Unmanned wave glider heading model identification and control by artificial fish swarm algorithm

We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.

Robust parametric approach for tracking control of an air-breathing hypersonic cruise vehicle

To realize the stabilization and the tracking of flight control for an air-breathing hypersonic cruise vehicle, the linearization of the longitudinal model under trimmed cruise condition is processed firstly. Furthermore, the flight control problem is formulated as a robust model tracking control problem. And then, based on the robust parametric approach, eigenstructure assignment and reference model tracking theory, a parametric optimization method for robust controller design is presented. The simulation results show the effectiveness of the proposed approach.

QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

SOME ISSUES OF TURBULENCE STATISTICS

The issue of dropping the random force f(i) and the arbitrariness of choosing the basic variable in the variational approach to turbulence closure problem, pointed out recently by the Russian scientists Bazdenkov and Kukharkin, are discussed. According to the mean-square estimation method, the random force f(i) should be dropped in the error expression of the LFP (Langevin-Fokker-Planck) model. However, f(i) is not neglected, its effect has been taken into account by the variational approach. In order to optimize the perturbation solution of the Liouville equation, the LFP model requires that the basic variable is as near to Gaussian as possible. Hence, the velocity, instead of the vorticity, should be chosen as the basic variable in the three-dimensional turbulence. Although the LFP model and the zero-order Gaussian term of PDF (probability density function) imply whiteness assumption (zero correlation time of f(i)), the higher-order non-Gaussian terms of PDF correspond to the nonwhiteness of turbulence dynamics, the variational approach does calculate the nonwhiteness effect properly.

Optimization and Configuration of Control Parameters to Enhance Small-signal Stability of Hybrid LCC-MMC HVDC System

This paper investigates the small-signal stability of the hybrid high-voltage direct current(HVDC)transmission system.The system is composed of line commutated converter(LCC)as rectifier and modular multi-level converter(MMC)as inverter under weak AC grid condition.Firstly,the impact of short-circuit ratio(SCR)at inverter side on the system stability is investigated by eigen-analysis,and the key control parameters which have major impact on the dominant mode are identified by the participation factor and sensitivity analysis.Then,considering the quadratic index and damping ratio characteristic,an objective function for evaluating the system stability is developed,and an optimization and configuration method for control parameters is presented by the utilization of Monte Carlo method.The eigenvalue results and the electromagnetic transient(EMT)simulation results show that,with the optimized control parameters,the small-signal stability and the dynamic responses of the hybrid system are greatly improved,and the hybrid system can even operate under weak AC grid condition.

Control Parameter Optimization of Thermostatically Controlled Loads Using a Modified State-queuing Model

Thermostatically controlled loads(TCLs)are one of the best candidates to participate in direct load control(DLC).However,little attention is given to the parameter optimization of the TCLs control system due to the complexity of the TCLs’dynamics.In this paper,the parameters of the feedback control system based on the direct compressor control mechanism(DCCM)are optimized using the modified state-queuing(SQ)model,which can provide good characterizaton and greatly simplifies the dynamics of the TCLs.The simulation results verify the effectiveness of the proposed method.

Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms

Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.

High Frequency Stability Constraints Based MMC Controller Design Using NSGA-III Algorithm

The occurrence of high frequency resonances(HFRs)has been frequently observed in several MMC-HVDC projects.To avoid these HFRs,the controller design of an MMC must satisfy two requirements:1)The controller should remain stable while in the high frequency range,and 2)MMC impedance should not possess a negative real part in the high frequency range.So far,majority of the related studies on MMC controller design have been unable to address these requirements precisely.This paper first describes the simplified high frequency MMC impedance model developed indigenously by the authors.Subsequently,the driving mechanism of two kinds of HFRs is revealed using the said developed model,including:1)MMC controller instability,and 2)Interaction instability between MMC and AC cables.Furthermore,the mathematical expressions outlining the controller stability constraint and positive damping constraint are proposed.Additionally,NSGA-III based multi-objective optimization algorithm is adopted,to identify the region most suitable for satisfying the proposed stability constraints under the MMC controller parameters.The proposed controller design method is capable of effectively evading the HFRs triggered by the incompatible MMC.The detailed time-domain simulations generated using PSCAD/EMTDC software validate the proposed designed method and endorse the improved results.

Enlarging the guaranteed region of attraction in nonlinear systems with bounded parametric uncertainty

A novel approach to enlarge the guaranteed region of attraction in nonlinear systems with bounded parametric uncertainties based on the design of a nonlinear controller is proposed.The robust domain of attraction(RDA) is estimated using the parameter-dependent quadratic Lyapunov function and enlarged by the optimal controlling parameters.The problem of extending the RDA is indicated in a form of three-layer optimization problem.Some examples illustrate the efficiency of the proposed strategy in enlarging RDA.