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This paper presents a method of continuous-time simple adaptive control (SAC) using multiple neural networks for a single-input single-output (SISO) nonlinear systems with unknown parameters and dynamics, bounded-input bounded- output, and bounded nonlinearities. The control input is given by the sum of the output of the simple adaptive controller and the sum of the outputs of the parallel small-scale...
Sliding mode control (SMC) has a strong capability of controlling nonlinear systems with uncertainties. However, the discontinuous control signal causes the significant problem of chattering. Furthermore, it requires thorough knowledge of the parameters and dynamics of the controlled plant, which are difficult to be obtained or may be unknown, to calculate the equivalent control law of SMC. In this...
This paper presents the fuzzy PID (FPID) controller using neural network (NN) for controlling the magnetic levitation system. Magnetic levitation systems are open loop unstable, uncertainly and inherently nonlinear systems. Consequently, controlling this kind of the system is very difficulty. The FPID controller is developed to provide nonlinear or linear control action that can improve performance...
Sliding mode control (SMC) has a strong capability in controlling nonlinear systems with uncertainties. However, SMC requires thorough knowledge of the controlled plant parameters and dynamics that is difficult to be obtained or may be unknown, which causes difficulties in calculating the equivalent control law of SMC. To overcome this problem, this paper proposes an adaptive SMC using simple adaptive...
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