This paper presents the high-precision lateral positioning of a piezoelectric tube scanner (PTS) used in an atomic force microscope (AFM). The operation of the PTS is affected by various nonlinearities depending upon its operating conditions. An internal reference model-based optimal linear quadratic Gaussian (LQG) controller with a vibration compensator is designed and implemented on the AFM to reduce creep, hysteresis, induced vibration, and cross coupling. This proposed controller has integral action on the error state which makes it possible to track the reference signal and the vibration compensator achieves significant damping of the resonant modes of the PTS in the X- and Y-axes. It also compensates the cross coupling between the X-Y axes dynamics of the AFM system, reducing the artifacts instigated by the system dynamic behavior at high scan rates. The closed-loop frequency responses for both the axes have a high bandwidth. The experimental results are presented which demonstrate the efficacy of the proposed method.

In this research, we proposed a stereo visual simultaneous localisation and mapping (SLAM) system that efficiently works in agricultural scenarios without compromising the performance and accuracy in contrast to the other state-of-the-art methods. The proposed system is equipped with an image enhancement technique for the ORB point and LSD line features recovery, which enables it to work in broader scenarios and gives extensive spatial information from the low-light and hazy agricultural environment. Firstly, the method has been tested on the standard dataset, i.e., KITTI and EuRoC, to validate the localisation accuracy by comparing it with the other state-of-the-art methods, namely VINS-SLAM, PL-SLAM, and ORB-SLAM2. The experimental results evidence that the proposed method obtains superior localisation and mapping accuracy than the other visual SLAM methods. Secondly, the proposed method is tested on the ROSARIO dataset, our low-light agricultural dataset, and O-HAZE dataset to validate the performance in agricultural environments. In such cases, while other methods fail to operate in such complex agricultural environments, our method successfully operates with high localisation and mapping accuracy.

This paper presents the design of an internal reference model-based optimal linear quadratic Gaussian (LQG) controller for the lateral positioning of a piezoelectric tube actuator (PTA) used in an atomic force microscope (AFM). In this control design, internal modeling of the reference signal and system error are considered. As a result, the steady-state tracking error is minimized. In addition to the LQG controller, a vibration compensator is incorporated with the plant to suppress the vibration of the PTA at the resonance frequency. It achieves a high closed-loop bandwidth and significant damping of the resonant mode of the PTA, which enables a reference triangular signal to be tracked. Comparison of performance of the optimal LQG controller augmented with a vibration compensator and a PI controller demonstrates that the proposed controller shows significant improvements over the existing AFM PI controller.

In this paper, a longitudinal and lateral control system of an autonomous vehicle is presented by developing a novel hybrid trajectory tracking algorithm. In this proposed method, the longitudinal control system is developed based on the curvature information of the reference path. The autonomous vehicle modifies the desired speed according to the estimated size and types of the reference trajectory curves. This desired speed is integrated into the PID controller to maintain an optimal speed of the vehicle while following the given path. The lateral control system is designed based on feedforward (preview control) and feedback (LQR) controllers to reduce lateral errors between the trajectory and autonomous vehicle. The feedforward and the feedback controllers generate precise steering angles to eliminate orientation and lateral errors caused by the curvature of the trajectory and external disturbances. The effectiveness of the proposed method is evaluated by comparing simulation and experimental results with different trajectory tracking algorithms on simulated and experimented paths. It is proven that the proposed algorithm is capable of significantly minimizing lateral errors on sharp curves compared to other path tracking methods.