Wenchuan Cai

Indirect Robust Adaptive Fault -Tolerant Control for Attitude Tracking of Spacecraft

Reliable and cost-effective control of spacecraft should account for modeling uncertainties, unexpected disturbances, subsystem failures, and limited resources simultaneously. This paper presents an indirect (nonregressor-based) approach to attitude tracking control of spacecraft. It is shown that the control algorithms developed are not only robust against external disturbances and adaptive to unknown and time-varying mass/inertia properties, but also able to accommodate actuator failures under limited thrusts. All are achieved with inexpensive online computations (a feature of practical importance in reducing the usage of onboard resources in terms of computing power and memory size). Furthermore, this method is user/designer friendly in that it does not involve a time-consuming design procedure and demands little redesigning or reprogramming during vehicle operation. The benefits of the proposed control method are analytically authenticated and also validated via simulation study.

Adaptive backstepping control of train systems with traction/braking dynamics and uncertain resistive forces

Although backstepping control design approach has been widely utilised in many practical systems, little effort has been made in applying this useful method to train systems. The main purpose of this paper is to apply this popular control design technique to speed and position tracking control of high-speed trains. By integrating adaptive control with backstepping control, we develop a control scheme that is able to address not only the traction and braking dynamics ignored in most existing methods, but also the uncertain friction and aerodynamic drag forces arisen from uncertain resistance coefficients. As such, the resultant control algorithms are able to achieve high precision train position and speed tracking under varying operation railway conditions, as validated by theoretical analysis and numerical simulations.

Fault-Tolerant Adaptive Control of High-Speed Trains Under Traction/Braking Failures: A Virtual Parameter-Based Approach

Advanced control is a key technology for enhancing safe and reliable operation of high-speed trains. This paper presents an automated train control scheme for high-speed trains with combined longitudinal aerodynamics and tracking/braking dynamics, with special emphasis on reliable position and velocity tracking in the face of traction/braking failures. The controller is synthesized using a so-called virtual-parameter-based backstepping adaptive control method, which exhibits several salient features: 1) The inherent coupling effects are taken into account as a result of combining both longitudinal and traction/braking dynamics; 2) fully parameter independent rather than partially parameter independent control algorithms are derived; and 3) closed-loop tracking stability of the overall system is ensured under unnoticeable time-varying traction/braking failures. The effectiveness of the developed control scheme is authenticated via a formative mathematical analysis based on Lyapunov stability theory and validated via numerical simulations.

Control and Monitoring for Grid-Friendly Wind Turbines: Research Overview and Suggested Approach

Recent years have witnessed a continuing increase in the wind turbine (WT) size and penetration level into the grid, revealing critical challenges to sustainable market development and urgent demands of low voltage ride-through, load reduction, and optimal operation. This paper presents an overview on grid-friendly WTs and relevant technologies for control and monitoring. The development trends are discussed as well to explore potential approaches for optimizing WT systems. The main body contains three parts: the “grid-friendly” criterion and a prototype, the overview and suggestions for WT controller design concerning load reduction and power optimization, and suggested solutions for wind power monitoring systems. This paper is expected to be a reference for extending the knowledge of grid-friendly WTs and optimizing the performance of future large-scale wind power systems.

Bio-inspired Approach for Smooth Motion Control of Wheeled Mobile Robots

Wheeled mobile robot (WMR) has gained wide application in civilian and military fields. Smooth and stable motion of WMR is crucial not only for enhancing control accuracy and facilitating mission completion, but also for reducing mechanical tearing and wearing. In this paper, we present a novel bio-inspired approach aiming at significantly reducing motion chattering phenomena inherent with traditional methods. The main idea of the proposed smooth motion controller is motivated by two famous Chinese sayings “haste does not bring success” and “ride softly then you may get home sooner”, which inspires the utilization of pre-processing the speed commands with the help of fuzzy rules to generate more favorable movement for the actuation device, so as to effectively avoid the jitter problem that has not yet been adequately solved by traditional methods. Detail formulas and algorithms are derived with consideration of the kinematics and dynamics of WMR. Smooth and asymptotically stable tracking of the WMR along the desired position and orientation is ensured and real-time experiment demonstrates the effectiveness and simplicity of the proposed method.

Neural-Memory Based Control of Micro Air Vehicles (MAVs) with Flapping Wings

This paper addresses the problem of wing motion control of flapping wing Micro Air Vehicles (MAVs). Inspired by hummingbirds wing structure as well as the construction of its skeletal and muscular components, a dynamic model for flapping wing is developed. As the model is highly nonlinear and coupled with unmeasurable disturbances and uncertainties, traditional strategies are not applicable for flapping wing motion control. A new approach called neural-memory based control is proposed in this work. It is shown that this method is able to learn from past control experience and current/past system behavior to improve its performance during system operation. Furthermore, much less information about the system dynamics is needed in construction such a control scheme as compared with traditional NN based methods. Both theoretical analysis and computer simulation verify its effectiveness.

Neuroadaptive Variable Speed Control of Wind Turbine With Wind Speed Estimation

It is difficult to measure the wind speed accurately in short term. This reveals challenges for wind turbine control, especially for maximum power point tracking with adaptive control strategies. In this paper, a genetic algorithm based support vector machine model is adopted to estimate the wind speed, using physically measurable signals, such as the electrical power, pitch angle, and rotor speed, while the desired rotor speed can be obtained accordingly. Further, by combining the radial basis function neural networks with adaptive algorithms, a novel virtual parameter based neuroadaptive controller is developed to accommodate the system uncertain and external disturbances. The effectiveness and performances of the proposed method are validated and demonstrated with FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and Simulink.

A Novel Approach for Active Adhesion Control of High-Speed Trains Under Antiskid Constraints

Wheel skid is highly undesirable because it could endanger the safe operation of high-speed trains. How to avoid excessive wheel skid via an active adhesion control method represents an interesting and challenging topic of research. In this work, we first introduce the conditions of antiskid operation and formulate it as a constrained tracking control problem, based on which two model-based antiskid slip velocity control laws are developed. Then, by applying two adaptive force observers to estimate the unknown and varying adhesion force and resistance, we develop an adaptive antiskid adhesion control scheme. The novelty of the proposed method is that control errors of the closed-loop system are used to online update the observer parameters, such that the predefined control precision can be ensured with the proposed observer-based adhesion control. To deal with the constrained antiskid control, a barrier Lyapunov function is constructed, and the effectiveness of the proposed control scheme is theoretically authenticated with confirmation by numerical simulation.

Neuro-Adaptive Fault-Tolerant Approach for Active Suspension Control of High-Speed Trains

Excessive lateral and roll motions of a high-speed train might endanger its operational safety. This paper investigates how to suppress those motions via an active-suspension method. By exploiting the structural properties of the system model and the triangular control gain, a new control scheme capable of attenuating immeasurable disturbances, compensating modeling uncertainties, and accommodating actuation faults is developed. Compared with most existing methods, the proposed method does not require precise information on the suspension parameters and the detail system model. Moreover, the magnitude of the actuation fault and the time instant at which the actuation fault occurs are not needed in setting up and implementing the proposed control scheme. The controller is tested and validated via computer simulations in the presence of parametric uncertainties and varying operation conditions.

Fault-Tolerant Optimal Tip-Speed-Ratio Tracking Control of Wind Turbines Subject to Actuation Failures

In this paper, a maximum-power-point-tracking controller for variable-speed wind turbines (VSWTs) is developed, which is shown to be able to account for modeling uncertainties, unexpected disturbances, subsystem failures, and actuation saturation simultaneously. A novel memory-based approach is used to predict wind speed that is used to generate desired rotor speed accordingly. It is shown that the proposed algorithm not only is robust against nonlinear aerodynamics and adaptive to unknown and time-varying inertia/damp/stiffness properties of VSWTs but also is able to accommodate actuator failures under torque constraints. The benefits of the proposed control method are analytically authenticated and demonstrated with Fatigue, Aerodynamics, Structures, and Turbulence code and Simulink.