Ship RBF neural network sliding mode PID heading control

In view of the inherent non-linearity, complexity, susceptibility to external wind, wave, and current interference of under-driven ships, and the difficulty of adjusting and adjusting control parameters, to improve the performance of ship’s autopilot, a kind of RBF neural network sliding mode variable structure PID controller is designed. Traditional PID control is sensitive to parameter changes, online tuning is difficult, and easy to overshoot. In order to solve this problem, combining the variable structure characteristics of PID, a differential compensation term is added to the integral term to convert the PID control parameters into three parameters with more obvious physical meanings, and then combined with the RBF neural network learning and identification function to realize online tuning and adaptive control of ship control parameters. Using MATLAB software to simulate the container ship “MV KOTA SEGAR” MMG model shows that the designed RBF neural network sliding mode PID controller can effectively eliminate the ship’s lateral deviation caused by external interference such as wind, waves, currents, etc., with high control accuracy,robustness and strong adaptability.

Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains

One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Logic strategy for online tuning of the PID gains of the UAV motion controller. A Cascaded-PID scheme is proposed, in which velocity commands are calculated and sent to the flight control unit from a given target desired position (waypoint). Therefore, the flight control unit is responsible for the lower control loop. The main advantage of the proposed method is that it can be applied to any UAV without the need of its formal mathematical model. Robot Operating System (ROS) is used to integrate the proposed system and the flight control unit. The solution was evaluated through flight tests and simulations, which were conducted using Unreal Engine 4 with the Microsoft AirSim plugin. In the simulations, the proposed method is compared with the traditional Ziegler-Nichols tuning method, another Fuzzy Logic approach, and the ArduPilot built-in PID controller. The simulation results show that the proposed method, compared to the ArduPilot controller, drives the UAV to reach the desired setpoint faster. When compared to Ziegler-Nichols and another different Fuzzy Logic approach, the proposed method demonstrates to provide a faster accommodation and yield smaller errors amplitudes.

A Frequency Adaptive Scheme Based on Newton Structure of PRRC for LCL-Type Inverter Connected with Weak Grid

Repetitive control (RC) is gradually used in inverters tied with weak grid. To achieve the zero steady-state error tracking of inverter current and compensate the harmonic distortion caused by frequency fluctuation, a frequency adaptive (FA) control scheme for LCL-type inverter connected with weak grid is proposed. This scheme adopts a proportional resonance (PR) controller in parallel with RC (PRRC) to overcome the disadvantages caused by RC inherent one-cycle time delay. A fractional delay (FD) filter based on the Newton structure is proposed to approximate the fraction item of fs/f, where fs and f are sample frequency and grid frequency, respectively. The structure of the proposed FD filter is relatively simple; moreover, coefficients of the filter maintain constant so as not to need online tuning even when grid frequency fluctuates, which decreases the computational burden considerably. The feasibility and effectiveness of the proposed FA control scheme, named as Newton-FAPRRC, are all verified by the simulation and experimental results.

Compliant Detachment of Wall-Climbing Robot Unaffected by Adhesion State

Adhesion state is a key factor affecting the motion stability of a wall-climbing robot. According to different adhesion states, there is no universal method for compliant detachment. We propose an online impedance strategy for controlling peeling angle to realize compliant movement. Variable compliant motions are achieved by online tuning the stiffness and damping parameters of proportional-derivative control, which realizes compliant detachment with a peeling angle of π, the adhesion strength to adjust to a minimum and basically eliminated the instant change in normal adhesion strength at the detachment end state. The proposed controller was validated using a vertical climbing robot. The results showed that, with the proposed controller, the sudden change in the normal adhesion force during peeling was significantly reduced. Besides, there is no correlation between the sudden change in the normal adhesion force at the detachment end state and the adhesion state. Regardless of the adhesion states, the compliant detachment can be accomplished reliably.

Capacitor Voltage Balancing with Online Controller Performance-Based Tuning for a Switch-Sharing-Based Multilevel Inverter

Switch-sharing-based multilevel inverters offer great advantages in terms of efficiency improvement and output quality enhancement for low-power photovoltaic (PV) applications. However, the capacitor voltage balancing issue may critically deteriorate the output voltages, and thus could nullify the aforementioned benefits. Hence, this paper proposes a capacitor voltage balancing solution based on a buck-boost converter with performance-based tuning controllers to address the issue. The proposed controllers are designed based on the proportional-integral (PI) configuration equipped with an online tuning mechanism. The main purpose of the online tuning mechanism is to fine tune the proportional gain according to the DC input voltage, based on the measured output current total harmonic distortion (THD) performance of the inverter, while at the same time preventing the controller from reaching a state of saturation. By using the actual measurement of current THD performance, online tuning accuracy can be improved since no ideal condition is assumed, and thus, the associated error can be minimized. Simulation and experimental results reveal that the capacitor voltages can be balanced at high modulation indexes with improved current harmonic performance able to be be acquired at the inverter’s output.