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A novel adaptive controller based on sliding mode control with Fourier series approximation approach is presented. To eliminate the impact of time variant characteristics of dead zone, a control law is proposed with an undetermined uncertainty item and an undetermined compensation item through sliding control method, which are then approximated with Fourier series. Lyapunov method is used to guarantee...
A B-spline neural-network-based variable structure controller (VSC) for a class of nonlinear systems with parameter uncertainties is presented. The neural networks compensator is used to compensate the uncertainties in the systems. The tracking performance and stability is guaranteed by the VSC controller. The asymptotical stability of overall systems is verified by the Lyapunov stability criterion...
A novel robust Hinfin decentralized intelligent control (RHDIC) strategy is proposed for the trajectory following problem of robot manipulators. The proposed system is comprised of a computed torque controller and neural robust controller with new learning algorithm. Based on Lyapunov stability theorem, it is shown that the proposed controller can guarantee stability of closed-loop systems and satisfactory...
According to the nonlinear closed electro-hydraulic proportional system, a multiple sliding mode adaptive control method is proposed. Recursive technique is used to simplify the control problem of higher-order system. A robust adaptation scheme was used to reduce the tracking error resulted from system uncertainties . Firstly, the recursive technique and state feedback precise linearization scheme...
Direct adaptive sliding mode tracking control law design method and its application to an inverted pendulum system have been presented in this paper. Firstly, problem formulation and the introduction of dynamic fuzzy logical system (DFLS) were given. Then the sliding mode control (SMC) law was constructed via DFLS which parameters were self-tuned on-line. The adaptive laws were determined so that...
The position tracking control of a missile electrohydraulic servo mechanism is studied. Since the dynamics of the system are highly nonlinear and have large extent of model uncertainties, such as big changes in load and parameters, a design method of adaptive fuzzy sliding mode control is presented. The adaptive fuzzy controller is introduced to approach the equivalent control of sliding mode control,...
A new stable sliding mode control method for a class of underactuated mechanical systems with saturation is proposed in this paper. Two state variables of the underactuated system are chosen to construct the first-layer sliding surface. Then, the first-layer sliding surface and one of the left state variables are used to construct the second-layer sliding surface. This process continues till the last...
Control of pneumatic servo systems is well-known to be challenge due to highly nonlinearities in the actuator dynamics and compressibility of the media. Most state feedback based strategies for the pneumatic servo systems require full state feedback to stabilize the closed loop system which implies the need for pressure measurements of the cylinder. Many researchers used state observers to complete...
Synchronization of uncertain Chen chaotic systems is discussed. Based on active control and virtual feedback control strategies, a novel active sliding mode controller is presented, which can ensure quick synchronization of uncertain chaos system starting from any initial condition. According to Lyapunov stable theory, the synchronization robust of uncertain chaos system is proved. A numerical simulation...
Aiming at the uncertain nonlinear system with a dead zone input, a design method of adaptive neuro sliding mode control is presented to combine neural network theory with sliding mode control theory. RBF neural networks are used to realize modeling of nondeterministic system. Adaptive laws are derived based on Lyapunov stability theory which guarantees the stability of control system. Theoretical...
A new learning algorithm for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions is proposed. The concept of exponential fast terminal sliding mode is introduced into the learning algorithm to improve approximation ability. The Lyapunov stability analysis guarantees that the approximation is stable and converges to the unknown function with improved speed. The...
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