 98%. For comparison purposes, the same experiments were carried out using a pre-trained YOLO v2 model. The YOLO counterpart gave an accuracy beyond 97% in the absence and in the presence of the intruder. In other words, this work slightly outperformed the YOLO counterpart in terms of the accuracy in both cases, which indicates the robustness of this work. However, it took the YOLO counterpart an hour or so to locate a rat contained in the frames, which highlights the contribution of this work.