Performance of first and second-order sliding mode observers for nonlinear systems

This paper presents a brief study on the design and performance comparison of conventional first-order and super-twisting second-order sliding mode observers for some nonlinear control systems. Estimation accuracy, fast response, chattering effect, peaking phenomenon and robustness are considered for nonlinear ystems under observer-based output feedback control and state feedback control.

Adaptive intelligent systems for energy management of vehicles

The thesis demonstrated the architecture of adaptive intelligent systems for energy management that is capable of interacting with complex systems including the vehicle, environment, and driver components, as well as the interrelationships between these variables, to deliver fuel consumption improvements.

Estimator-based sliding mode control strategy design

This study addresses the design and properties of serial sliding mode control (SMC) systems for an induction servo motor drive to track periodic commands. It contains a SMC, an adaptive SMC (ASMC) and an estimator-based SMC (ESMC). The effectiveness of the proposed control systems is verifi ed by numerical simulations, and the superiority of the ESMC system is indicated in comparison with the SMC and ASMC systems.

Underwater swarm robotics consensus control

The control of a swarm of underwater robots requires more than just a control algorithm, it requires a communications system. Underwater communications is difficult at the best of times and so large time delays and minimal information is a concern. The control system must be able to work on minimal and out of date information. The control system must also be able to control a large number of robots without a master control, a decentralized control approach. This paper describes one such control method.

ε-bounded state estimation for time-delay systems with bounded disturbances

A new problem on ε-bounded functional state estimation for time-delay systems with unknown bounded disturbances is studied in this paper. In the presence of unknown bounded disturbances, the common assumption regarding the observers matching condition is no longer required. In this regard, instead of achieving asymptotic convergence for the observer error, the error is now required to converge exponentially within a ball with a small radius ε > 0. This means that the estimate converges exponentially within an ε-bound of the true value. A general observer that utilises multiple-delayed output and input information is proposed. Sufficient conditions for the existence of the proposed observer are first given. We then employ an extended Lyapunov-Krasovskii functional which combines the delay-decomposition technique with a triple-integral term to study the ε-convergence problem of the observer error system. Moreover, the obtained results are shown to be more effective than the existing results for the cases with no disturbances and/or no time delay. Three numerical examples are given to illustrate the obtained results.

Swing-up and stability control of Wheeled Acrobot (WAcrobot)

In this paper the Wheeled Acrobot (WAcrobot), a novel mechanical system consisting of an underactuated double inverted pendulum robot (Acrobot) equipped with actuated wheels, is described. This underactuated and highly nonlinear system has potential applications in mobile manipulators and leg-wheeled robots. It is also a testbed for researchers studying advanced methodologies in nonlinear control. The control system for swing-up of the WAcrobot based on collocated or non-collocated feedback linearisation to linearise the active or passive Degree Of Freedom (DOF) followed by Linear Quadratic Regulator (LQR) to stabilise the robot is discussed. The effectiveness of the proposed scheme is validated with numerical simulation. The numerical results are visualised by graphical simulation to demonstrate the correlation between the numerical results and the WAcrobot physical response.

A sliding-mode-control-based adaptive cruise controller

This paper presents an alternative solution to the conventional cruise controller of a hybrid electric vehicle based on the sliding mode control approach. The mathematical model of a hybrid electric vehicle cruise control system is developed. Then, the sliding mode control approach is applied as the controller. The sliding mode control stability is investigated and demonstrated. Thereafter, the system is simulated and the results are presented. © 2014 IEEE.

Global finite-time stabilisation for a class of stochastic nonlinear systems by output feedback

In this paper, the problem of global finite-time stabilisation by output feedback is considered for a class of stochastic nonlinear systems. First, based on homogeneous systems theory and the adding a power integrator technique, a homogeneous reduced order observer and control law are constructed in a recursive manner for the nominal system. Then, the homogeneous domination approach is used to deal with the nonlinearities in drift and diffusion terms; it is shown that the proposed output-feedback control law can guarantee that the closed-loop system is global finite-time stable in probability. Finally, simulation examples are carried out to demonstrate the effectiveness of the proposed control scheme.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.

Cyber security and protection of ICS systems: An australian example

Many aspects of our modern society now have either a direct or implicit dependence upon information technology. As such, a compromise of the availability or integrity in relation to these systems (which may encompass such diverse domains as banking, government, health care, and law enforcement) could have dramatic consequences from a societal perspective. These key systems are often referred to as critical infrastructure. Critical infrastructure can consist of corporate information systems or systems that control key industrial processes; these specific systems are referred to as ICS (Industry Control Systems) systems. ICS systems have devolved since the 1960s from standalone systems to networked architectures that communicate across large distances, utilise wireless network and can be controlled via the Internet. ICS systems form part of many countries’ key critical infrastructure, including Australia. They are used to remotely monitor and control the delivery of essential services and products, such as electricity, gas, water, waste treatment and transport systems. The need for security measures within these systems was not anticipated in the early development stages as they were designed to be closed systems and not open systems to be accessible via the Internet. We are also seeing these ICS and their supporting systems being integrated into organisational corporate systems.

Incorporating human perception with the motion washout filter using fuzzy logic control

Abstract—Nowadays, classical washout filters are extensively used in commercial motion simulators. Even though there are several advantages for classical washout filters, such as short processing time, simplicity and ease of adjustment, they have several shortcomings. The main disadvantage is the fixed scheme and parameters of the classical washout filter cause inflexibility of the structure and thus the resulting simulator fails to suit all circumstances. Moreover, it is a conservative approach and the platform cannot be fully exploited. The aim of this research is to present a fuzzy logic approach and take the human perception error into account in the classical motion cueing algorithm, in order to improve both the physical limits of restitution and realistic human sensations. The fuzzy compensator signal is applied to adjust the filtered signals on the longitudinal and rotational channels online, as well as the tilt coordination to minimize the vestibular sensation error below the human perception threshold. The results indicate that the proposed fuzzy logic controllers significantly minimize the drawbacks of having fixed parameters and conservativeness in the classical washout filter. In addition, the performance of motion cueing algorithm and human perception for most occasions is improved.