Progressive edge-sensing dynamic scene deblurring

Deblurring images of dynamic scenes is a challenging task because blurring occurs due to a combination of many factors. In recent years, the use of multi-scale pyramid methods to recover high-resolution sharp images has been extensively studied. We have made improvements to the lack of detail recovery in the cascade structure through a network using progressive integration of data streams. Our new multi-scale structure and edge feature perception design deals with changes in blurring at different spatial scales and enhances the sensitivity of the network to blurred edges. The coarse-to-fine architecture restores the image structure, first performing global adjustments, and then performing local refinement. In this way, not only is global correlation considered, but also residual information is used to significantly improve image restoration and enhance texture details. Experimental results show quantitative and qualitative improvements over existing methods.

POWER COMPONENTS ESTIMATION ACCORDING TO IEEE STANDARD 1459-2010

In this paper, the design and implementation of a novel recursive method for the power measurement according to the IEEE Standard 1459–2010 have been described. The most important parts are adaptive band- and low-pass FIR filters that extract fundamental and dc components, respectively. In addition, by using oversampling techniques and decimation filters, coefficient sensitivity problems of the large-order FIR comb cascade structure are overridden and the parameter estimation accuracy is improved. The symmetrical components are estimated through transformation matrix of adaptive phase shifters. The effectiveness of the proposed techniques is demonstrated by simulation results.

A Novel Family of Exact Nonlinear Cascade Control Design Solutions for a Class of UAV Systems

In this work, a novel family of exact nonlinear control laws is developed for trajectory tracking of unmanned aerial vehicles. The proposed methodology exploits the cascade structure of the dynamic equations of most of these systems. In a first step, the vehicle position in Cartesian coordinates is controlled by means of fictitious inputs corresponding to the angular coordinates, which are fixed to a combination of computed torque and proportional-derivative elements. In a second step, the angular coordinates are controlled as to drive them to the desired fictitious inputs necessary for the first part, resulting in a double-integrator 3-input cascade control scheme. The proposal is put at test in two examples: 4-rotor and 8-rotor aircrafts. Numerical simulations of both plants illustrate the effectiveness of the proposed method, while real-time results of the first one confirm its applicability.

Integrated Lateral and Longitudinal Control with Optimization-Based Allocation Strategy for Autonomous Electric Vehicles

This paper presents a novel approach for path-following control of a four-wheeled autonomous vehicle. The rear wheels of the vehicle are driven independently, all four wheels can be braked independently, and the front wheels are steered together. The proposed cascade structure consists of two convex optimization-based parts: one for path-following and another for the control allocation problem of the actuators. The control algorithm presents cost functions for the allocation problem focusing on safety. The proposed cost functions were examined and compared to former ones in a simulation environment. After all, the controller was tested in real-time test on a Lotus Evora test vehicle developed by ThyssenKrupp.

Asymptotic stabilization of underactuated surface vehicles with actuator saturation

This paper investigates the problem of global asymptotic stabilization of underactuated surface vessels (USVs) with input saturation. A novel input transformation is presented, so that the USV system can be transformed to a cascade structure. For the obtained system, the improved fractional power control laws are proposed to ensure input signals do not exceed actuator constraints and enhance convergence rates. Finally, stabilization and parameter optimization algorithm of USVs are proposed. Simulations are given to demonstrate the effectiveness of the presented method.

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.