Adaptive Integral Sliding Mode Based Course Keeping Control of Unmanned Surface Vehicle

This paper investigates the course keeping control problem for an unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

Adaptive Nonsingular Fast Terminal Sliding Mode Control for Maximum Power Point Tracking of a WECS-PMSG

This paper investigates maximum power extraction from a wind-energy-conversion system (WECS) with a permanent magnet synchronous generator (PMSG) operating in standalone mode. This was achieved by designing a robust adaptive nonsingular fast terminal sliding mode control (ANFTSMC) for the WECS-PMSG. The proposed scheme guaranteed optimal power generation and suppressed the system uncertainties with a rapid convergence rate. Moreover, it is independent of the upper bounds of the system uncertainties as an online adjustment algorithm was utilized to estimate and compensate them. Finally, four case studies were carried out, which manifested the remarkable performance of ANFTSMC in comparison to previous methods reported in the literature.

Adaptive Integral Sliding Mode based Path Following Control of Unmanned Surface Vehicle

This paper investigates the path following control problem for a unmanned surface vehicle (USV) in the presence of unknown disturbances and system uncertainties. The simulation study combines two different types of sliding mode surface based control approaches due to its precise tracking and robustness against disturbances and uncertainty. Firstly, an adaptive linear sliding mode surface algorithm is applied, to keep the yaw error within the desired boundaries and then an adaptive integral non-linear sliding mode surface is explored to keep an account of the sliding mode condition. Additionally, a method to reconfigure the input parameters in order to keep settling time, yaw rate restriction and desired precision within boundary conditions is presented. The main strengths of proposed approach is simplicity, robustness with respect to external disturbances and high adaptability to static and dynamics reference courses without the need of parameter reconfiguration.

Nonlinear state feedback controller combined with RBF for nonlinear underactuated overhead crane system

This paper proposes a robust control scheme for an underactuated crane system. The presented scheme contains two control strategies, feedback control term and corrective control term, based on Radial Basis Function (RBF) neural networks. A feedback control term is deigned based on the nominal dynamic model of the controlled system. RBF neural networks have been used as adaptive control term to compensate for the system uncertainties and external disturbance. Lyapunov stability theorem has been used to derive updating laws for the weights of the RBF neural networks. To illustrate the robustness and effectiveness of the proposed controller, Matlab program is used to simulate the model of the nonlinear overhead crane system with the proposed control method, taking into account system uncertainties and external disturbance. Simulation results indicated superior control performance of the proposed control method compared to the other control methods used in the test.

Uncertainty of Integral System Safety in Enginering

The probabilistic safety analysis evaluates system reliability and failure probability by using statistics and probability theory but it cannot estimate the system uncertainties due to variabilities of system state probabilities. The article firstly resumes how the information entropy expresses the probabilistic uncertainties due to unevenness of probability distributions of system states. Next it argues that the conditional entropy with respect to system operational and failure states appropriately describes system redundancy and robustness, respectively. Finally the article concludes that the joint probabilistic uncertainties of reliability, redundancy and robustness defines the integral system safety. The concept of integral system safety allows more comprehensive definitions of favorable system functional properties, configuration evaluation, optimization and decision making in engineering.

Statistical Analysis for Transmission Error of Gear System with Mechanical and Thermal Deformation Uncertainties

We establish a robust algorithm to analyze the influence of system uncertainties on the transmission error of a spur gear pair under 2D simplification. The algorithm provides a way of generating smooth cutter profiles with machining uncertainties and measuring the thermal deformation through the uncertainties in material properties. Then, it produces realizations of gear tooth profiles based on the analytical method for accuracy and computational efficiency. Numerical investigations show the statistical analysis on the tooth contact analysis by comparing steel and plastic gears. It is worthwhile remarking that the plastic gear is susceptible to the geometric error caused by thermal deformation. Moreover, although the impact of thermal deformation on steel gear may seem slim, it can have a noticeable influence when it exists with mechanical uncertainties together.

A Robust Noise-Free Linear Control Design for Robot Manipulator with Uncertain System Parameters

In robot control, the sliding mode control is known for its robustness against external disturbances and system uncertainties. However, it has the disadvantage of control chattering, which can damage the actuator and degrade system performance. With a new stability proof, this paper presents an alternative simple linear feedback control that can cope with large system uncertainties and suppress large external disturbances, doing so as effectively as sliding mode control does. The advantage of using linear control is that the control law is simple and control chattering can be avoided. Moreover, a noise-free control scheme is proposed as an improvement of the feedback control; the modified design preserves the advantages of linear control and generates a chattering-free control signal even in a noisy environment.

System Characterization and Adaptive Tracking Control of Quadrotors under Multiple Operating Conditions

This paper presents an adaptive controller design framework with input compensation for quadrotor systems, which deals with different system operating conditions with a uniform update law for the controller parameters. The motivation of the work is to handle the situation that existing adaptive control schemes are either restricted to the system equilibrium as the hover condition or unable to deal with the diverse system uncertainties which cause system interactor matrix and high-frequency gain matrix to change. An adaptive control scheme equipped with an input compensator is constructed to make the system to have a uniform interactor matrix and a consistent pattern of the gain matrix signs over different operating conditions, which are key prior design conditions for model reference adaptive control applied to quadrotor systems. To deal with the uncertain system high-frequency gain matrix, a gain matrix decomposition technique is employed to parametrize an error system model in terms of the gain parameters and tracking errors, for the design of an adaptive parameter update law with reduced system knowledge. It is ensured that all closed-loop system signals are bounded, and the system output tracks a reference output asymptotically despite the system parameter uncertainties and the uncertain offsets at non-equilibrium operating conditions. The proposed scheme expands the capacity of adaptive control for quadrotors to operate at multiple operating conditions in the presence of system uncertainties. Simulation results of a quadrotor with the proposed adaptive control scheme are presented to show the desired system performance.