Sliding-mode neuro-controller for uncertain systems

In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results

Design of a Memristor-based Chattering Free Sliding Mode Controller and Speed Control of the BLDC Motor

In this study, a memristor-based sliding mode controller (Mem-SMC) was designed for speed control of BLDC motor and the performance of the controller was tested in simulation. The sliding mode controller, known for its robustness against disturbances and parameter variations, was designed with a memristor known as a missing circuit element. Simulation results show that the proposed controller is successful in the speed reference tracking and is also able to respond quickly to sudden changes in the reference

A comparison study of some control methods for delta spatial parallel robot

A comparison between three methods applied to parallel robot control namely: computed torque controller, sliding mode control and sliding mode control using neural networks is presented in this paper. The simulation results show that PD control method is only accurate when model parameters are precisely identified. In case of uncertain parameters, sliding mode and neural network sliding mode control methods are applied instead. Three controllers are implemented in Matlab for simulation. The results show that the control quality is improved by using the neural network sliding mode control method in comparison with two others.

Sliding mode robot controller parameter tuning with genetic algorithms and fuzzy logic

Sliding Mode Controllers (SMC) possess robustness properties under parameter uncertainties. Usually, a Lyapunov based controller design with a switching control signal constitutes the backbone of robustness. However, the ideally zero switching time of the controller output cannot be achieved in digital implementation. This causes a phenomenon called chattering – high frequency oscillations observed in systems state variables. Chattering also shows itself as high amplitude oscillatory behavior in the control signal. A chattering actuator output is not favorable for many plants, including robot manipulators driven by actuator torques. This problem is traditionally solved by smoothing the switching control output, deviating from the original mathematical foundations robustness. Over-smoothing causes performance deterioration, while too limited smoothing action may lead to the wear of the mechanical system components. This motivates the exploration of automatic tuning approaches which consider chattering and performance simultaneously. This thesis proposes two SMC smoothing and parameter tuning methods with soft computing (SC) methodologies. The first method is based on Genetic Algorithms (GA). SMC controller parameters, including the ones governing the smoothing action are tuned off-line by evolutionary computing. A measure is employed to assess the instantaneous level of chattering. The integral of this value combined with performance indicators including the rise time and steady state error in a step reference scenario are used as the fitness function. The method is tested on the model of a direct drive (DD) SCARA type robot, via simulations. The GA-tuned SMC is, however, tailored for a fixed reference signal and fixed payload. Different references and payload values may pronounce the chattering effects or lead to performance loss due to over-smoothing. The second SMC parameter tuning method proposed employs a fuzzy logic system to enlarge the applicability range of the controller. The chattering measure and the sliding variable are used as the inputs of this system, which tunes the controller output smoothing mechanism on-line, as opposed to the off-line GA technique. Again, simulations with the direct-drive robot model are employed to test the control and tuning method

Contribuições a Aplicações Práticas de Sistemas de Controle por Modos Deslizantes.

Este trabalho apresenta o desenvolvimento de duas abordagens com a finalidade de auxiliar o projeto e a aplicação prática de sistemas de controle por modos deslizantes. As metodologias foram desenvolvidas com o objetivo de se obter malhas de controle que apresentam as características de robustez relacionadas aos sistemas de controle por modos deslizantes em processos com comportamentos não lineares ou com variações de parâmetros e na presença de distúrbios externos. Buscou-se uma relativa simplicidade nos procedimentos de projeto e algoritmos de controle que diminuam as dificuldades associadas às aplicações práticas dessas estruturas de controle. Entre essas dificuldades, pode-se citar a atenuação ou eliminação do fenômeno conhecido como chattering, que é responsável por efeitos negativos nessas malhas de controle, tais como a excitação de dinâmicas não modeladas, a deterioração do desempenho do controlador, o aumento do desgaste em partes mecânicas móveis e perdas por dissipação de calor em circuitos elétricos de potência. Uma das contribuições deste trabalho corresponde ao desenvolvimento de um procedimento de sintonia para sistemas de controle por modos deslizantes em processos que possam ser aproximados por modelos de ordem reduzida (primeira ordem). O procedimento desenvolvido fornece meios para escolher os valores dos ganhos da lei de controle utilizada de modo que as malhas de controle resultantes apresentem respostas adequadas, evitando circunstâncias que podem resultar em sistemas com respostas lentas ou oscilatórias. Outra contribuição desenvolvida diz respeito à aplicação de conceitos da Teoria dos Conjuntos Aproximados na realização de estruturas de controle por modos deslizantes. O procedimento desenvolvido pode ser utilizado na substituição de estruturas mais complexas que empregam técnicas de inteligência artificial, resultando em algoritmos de controle mais simples e, portanto, de mais fácil realização prática. Para comprovar a viabilidade prática dos procedimentos desenvolvidos, são apresentados resultados de ensaios reais realizados em um sistema de nível em escala reduzida