8 research outputs found
Direct thrust force control of primary permanent magnet linear motor based on improved extended state observer and model-free adaptive predictive control
A model-free adaptive predictive control algorithm based on an improved extended state observer (IESO) is proposed to solve the problem that the primary permanent magnet linear motor is susceptible to time-varying parameters and unknown disturbances. Firstly, a model-free adaptive control algorithm based on compact format is designed to achieve high control precision of the system and reduce thrust fluctuation, only through the input/output data of the system. Because the traditional model-free adaptive control is too sensitive to the internal parameters of the controller, a combination of model-free adaptive control and predictive control is further developed. By predicting the data for a future time in advance, the sensitivity to the internal parameters of the controller is reduced and the control performance is further improved. Since the load change and other nonlinear disturbances in practical applications have a great impact on the control effect of the system, an improved extended state observer is further used to compensate for the impact of nonlinear disturbances on the control system. In addition, the stability of the closed-loop system is analyzed. Comparable simulation results clearly demonstrate the good tracking performance and strong robustness of the proposed control
Advances in the Field of Electrical Machines and Drives
Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications
Nonlinear Systems
Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems
Run-time reconfiguration for efficient tracking of implanted magnets with a myokinetic control interface applied to robotic hands
Tese (doutorado)—Universidade de BrasÃlia, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Este trabalho introduz a aplicação de soluções de aprendizagem de máquinas visado ao problema do rastreamento de posição do antebraço baseado em sensores magnéticos. Especi ficamente, emprega-se uma estratégia baseada em dados para criar modelos matemáticos que possam traduzir as informações magnéticas medidas em entradas utilizáveis para dispositivos protéticos. Estes modelos são implementados em FPGAs usando operadores customizados de ponto flutuante para otimizar o consumo de hardware e energia, que são importantes em dispositivos embarcados. A arquitetura de hardware é proposta para ser implementada como um sistema com reconfiguração dinâmica parcial, reduzindo potencialmente a utilização de recursos e o consumo de energia da FPGA. A estratégia de dados proposta e sua implemen tação de hardware pode alcançar uma latência na ordem de microssegundos e baixo consumo de energia, o que encoraja mais pesquisas para melhorar os métodos aqui desenvolvidos para outras aplicações.Coordenação de Aperfeiçoamento de Pessoal de NÃvel Superior (CAPES).This work introduces the application of embedded machine learning solutions for the problem of magnetic sensors-based limb tracking. Namely, we employ a data-driven strat egy to create mathematical models that can translate the magnetic information measured to usable inputs for prosthetic devices. These models are implemented in FPGAs using cus tomized floating-point operations to optimize hardware and energy consumption, which are important in wearable devices. The hardware architecture is proposed to be implemented as a dynamically partial reconfigured system, potentially reducing resource utilization and power consumption of the FPGA. The proposed data-driven strategy and its hardware implementa tion can achieve a latency in the order of microseconds and low energy consumption, which encourages further research on improving the methods herein devised for other application
Control of Proton Exchange Membrane Fuel Cell System
265 p.In the era of sustainable development, proton exchange membrane (PEM) fuel cell technology has shown significant potential as a renewable energy source. This thesis focuses on improving the performance of the PEM fuel cell system through the use of appropriate algorithms for controlling the power interface. The main objective is to find an effective and optimal algorithm or control law for keeping the stack operating at an adequate power point. Add to this, it is intended to apply the artificial intelligence approach for studying the effect of temperature and humidity on the stack performance. The main points addressed in this study are : modeling of a PEM fuel cell system, studying the effect of temperature and humidity on the PEM fuel cell stack, studying the most common used power converters in renewable energy systems, studying the most common algorithms applied on fuel cell systems, design and implementation of a new MPPT control method for the PEM fuel cell system
Dynamic neural network-based feedback linearization of electrohydraulic suspension systems
Resolving the trade-offs between suspension travel, ride comfort, road holding,
vehicle handling and power consumptions is the primary challenge in designing
Active-Vehicle-Suspension-Systems (AVSS). Controller tuning with global
optimization techniques is proposed to realise the best compromise between these
con
icting criteria. Optimization methods adapted include
Controlled-Random-Search (CRS), Differential-Evolution (DE), Genetic-Algorithm
(GA), Particle-Swarm-Optimization (PSO) and Pattern-Search (PS). Quarter-car
and full-car nonlinear AVSS models that incorporate electrohydraulic actuator
dynamics are designed. Two control schemes are proposed for this investigation.
The first is the conventional Proportional-Integral-Derivative (PID) control, which
is applied in a multi-loop architecture to stabilise the actuator and manipulate the
primary control variables. Global optimization-based tuning achieved enhanced
responses in each aspect of PID-based AVSS performance and a better resolve in
con
icting criteria, with DE performing the best. The full-car PID-based AVSS
was analysed for DE as well as modi ed variants of the PSO and CRS. These
modified methods surpassed its predecessors with a better performance index and
this was anticipated as they were augmented to permit for e cient exploration of
the search space with enhanced
exibility in the algorithms. However, DE still
maintained the best outcome in this aspect. The second method is indirect
adaptive dynamic-neural-network-based-feedback-linearization (DNNFBL), where
neural networks were trained with optimization algorithms and later feedback
linearization control was applied to it. PSO generated the most desirable results,
followed by DE. The remaining approaches exhibited signi cantly weaker results
for this control method. Such outcomes were accredited to the nature of the DE
and PSO algorithms and their superior search characteristics as well as the nature
of the problem, which now had more variables. The adaptive nature and ability to
cancel system nonlinearities saw the full-car PSO-based DNNFBL controller
outperform its PID counterpart. It achieved a better resolve between performance
criteria, minimal chatter, superior parameter sensitivity, and improved suspension
travel, roll acceleration and control force response
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words
An RBFNN-Based Direct Inverse Controller for PMSM with Disturbances
Considering the system uncertainties, such as parameter changes, modeling error, and external uncertainties, a radial basis function neural network (RBFNN) controller using the direct inverse method with the satisfactory stability for improving universal function approximation ability, convergence, and disturbance attenuation capability is advanced in this paper. The weight adaptation rule of the RBFNN is obtained online by Lyapunov stability analysis method to guarantee the identification and tracking performances. The simulation example for the position tracking control of PMSM is studied to illustrate the effectiveness and the applicability of the proposed RBFNN-based direct inverse control method