Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC) system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF) network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme

Nonlinear dynamic modeling and fuzzy sliding-mode controlling of electromagnetic levitation system of low-speed maglev train

The electromagnet levitation system (ELS) of low-speed maglev train is taken as the research object. The nonlinear dynamics and control law of ELS are discussed. Specifically, by employing the Euler-Lagrange’s method, a nonlinear dynamic model is constructed for the single-ELS. Then, the linear control law is studied, which has a disadvantage of weak robustness. To improve the performance of the controller, a fuzzy sliding-mode control law is proposed. According to the dynamic nonlinear model, a novel sliding surface which can make the system reach the stable point within the finite time is presented. Moreover, the fuzzy inference method is utilized to slow down the speed of the states crossing the sliding surface. The simulation results demonstrate that the global robustness of external disturbance and parameter perturbation can be achieved through the proposed control law. And the chattering phenomenon can be reduced significantly. Finally, the experiments are also implemented to examine its practical dynamic performance of the proposed control law

Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM

In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE

Fuzzy Attitude Control of Solar Sail

This study presents various fuzzy type controllers for a solar sail. First, a Twin Parallel Distributed Compensator (TPDC) is built for a Takagi-Sugeno fuzzy model of the solarsail. The T-S fuzzy model is constructed by linearizing the existing nonlinear equations fo motion of the solar sail. The T-S fuzzy model is used to solve a set of linear matrix inequalities to derive state feedback controller gains. The TPDC controls the solar sail using a combination of reaction wheels and roll stabilizer bars for attitude control and trim masses for disturbance rejection. The TPDC tracks and stabilizes the solar sail to any desired state in the presence of parameter uncertainties and external disturbances while satisfying actuator constraints. The performance of the TPDC is compared to a Ziegler-Nichols tuned proportional-integral-derivative (PID) controller. Numerical simulation shows the TPDC outperforms he PID controller when stabilizing the solar sail to a desired state. When compared to the particle swarm optimized PID controller, the TPDC has a slower response, irrespective of the initial conditions and desired states. To control the solar sail using trim masses, a hybrid fuzzy-logic supervisor with a PID attitude controller is built. Particle swarm optimization is also used to obtain gains for roll, pitch and yaw PID controllers. The calculated PID gains are used to build a fuzzy supervisor that tunes the PID controller gains based on the Euler angle error and error rate. The proposed PID controller stabilizes the solar sail about any commanded input from any initial condition. The supervisory fuzzy PID controller is shown to be robust model uncertainties and outperforms a particle swarm optimized PID controller when stabilized about a non-optimal state from a non-optimal initial condition

An improved synchronous control strategy based on fuzzy controller for PMSM

In industry, rail gnawing phenomenon often appears in the synchronous operation of crane. An improved synchronous control strategy for a dual permanent magnet synchronous motor (PMSM) servo system is proposed in this paper. To achieve a synchronous performance with robustness, a cross-coupled synchronous system based on the virtual electronic shaft which both torque and speed are used as feedback signals is developed. Two fuzzy PID compensators with accurate approximation capability based on programmable logic controller (PLC) is implemented to estimate the lumped uncertainty. In the computer simulation, the improved synchronous control strategy is proved to have strong ability to adjust for the external disturbance, and have both smaller convergence time and tracking error. Some experimental results are illustrated to depict the validity of the proposed control approach, which can effectively inhibited wheel’s rail gnawing phenomenon in crane.http://www.eejournal.ktu.lt/index.php/elthb201

Robust global sliding model control for water-hull-propulsion unit interaction systems - part 2: model validation

Unexpected severe hull deformation caused by wave loads poses alignment problem to the propulsion shaft line in large scale ships, which would significantly influence the dynamical performance of the marine propulsion system. How to suppress negative disturbance imposed by the interaction between water-hull-propulsion and ensure the normal operation of the marine propulsion system is a challenging task. To address this issue, a new global sliding model control (GSMC) for marine water-hull-propulsion unit systems is proposed and investigated to obtain more accurate control performance in a series of researches. In Part 1 the GSMC controller has been developed and the bounded nonlinear model uncertainties have been derived based on the experiments and sea trial. In this work the upper boundary of 1,85 % was introduced into the GSMC controller to derive the total control law realising the robust control of the marine propulsion system. Numerical simulations based on the real bulk carrier parameters show a high effectiveness of the GSMC for speed tracking, compared with the traditional sliding model controller and Proportional Integral Derivative (PID) controller. By the proposed and investigated control system in this paper may be developed a simple practical-effective robust control strategy for marine propulsion systems subject to some complex unknown uncertainties through further investigations, validations and modification

Hybrid power management for fuel cell-supercapacitor powered hybrid electric vehicle

Fuel cell (FC) with a combination of supercapacitor (SC) based hybrid electric vehicles have been regarded as a potential solution in the future transportation system. This is due to their zero-emission, enhancement of transient power demand, ability to absorb the energy from the regenerative braking, high efficiency, and long mileage. Nevertheless, the nonlinear output characteristics of the FC system are a feeble point owing to internal constraints such as membrane water content and cell temperature. Hence it is essential to extricate as much power as possible from the stack to avert excessive fuel usage and low system efficiency. Conversely, despite the advantages of the SC as an auxiliary energy storage system, the series connection of SC cells causes a cell imbalance problem due to uneven cell characteristics that occur during the manufacturing process and its ambient conditions. This discrepancy of cell voltages in a supercapacitor module leads to reduce the stack’s efficiency and its lifetime. Furthermore, the above limitations of the power sources and initial state of SC’s charge affect the power management’s distribution of power among the multiple sources. Therefore, the aim of this thesis is to propose a hybrid power management for fuel cell-supercapacitor powered hybrid electric vehicles to solve the three identified problems. Firstly, this thesis focuses on a maximum power point tracking (MPPT) controller with a modified 4-leg interleaved boost converter (M-FLIBC) topology for the FC system. The effectiveness of the proposed IBC with a controller for the FC is compared with the two additional controllers couples with the conventional FLIBC topology. Next, a global modular balancer for voltage balancing of multiple supercapacitor cells is connected in series for an HEV system. The global modular balancing architecture is proposed based on forward conversion, which integrates cell balancing, module balancing, and operating for different frequencies. Thus, greatly reducing the volume and implementation complexity. Finally, the thesis evaluates hybrid power management (HPM) for effective power sources distribution, in order to reduce hydrogen consumption and enhance the vehicle's fuel economy. In this case, an equivalent circuit model of SC is developed for the energy storage system. The combination of an extended Kalman filter (EKF) and traditional coulomb counting (CC) method is used to estimate the SC state of charge in improving the effectiveness of the HPM. To evaluate the fuel economy under realistic driving conditions, the combined environmental protection agency (EPA) test cycles for a city and highway are considered. The outcome of performance comparison of the different controllers based on MPPT technique in terms of voltage, current, power, settling time, and efficiency of the FC indicates that the radial basis function network (RBFN) based MPPT controller with the M-FLIBC outperforms the PID and Fuzzy based controllers. With respect to controlling of SC in HEV environment, the proposed topology of SC presents effective voltage balancing with a lower component count, able to operate at different frequencies, i.e., 10 to 70 kHz, as well opens to unlimited stackable modular numbers of SC cells for the HEV performance analysis. Ultimately, with all the proposed control topologies and combined EKF-CC based power management for the FC-SC in Series HEV, the vehicle's fuel economy is increased to 93.38 km/kg as compared to traditional CC based power management of 86.53 km/kg, besides it improves the vehicle’s acceleration within 0-100 km/h in 9.0 seconds respectively. Finally, the research shows that the hybrid power management of FC and SC powered HEV leads to improved performance of the vehicle in terms of the key measures. Suggestions for future research are also highlighted

Robust global sliding model control for water-hull-propulsion unit interaction systems - Part 1: System boundary identification

Nepredviđene jake deformacije trupa uzrokovane težinom valova mogle bi značajno utjecati na dinamičko ponašanje pogonskog sustava velikih brodova, rezultirajući slabljenjem upravljačke funkcije broda. U ovom se radu predlaže upravljanje novim globalnim kliznim modelom (GSMC) za brodske sustave voda-trup-pogonska jedinica u svrhu poboljšanja upravljačkih performansi. Taj GSMC najprije je primijenjen u razvijanju modela za upravljanje brodskim pogonom s nelinearnim nesigurnostima. U GSMC modelu primijenjena je metoda funkcije zasićenja za isključenje podrhtavanja na kliznoj površini. Zatim je pomoću Lyapunova kriterija stabilnosti potvrđena stabilnost upravljačkog sustava. Po prvi puta, granični problem nesigurnosti nelinearnog modela kvantitativno je istražen. Nesigurnosti graničnog nelinearnog modela potrebne u predloženom GSMC modelu, uključujući gubitak /varijacije zakretnog momenta motora, prijenos energije za različite uvjete opterećenja i brzine rotacije osovine, dobivene su na temelju eksperimenata provedenih na liniji brodski osovinski vod – probni stol integriranog pogonskog sustava kao i pokusne plovidbe na moru broda za prijevoz rasutog tereta. Postignuta je gornja granica od 1,85 % za nesigurnost modela, i ona bi se trebala unijeti u GSMC za integrirani brodski pogonski sustav kako bi se izveo ukupni upravljački zakon za realizaciju izdržljivog upravljanja sustavaUnexpected severe hull deformation caused by the wave loads would significantly influence the dynamical behaviours of the propulsion system in large scale ships, resulting in degradation of the ship control performance. A new global sliding model control (GSMC) for marine water-hull-propulsion unit systems is proposed to obtain more accurate control performance in this paper. The GSMC was firstly employed to establish the marine propulsion control model with nonlinear uncertainties. In the GSMC model, the saturation function method is applied to eliminate chattering on the sliding surface. Then the Lyapunov stability criterion is adopted to confirm the stability of the control system. Following, for the first time, the boundary problem of the nonlinear model uncertainties were investigated quantitatively. The bounded nonlinear model uncertainties required in the proposed GSMC model, involving engine torque loss / variations, power transfer for various load conditions and shaft rotational speeds, were derived based on the experiments carried out on a marine shaft-line test-bed of the integrated propulsion system as well as a sea trial implemented for a running bulk carrier. An upper boundary of 1,85 % for the model uncertainty has been obtained, which would be introduced into the GSMC for the integrated marine propulsion system to derive the total control law realising the robust control of the system

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi