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Passivity method is very effective to analyze large-scale nonlinear systems with strong nonlinearities. However, when most parts of the nonlinear system are unknown, the published neural passivity methods are not suitable for feedback stability. In this brief, we propose a novel sliding mode learning algorithm and sliding mode feedback passivity control. We prove that for a wide class of unknown nonlinear...
In order to minimize steady-state error with respect to uncertainties in robot control, the integral gain of PID control should be increased. Another method is to add a compensator to PD control, such as neural compensator, but the derivative gain of this PD control should be large enough. These two approaches deteriorate transient performances. In this paper, the popular neural PD is extended to...
This paper addresses the iterative tuning method of PID control for the robot manipulator based on the responses of the closed loop system. Several properties of the robot control are used, such as any PD control can stabilize a robot in regulation case, the colsed-loop system of PID control can be approximated by a linear system, and the control torque to the robot manipulator is linearly independent...
In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.
In this paper, adaptive hierarchical fuzzy CMAC neural network controller (HFCMAC), for a certain class of nonlinear dynamical system is presented. The main advantages of adaptive HFCMAC control are: Better performance of the controller because adaptive HFCMAC can adjust itself to the changing enviroment and can be implemented in real time applications. The proposed method provides a simple control...
In this paper, we present a new sliding mode controller for a class of unknown nonlinear discrete-time systems. We make the following two modifications: 1) the neural identifier which is used to estimate the unknown nonlinear system, applies new learning algorithms. The stability and non-zero properties are proved by dead-zone and projection technique. 2) We propose a new sliding surface and give...
Normal industrial PD control of overhead crane has two drawbacks, it needs joint velocity sensors; it cannot guarantee zero steady state error. In this paper we make two modifications to overcome these problems. High-gain observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate gravity and friction. We give a new proof for high-gain observer, which explains...
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