Yancheng Liu

Fully-tuned fuzzy neural network based robust adaptive tracking control of unmanned underwater vehicle with thruster dynamics

Abstract In this paper, a fully-tuned fuzzy neural network based robust adaptive control (FFNNBARC) scheme for trajectory and attitude tracking of Unmanned Underwater Vehicle (UUV) subject to thruster dynamics and unknown disturbances, is proposed. The FFNNBRAC consists of a fully-tuned fuzzy neural network (FFNN) controller and a robust controller, where the FFNN estimation is introduced to approximate a backstepping control law, and the robust controller is proposed to provide the finite L2-gain property which can cope with reconstruction errors and can enhance the robustness of the overall control system. As a sequence, the FFNNBRAC scheme is able to render tracking errors asymptotically converge to zero and can guarantee all signals are bounded. Simulation studies and comparisons demonstrate the effectiveness and superiority of the FFNNBRAC scheme in terms of robustness and accuracy.

A hybrid topology scale-free Gaussian-dynamic particle swarm optimization algorithm applied to real power loss minimization

Abstract This paper proposes a hybrid topology scale-free Gaussian-dynamic particle swarm (HTSFGDPS) optimization algorithm for real power loss minimization problem of power system. The swarm population is divided into two parts: hybrid topology population and scale-free topology population. The novel hybrid topology is mixed with fully connected topology and ring topology. Then, it enables the particles to have stronger exploration ability and fast convergence rate at the same time. In the scale-free part, the topology will be gradually generated as the construction process and the optimization process progress synchronously. As a result, the topology exhibits disassortative mixing property, which can improve the swarm population diversity. This work focuses on a new combination of swarm intelligence optimization theory and complex network theory, as well as its application to electric power system. The presented method is tested on IEEE 14-Bus and 30-Bus power system. The numerical results, compared with other stochastic search algorithms, show that HTSFGDPS could find high-quality solutions with higher convergence speed and probability.

Study on DTC-SVM of PMSM based on propeller load characteristic

This paper analyzed the mathematical model of permanent magnet synchronous motor (PMSM), which is used as the propulsion motor in ship electric propulsion systems. Aiming at the problems of bang-bang direct torque control (DTC) applied to PMSM, such as the singularity of basic voltage space vectors available for selecting, large torque ripple and inconstant inverter switching frequency, a novel control method using space vector pulse width modulation (SVPWM) was proposed and a SVPWM module was designed. Then, a DTC-SVM model of PMSM was founded based on the MATLAB/Simulink. Finally, the validity of the speed control performance of propulsion motor with propeller load was verified by simulation The results indicate that DTC-SVM can reduce torque ripple greatly and have good response performance in the process of start and speedup.

A multi-mode operation control strategy for flexible microgrid based on sliding-mode direct voltage and hierarchical controls

Multi-mode operation and transient stability are two problems that significantly affect flexible microgrid (MG). This paper proposes a multi-mode operation control strategy for flexible MG based on a three-layer hierarchical structure. The proposed structure is composed of autonomous, cooperative, and scheduling controllers. Autonomous controller is utilized to control the performance of the single micro-source inverter. An adaptive sliding-mode direct voltage loop and an improved droop power loop based on virtual negative impedance are presented respectively to enhance the system disturbance-rejection performance and the power sharing accuracy. Cooperative controller, which is composed of secondary voltage/frequency control and phase synchronization control, is designed to eliminate the voltage/frequency deviations produced by the autonomous controller and prepare for grid connection. Scheduling controller manages the power flow between the MG and the grid. The MG with the improved hierarchical control scheme can achieve seamless transitions from islanded to grid-connected mode and have a good transient performance. In addition the presented work can also optimize the power quality issues and improve the load power sharing accuracy between parallel VSIs. Finally, the transient performance and effectiveness of the proposed control scheme are evaluated by theoretical analysis and simulation results.

Self-adapting hybrid strategy particle swarm optimization algorithm

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

The analysis of stability of networked control systems based on industrial switched ethernet

In order to enhance the stability of networked control systems(NCS) constructed by switched Ethernet, proposed a new kind of flow control approach based on PAUSE operation defined in 802.3x. During the implementation of flow control, the switch monitors its input and output buffers to prevent buffer overflow. Based on the analysis of structure of PAUSE frame, a general method was provided to design the high and low buffer mark level for analyzing the basic timing characteristics of input and output buffers in PAUSE operation, then, the time delay of the store-and-forward mechanism can be determined effectively. Paper gave a simple method for evaluating effective bandwidth based on output bandwidth, input bandwidth and switchpsilas processing speed, and calculated the response time for multiple sensors simultaneously to transmit the same packets to a target actuator. The simulated results indicate the proposed PAUSE operation can prevent buffer overflow, decrease the total response time delay to meet the requirement of hard real-time communication in industrial control field.

Self-adaptive differential evolution algorithm with hybrid mutation operator for parameters identification of PMSM

Parameters identification of permanent magnetic synchronous motor (PMSM), which significantly influences the control performance of the drive system, is an important and challenging task of power electronic system. The problem requires both high solution quality and fast convergence speed due to the constraints of hardware. This paper presents a self-adaptive differential evolution algorithm with hybrid mutation operator (SHDE) for parameters identification problem. In this method, a novel mutation operator, called “current-to-archive-best,” is developed by mixing the best solutions randomly selected from archive set and current population. Thus, the algorithm could use the best searching memories so far to generate promising solutions, yielding a faster evolving procedure. Besides, the corresponding control parameters of SHDE are also self-adapted without tedious trial-and-error progress to get appropriate values. Moreover, the parameters estimation program is inserted into the PMSM simulation that is solved by using Newton–Raphson method without any pre-assumption and simplification. This framework, which may be used under any working conditions with large disturbance, is different from other publications, resulting in wider applications. The proposed method applied to parameters identification of PMSM is evaluated on a PMSM drive system with two different operations. The comprehensive results and statistical analyses, compared with other state-of-the-art algorithms, show that SHDE could find high-quality solutions with higher convergence speed and probability.

Adaptive neuro-fuzzy tracking control of UUV using sliding-mode-control-theory-based online learning algorithm

This paper presents the control of an Unmanned Underwater Vehicle(UUV) with five degrees of freedom by using an adaptive neuro-fuzzy controller combined with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure is composed of an adaptive fuzzy neural network(AFNN) and a conventional PD controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only decrease the steady-state error but also improve the transient response performance of the UUV.

Command filtered adaptive control for integrated missile guidance and autopilot with terminal angular constraint

For homing missiles considering terminal angular constraint, integrated design of guidance and control utilizing adaptive command filtered backstepping is studied in this paper. The proposed control scheme guarantees not only the stability of the missile dynamics with state constraints but also the interception performance with a desired terminal attitude angle. An adaptive law is introduced to compensate unknown uncertainties in the varying-time system. Simulation results are further presented to show the effectiveness and performance of the proposed approach.

The analysis of the key performances of ControlNet

In order to evaluate the real time property, stability and deterministic of ControlNet fieldbus, a simple and effective transmission time delay model was proposed to analyze the characteristic of virtual token passing (VTP) mechanism. Then, the maximum transmission time delay in the scheduled part of network update time can be determined. According to the contribution of the transmission time delay to the network throughput, a new performance evaluation architecture, which includes the network efficiency, network utilization, packet loss rate and throughput, was given. The simulated results indicate ControlNet has good throughput and excellent real-time property in multicast mode when the traffic load is small, and the network efficiency is over 90%.