Intelligent PID Controller of Flexible Link Manipulator with Payload

This paper presents the experimental study of intelligent PID controller with the present of payload. The controllers were constructed to optimally track the desired hub angle and vibration suppression of DLFRM. The hub angle and end-point vibration models were identified based on NNARX structure. The results of all developed controllers were analyzed in terms of trajectory tracking and vibration suppression of DLFRM subjected to disturbance. The simulation studies showed that the intelligent PID controllers have provided good performance. Further investigation via experimental studies was carried out. The results revealed that the intelligent PID control structure able to show similar performance up to 20 g of payload hold by the system. Once the payload increased more than 20 g, the performance of the controller degrades. Thus, it can be concluded that, the controllers can be applied in real application, provided the tuning process were carried out with the existence of the maximum payload which will be subjected in the system. The 20 g payload value can act as uncertainty for the controller performance

Sensored speed control of brushless DC motor based salp swarm algorithm

This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time

Automated robust control system design for variable speed drives

Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions present themselves as complex control strategies that demand specialist expertise for their implementation. As a direct consequence, these factors have limited their adoption for the industrial control of drives. To counter this trend, the thesis proposes two techniques for robust control system design. The developed strategies employ particular Evolutionary Algorithms EA), which enable their simple and automated implementation. More specifically, the EA employed and tested are the Genetic Algorithms (GA), Bacterial Foraging (BF) and the novel Hybrid Bacterial Foraging, which combines specific desirable features of the GA and BF. The first technique, aptly termed Robust Experimental Control Design, employs the above mentioned EA in an automated trial-and-error approach that involves directly testing control parameters on the experimental drive system, while it operates under variable mechanical loads, evolving towards the best possible solutions to the control problem. The second strategy, Robust Identification-based Control Design, involves a GA system identification procedure employed in automatically defining an uncertainty model for the variable mechanical loads and, through the adoption of the Frequency Domain H-infinity Method in combination with the developed EA, robust controllers for drive systems are designed. The results that highlight the effectiveness of the robust control system design techniques are presented. Performance comparisons between the design techniques and amongst the employed EA are also shown. The developed techniques possess commercially viable qualities because they elude the need for skilled expertise in their implementation and are deployed in a simple and automated fashion

Automated robust control system design for variable speed drives

Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions present themselves as complex control strategies that demand specialist expertise for their implementation. As a direct consequence, these factors have limited their adoption for the industrial control of drives. To counter this trend, the thesis proposes two techniques for robust control system design. The developed strategies employ particular Evolutionary Algorithms EA), which enable their simple and automated implementation. More specifically, the EA employed and tested are the Genetic Algorithms (GA), Bacterial Foraging (BF) and the novel Hybrid Bacterial Foraging, which combines specific desirable features of the GA and BF. The first technique, aptly termed Robust Experimental Control Design, employs the above mentioned EA in an automated trial-and-error approach that involves directly testing control parameters on the experimental drive system, while it operates under variable mechanical loads, evolving towards the best possible solutions to the control problem. The second strategy, Robust Identification-based Control Design, involves a GA system identification procedure employed in automatically defining an uncertainty model for the variable mechanical loads and, through the adoption of the Frequency Domain H-infinity Method in combination with the developed EA, robust controllers for drive systems are designed. The results that highlight the effectiveness of the robust control system design techniques are presented. Performance comparisons between the design techniques and amongst the employed EA are also shown. The developed techniques possess commercially viable qualities because they elude the need for skilled expertise in their implementation and are deployed in a simple and automated fashion

State feedback control for a PM hub motor based on gray Wolf optimization algorithm

© 1986-2012 IEEE. This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the gray wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state-space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in linear quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWO-based state feedback controller (SFC) with and without the penalty term, the conventional SFC, and the genetic algorithm enhanced proportional-integral controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed SFC with the penalty term in fast response

Projeto do sistema de controle de um exoesqueleto do membro inferior direito

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2017.Nesta pesquisa o modelo de um exoesqueleto do membro inferior direita para melhorar a mobilidade do usuário e seu sistema de controle foram desenvolvidos. O projeto físico do modelo do exoesqueleto consiste em três partes principais: um quadril e a parte superior e inferior da perna conectados um com o outro por juntas revolutas. Cada uma das juntas é atuado por um motor Brushless DC (BLDC) com caixa de redução para aumentar torque. Os motores a serem usados na construção possuem sensores de velocidade e de posição para fornecer os dados necessários para o sistema de controle. Solidworks Computer Aided Design (CAD) software é usado para desenvolver o modelo do exoesqueleto, que é salvo em formato extensible markup language (XML) para depois ser importado em Simmechanics, permitindo a integração de modelos de corpos físicos com componentes de Simulink. A cinemática inversa do exoesqueleto é desenvolvido e projetado em Very high speed integrated circuit Hardware Description Language (VHDL) usando aritmética em ponto flutuante para ser executado a partir de um dispositivo Field Programmable Gate Array (FPGA). Quatro representações diferentes do projeto de hardware do modelo cinematico do exoesqueleto foram desenvolvidos fazendo análise de erro com Mean Square Error (MSE) e Average Relative Error (ARE). Análise de trade-off de desempenho e área em FPGA é feito. A estratégia de controle Proportional-Integrative-Derivative (PID) é escolhido para desenvolver o sistema de controle do exoesqueleto por ser relativamente simples e eficiente para desenvolver e por ser amplamente usado em muitas áreas de aplicação. Duas estratégias de sistemas de controle combinado de posiçaõ e velocidade são desenvolvidos e comparados um com o outro. Cada sistema de controle consiste em dois controladores de velocidade e dois de posição. Os parâmetros PID são calculados usando os métodos de sintonização Ziegler-Nichols e Particle Swarm Optimization (PSO). PSO é um método de sintonização relativamente simples porém eficiente que é aplicado em muitos problemas de otimização. PSO é baseado no comportamento supostamente inteligente de cardumes de peixes e bandos de aves em procura de alimento. O algoritmo, junto com o método Ziegler-Nichols, é usado para achar parâmetros PID apropriados para os blocos de controle nas duas estratégias te controle desenvolvidos. A resposta do sistema de controle é avaliada, analisando a resposta a um step input. Simulação da marcha humana é também feito nos dois modelos de sistema de controle do exoesqueleto fornecendo dados de marcha humana ao modelo e analisando visualmente os movimentos do exoesqueleto em Simulink. Os dados para simulação da marcha humana são extraídos de uma base de dados existente e adaptados para fazer simulações nos modelos de sistema de controle do exoesqueleto.In this research a model of an exoskeleton of the right lower limb for user mobility enhancement and its control system are designed. The exoskeleton design consists of three major parts: a hip, an upper leg and a lower leg part, connected to one another with revolute joints. The joints will each be actuated by Brushless DC (BLDC) Motors equipped with gearboxes to increase torque. The motors are also equipped with velocity and position sensors which provide the necessary data for the designed control systems. Solidworks Computer Aided Design (CAD) software is used to develop a model of the exoskeleton which is then exported in extensible markup language (XML) format to be imported in Simmechanics, enabling the integration of physical body components with Simulink components. The inverse kinematics of the exoskeleton model is calculated and designed in Very high speed integrated circuit Hardware Description Language (VHDL) using floating-point numbers, to be executed from a Field Programmable Gate Array (FPGA) Device. Four different bit width representations of the hardware design of the kinematics model of the exoskeleton are developed, performing error analysis with the Mean Square Error (MSE) and the Average Relative Error (ARE) approaches. Trade-off analysis is then performed against performance and area on FPGA. The Proportional-Integrative-Derivative (PID) control strategy is chosen to develop the control system for the exoskeleton for its relatively simple design and proven efficient implementation in a very broad range of real life application areas. Two control system strategies are developed and compared to one another. Each control system design is comprised of two velocity- and two position controllers. PID parameters are calculated using the Ziegler-Nichols method and Particle Swarm Optimization (PSO). PSO is a relatively simple yet powerful optimization method that is applied in many optimization problem areas. It is based on the seemingly intelligent behaviour of fish schools and bird flocks in search of food. The algorithm, alongside the Ziegler-Nichols method, is used to find suitable PID parameters for control system blocks in the two designs. The system response of the control systems is evaluated analyzing step response. Human gait simulation is also performed on the developed exoskeleton control systems by observing the exoskeleton model movements in Simulink. The gait simulation data is extracted from a human gait database and adapted to be fed as input to the exoskeleton control system models

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

The paper investigates the application of the Artificial Neural Network (ANN) in modeling of double-link flexible robotic manipulator (DLFRM). The system was categorized under multi-input multi-output. In this research, the dynamic models of DLFRM were separated into single-input single-output in the modeling stage. Thus, the characteristics of DLFRM were defined separately in each model and the coupling effect was assumed to be minimized. There are four discrete SISO model of double link flexible manipulator were developed from torque input to the hub angle and from torque input to the end point accelerations of each link. An experimental work was established to collect the input-output data pairs and used in developing the system model. Since the system is highly nonlinear, NARX model was chosen as the model structure because of its simplicity. The nonlinear characteristic of the system was estimated using the ANN whereby multi-layer perceptron (MLP) and ELMAN neural network (ENN) structure were utilized. The implementation of the ANN and its’ effectiveness in developing the model of DLFRM was emphasized. The performance of the MLP was compared to ENN based on the validation of the mean-squared error (MSE) and correlation tests of the developed models. The results indicated that the identification of the DLFRM system using the MLP outperformed the ENN with lower mean squared prediction error and unbiased results for all the models. Thus, the MLP provides a good approximation of the DLFRM dynamic model compared to the ENN

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance