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This paper considers the finite-time controller design for wheeled mobile robots. After the mobile robot dynamics have been transformed into an advantageous form, called the heisenberg form, a finite-time controller is designed in order to promulgate the practical stability of the system. The desired stability property of the closed-loop system is provided by applying a homogeneous sliding mode controller,...
In this paper, a consensus problem for a group of n agents is considered. A switching type protocol, or law, is proposed such that the states of all the agents agree upon an average value in finite-time. Sliding mode principles have been used to achieve finite-time average consensus. Specifically the sliding surface is defined as the set corresponding to consensus, on which the sliding motion is obtained...
This paper presents a closed-loop automatic method able to identify all electrical parameters of single phases induction machines. The system is based on Robust Model Reference Adaptive Controller that is applied at qd axes machine model. The identification procedure is based on currents measurements with standstill rotor and is divided in two times: in the first time, all parameters of main winding...
The boundary control problems associated to a semilinear elliptic equation defined in a curved domain Ω are considered. The Dirichlet and Neumann cases are analyzed. To deal with the numerical analysis of these problems, the approximation of Ω by an appropriate domain Ωh (typically polygonal) is required. Here, we do not consider the numerical approximation of the control problems. Instead of it,...
We consider an optimal control problem of MIMO systems affected by external disturbances with known dynamic characteristics but unknown initial conditions based on stability degree constraint. The objective is to find an optimal control, by which the cost function minimum and the state with the optimal control having a higher mean-square convergence rate can be obtained. A feedforward and feedback...
With unknown control gain function and plant bound functions, a H∞ direct adaptive fuzzy controller (AFC) for a class of single-input single-output (SISO) uncertain affine nonlinear systems under external disturbance is proposed. A conventional fuzzy logic system (FLS) is used to approximate the control gain function, and a FLS with variable universes of discourse, which has the characteristic of...
For the continuous chaotic system, using Lyapunov method, when the controlled system satisfies Lipschitz conditions, the generalized synchronization of chaotic systems with different structures control strategy is proposed based on tracking control method, you can make two different structure of the output of chaotic system state in order to exponentially fast convergence to any given reference signal...
In this paper, Covariance Matrix Adapted Evolution Strategy (CMAES) algorithm based design of robust optimal fixed structure controllers for systems with uncertainties and disturbance is addressed. PID controller and second order polynomial structure are considered for fixed structure controller. The design problem is formulated as minimization of maximum value of real part of the poles subject to...
Internet congestion control system is complex, uncertainty and nonlinear. An Improved PID Neural Network (I-PID-NN) controller with changing integration rate and incomplete derivation in hidden layer is applied in active queue management (AQM). The adjustment of neural network parameters are implemented by using gradient algorithm as learning the rules, and the probability of packet loss can achieve...
This paper presents a general form for the fast terminal sliding mode (FTSM). It is shown that the proposed form not only includes the conventional FTSM, but also provides new types of FTSM which can have a faster convergence rate than the conventional one. More importantly, the convergence time of the new FTSM models is bounded for any initial value, which means an upper bound of the convergence...
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters. To overcome premature of standard PSO algorithm, a modified PSO (MPSO) based on partial...
A nondeterministic algorithm that mimics the foraging behavior of ants to solve difficult optimization problems is known as Ant Colony Optimization (ACO). One of the most important problems in ACO is stagnation. Early convergence to a small region of the search space leaves its large sections unexplored. On the other hand, very slow convergence cannot sufficiently concentrate the search in the vicinity...
Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network (NN) structure has evolved as a powerful alternative technique that eliminates the need for excessive computations and storage requirements in solving optimal control problems. A typical AC structure consists of two interacting NNs. In this paper, a new architecture, called the “Cost Function Based Single Network...
Iterative learning control (ILC) is a technique to make use of the repetitiveness of the tasks, a system is commanded to execute in a fixed finite time interval. In this paper, we assume that the system to be controlled is discrete time and described by linear state space equations. We present a proportional, integral, derivative (PID) type ILC updating law with variable coefficients. An optimal procedure...
As a benchmark control problem with nonlinearity and instability, the controller design for inverted pendulums becomes more difficult when there are model uncertainties and unknown disturbances in the plant dynamics. In this paper, a kernel-based reinforcement learning controller is developed for inverted pendulums with unknown dynamics and stochastic disturbances. The learning controller makes use...
This paper presents the design of a robust iterative learning controller for a class of nonlinear systems with non-parametric uncertainties. The unknown nonlinearities are only required to satisfy the local Lipschitz condition. The learning controller is developed, by using both robust control and iterative learning control methods, which enables to compensate for the shortcoming when either one is...
Using the known periodicity of the given trajectory, we develop a new dynamical linearization method by introducing a concept of PPD, then present a new model-free periodic adaptive control approach (MFPAC) and its extension of higher-order learning control algorithm for a class of general nonlinear and non-affine discrete-time systems. It is model-free in nature and the controller design and analysis...
In this paper, we proposed a new extremum seeking predictive control scheme, in which a numerical optimization algorithm is first employed to provide a search sequence of the unknown extremum, then a predictive controller using the search sequence as its terminal constrain is designed and implemented in a receding horizon manner to simultaneously achieve the optimization of economic objective and...
The Attracting Ellipsoid Method is applied for the design of sliding mode controllers for linear systems subjected to matched and unmatched perturbations. The existence of first order actuators is considered and their time constants are designed to provide quasi-optimal practical stability. The technique is based on the existence of an attracting (invariant) ellipsoid such that the convergence to...
Observation problem for systems governed by Partial Differential Equations (PDE) has been a research field of its own for a long time. In this paper it is presented an observer design for a class or parabolic PDE's using sliding modes theory and bacstepping-like procedure in order to achieve exponential convergence. A Volterra-like integral transformation is used to change the coordinates of the error...
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