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The behavior of nonlinearity and time-varying cause the pneumatic actuator systems are difficult to be controlled. This paper proposes a Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems. The Fourier series-based functional approximation technique can approximate an unknown function, thus bypassing the model-based prerequisite. The learning laws for the coefficients...
State-feedback model predictive control (MPC) of discrete-time linear periodic systems with possibly time-dependent state and control input dimension is considered. States and inputs are subject to hard, mixed, polytopic constraints. It is described how discrete-time linear systems, both time-invariant and periodic, with multirate or multiplexed control inputs can be modeled as such periodic systems...
In this paper, based on a recent result on deterministic learning theory, we investigate learning from adaptive neural control for a class of discrete-time nonlinear systems. First, we use an adaptive neural control law without any robustification term to ensure the finite time tracking error convergence. With the tracking convergence of the system states to a periodic reference orbit, a partial PE...
A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an RWNN controller is designed to...
This paper presents experimental results validating a novel dual adaptive neuro-control scheme based on the unscented transform, for the dynamic control of nonholonomic wheeled mobile robots. This novel controller, recently proposed by the same authors, has so far been tested by simulations only. In contrast to the majority of adaptive control techniques hitherto proposed in the literature, the controller...
On-line system identification of linear time-varying (LTV) systems whose system parameters change in time has been studied lately. One neural network based such on-line identification method was studied by the author with a generalized adaptive linear element (ADALINE). Since the ADALINE is slow in convergence, which is not suitable for identification of LTV system, one technique was proposed to speed...
Deterministic learning control was investigated recently. Due to the existence of time varying disturbances, learning capability may be influenced. In this paper, deterministic learning theory is analyzed in environments with disturbances. With an appropriately designed neural adaptive controller, the disturbances are attenuated and partial persistent excitation (PE) condition for the radial basis...
This paper presents a simplified primal-dual neural network based on linear variational inequalities (LVI) for online repetitive motion planning of PA10 robot manipulator. To do this, a drift-free criterion is exploited in the form of a quadratic function. In addition, the repetitive-motion-planning scheme could incorporate the joint limits and joint velocity limits simultaneously. Such a scheme is...
Deterministic learning theory was presented and investigated recently. Due to the existence of time varying disturbances, learning capability may be influenced. In this paper, deterministic learning theory will be analyzed in environments with disturbances. With appropriately designed adaptive neural controller, the disturbances are attenuated and partial persistent excitation (PE) for radial basis...
This paper presents a novel robust adaptive trajectory linearization control (RATLC) method for a class of uncertain nonlinear systems based on a single hidden layer neural networks disturbance observer (SDO). The term ldquodisturbancerdquo used in this paper refers to the combination of model uncertainties and external disturbances. By utilizing the universal approximation property of neural networks...
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