Danwei Wang

Relationship between space-vector modulation and three-phase carrier-based PWM: a comprehensive analysis [three-phase inverters]

This paper comprehensively analyzes the relationship between space-vector modulation and three-phase carrier-based pulse width modulation (PWM). The relationships involved, such as the relationship between modulation signals (including zero-sequence component and fundamental components) and space vectors, the relationship between the modulation signals and the space-vector sectors, the relationship between the switching pattern of space-vector modulation and the type of carrier, and the relationship between the distribution of zero vectors and different zero-sequence signal are systematically established. All the relationships provide a bidirectional bridge for the transformation between carrier-based PWM modulators and space-vector modulation modulators. It is shown that all the drawn conclusions are independent of the load type. Furthermore, the implementations of both space-vector modulation and carrier-based PWM in a closed-loop feedback converter are discussed.

Feedback stabilization and tracking of constrained robots

Mathematical models for constrained robot dynamics, incorporating the effects of constraint force required to maintain satisfaction of the constraints, are used to develop explicit conditions for stabilization and tracking using feedback. The control structure allows feedback of generalized robot displacements, velocities, and the constraint forces. Global conditions for tracking, based on a modified computed-torque controller and local conditions for feedback stabilization, using a linear controller, are presented. The framework is also used to investigate the closed-loop properties if there are force disturbances, dynamics in the force feedback loops, or uncertainty in the constraint functions. >

Digital repetitive learning controller for three-phase CVCF PWM inverter

In this paper, a plug-in digital repetitive leaning control scheme is proposed for three-phase constant-voltage constant-frequency (CVCF) pulsewidth modulation inverters to achieve high-quality sinusoidal output voltages. In the proposed control scheme, the repetitive controller (RC) is plugged into the stable one-sampling-ahead-preview-controlled three-phase CVCF inverter system using only two voltage sensors. The RC is designed to eliminate periodic disturbance and/or track periodic reference signal with zero tracking error, The design theory of plug-in repetitive learning controller is described systematically and the stability analysis or overall system is discussed. The merits of the controlled systems include features of minimized total harmonic distortion, robustness to parameter uncertainties, fast response, and fewer sensors. Simulation and experimental results are provided to illustrate the effectiveness of the proposed scheme.

Brief Iterative learning control with initial rectifying action

This paper addresses the initial shift problem in iterative learning control with system relative degree. The tracking error caused by nonzero initial shift is detected when applying a conventional learning algorithm. Finite initial rectifying action is introduced in the learning algorithm and is shown effective in the improvement of tracking performance, in particular robustness with respect to variable initial shifts. The uniform convergence of the output trajectory to a desired one jointed smoothly with a specified transient trajectory from the starting position is ensured in the presence of fixed initial shift.

Linear Phase Lead Compensation Repetitive Control of a CVCF PWM Inverter

This paper presents a simple and efficient linear phase lead compensation repetitive control scheme for engineers to develop high-performance power converter systems. The linear phase lead compensation helps a repetitive controller to achieve faster convergence rate, higher tracking accuracy, and wider stability region. In the proposed scheme, the phase lead compensation repetitive controller is plugged into generic state-feedback-controlled converter systems. Its comprehensive synthesis, which involves principle, analysis, design, modeling, implementation, and experiments, is systematically and completely presented in great detail. A complete series of experiments is successfully carried out to verify the solution.

A fuzzy controller with supervised learning assisted reinforcement learning algorithm for obstacle avoidance

Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base due to the curse of dimensionality. In this paper, we propose a neural fuzzy system with mixed coarse learning and fine learning phases. In the first phase, a supervised learning method is used to determine the membership functions for input and output variables simultaneously. After sufficient training, fine learning is applied which employs reinforcement learning algorithm to fine-tune the membership functions for output variables. For sufficient learning, a new learning method using a modification of Sutton and Bartos model is proposed to strengthen the exploration. Through this two-step tuning approach, the mobile robot is able to perform collision-free navigation. To deal with the difficulty of acquiring a large amount of training data with high consistency for supervised learning, we develop a virtual environment (VE) simulator, which is able to provide desktop virtual environment (DVE) and immersive virtual environment (IVE) visualization. Through operating a mobile robot in the virtual environment (DVE/IVE) by a skilled human operator, training data are readily obtained and used to train the neural fuzzy system.

Modeling and Analysis of Skidding and Slipping in Wheeled Mobile Robots: Control Design Perspective

This paper aims to give a general and unifying presentation on modeling of wheel mobile robots (WMRs) in the presence of wheel skidding and slipping from the perspective of control design. We present kinematic models that explicitly relate perturbations to the vehicle skidding and slipping. Four configurations of mobile robots are considered, and perturbations due to skidding and slipping are categorically classified as input-additive, input multiplicative, and/or matched/unmatched perturbations. Furthermore, we relate the WMRs maneuverability with the vehicle controllability that provides a measure on the WMR ability to track a trajectory in the presence of wheel skidding and slipping. These classifications and formulations lay a base for the deployments of various control design techniques to overcome the addressed perturbations.

Zero-phase odd-harmonic repetitive controller for a single-phase PWM inverter

In this paper, a zero-phase odd-harmonic repetitive control scheme is proposed for pulse-width modulation inverters. The proposed repetitive controller combines an odd-harmonic periodic generator with a noncasual zero-phase compensation filter. It occupies less data memory than a conventional repetitive controller does. Moreover, it offers faster convergence of the tracking error, and yields very low total harmonics distortion (THD) and low tracking error. Analysis and design of the proposed system are presented. Experimental results with the proposed repetitive controller are presented to validate the approach. The phenomena of even-harmonic residues in the proposed control system is discussed and experimentally demonstrated.

Sparse Extreme Learning Machine for Classification

Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM.

Brief Paper: Convergence and Robustness of Discrete Time Nonlinear Systems with Iterative Learning Control

In this paper, iterative learning control design of a class of discrete time nonlinear dynamic systems with disturbances are considered. An iterative learning control law is proposed to overcome the uncertainties in system parameters and disturbances. It is shown that the system outputs, states and control inputs can be guaranteed to converge to desired trajectories in the absence of state, output disturbances and repeatability uncertainty. In the presence of these disturbances and initial state uncertainty, the tracking errors will be bounded. Experiment is carried out to verify the theory and results are presented.