Muhammad Yasser

Neuro-sliding mode control for magnetic levitation systems

It is well known that sliding mode control (SMC) is capable of tackling systems with uncertainties. However, the discontinuous control signal causes the significant problem of chattering. Furthermore, thorough knowledge of the plant dynamics may be unknown or difficult to obtain, which makes it difficult to calculate the control law. A synergistic combination of neural network (NN) and SMC methodology is proposed. The network weights are adjusted using a modified online error backpropagation algorithm. Moreover, a new and simple approach is utilized to construct corrective controls of SMC to overcome the chattering problem. As a result, chattering is eliminated and the error performance of SMC is also improved. Experimental studies carried out on a magnetic levitation system are presented.

Implementation of a Fuzzy PID Controller Using Neural Network on the Magnetic Levitation System

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 of the controller in comparison with a conventional PID controller using only linear policy. Unfortunately, since FPID controller are nonlinear, it is more difficult to set the controller gains compared the linear PID controller. In this paper we propose a neural network to assist the FPID controller. The NN is added in parallel with FPID controller. The NN is used to compensate for inadequate FPID parameters and for stabilize the magnetic levitation system. The uniqueness our method is when the parameters of FPID are incorrect, then the NN takes over the controller, otherwise the NN does not operate. Online training and fast computing of the NN has been designed for that purposes. Finally, the experiment results showed the effectiveness of the proposed method

Simple adaptive control for SISO nonlinear systems using multiple neural networks

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 neural networks. The parallel small-scale neural networks are used to compensate the nonlinearity of plant dynamics that is not taken into consideration in the usual SAC. The role of the parallel small- scale neural networks is to construct a linearized model by minimizing the output error caused by nonlinearities in the control systems. Finally, the stability analysis of the proposed method is carried out, and the effectiveness of this method is confirmed through computer simulations.

Adaptive sliding mode control using simple adaptive control for SISO nonlinear systems

Sliding mode control (SMC) has a strong capability of controlling nonlinear systems with uncertainties. However, it requires thorough knowledge of parameters and dynamics of the controlled plant, which are difficult to be obtained or may be unknown. This will cause difficulties in calculating equivalent control law of SMC. To overcome this problem, this paper proposes an adaptive SMC using simple adaptive control (SAC) developed for single-input single-output (SISO) nonlinear systems with unknown parameters and dynamics. The role of SAC is to construct an equivalent control input of adaptive SMC. To construct a corrective control input, this paper applies the method using the sign function with a modified sliding surface. Finally, the effectiveness of the proposed method is confirmed through computer simulation

An Adaptive Sliding Mode Control Using Simple Adaptive Control for A Class of SISO Nonlinear Systems with Bounded-Input Bounded-Output and Bounded Nonlinearity

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 control (SAC) developed for a class of single-input single-output (SISO) nonlinear systems with unknown parameters and dynamics, bounded-input bounded-output (BIBO), and bounded nonlinearity. The role of SAC is to construct an equivalent control input of adaptive SMC. To construct a corrective control input, this paper applies the sign function with a modified sliding surface. Finally, the stability analysis of the proposed method is carried out, and the effectiveness of this method is confirmed through computer simulations

Sliding mode control using neural networks for SISO nonlinear systems

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 paper, a combination of SMC and neural network (NN) is proposed. The weights of NN are adjusted using a backpropagation algorithm. To construct corrective control law of SMC for overcoming the chattering problem, a new and simple approach using a simplified distance function with a modified sliding surface is utilized. Thus, the chattering is eliminated and the performance of SMC is improved. Finally, a brief stability analysis of the proposed method is carried out, and the effectiveness of this method is confirmed through computer simulations.

Sliding Mode Control Using Neural Networks

Variable structure control with sliding mode, which is commonly known as sliding mode control (SMC), is a nonlinear control strategy that is well known for its robust characteristics (Utkin, 1977). The main feature of SMC is that it can switch the control law very fast to drive the system states from any initial state onto a user-specified sliding surface, and to maintain the states on the surface for all subsequent time (Utkin, 1977), (Phuah et al., 2005 a). The conventional SMC has two disadvantages (Ertugrul & Kaynak, 2000), (Slotine & Sastry, 1983), which are the chattering phenomenon (Slotine & Sastry, 1983), (Young et al., 1999) and the difficulty in calculating the equivalent control law of SMC that requires a thorough knowledge of the parameters and dynamics of the nominal controlled plant (Ertugrul & Kaynak, 2000), (Slotine & Sastry, 1983), (Hussain & Ho, 2004). Many methods of SMC using neural networks (NN) have been proposed (Phuah et al., 2005 a), (Ertugrul & Kaynak, 2000), (Hussain & Ho, 2004), (Phuah et al., 2005 b), (Yasser et al., 2007), (Topalov et al., 2007). In this paper, sliding mode controls using NN are proposed to deal with the problem of eliminating the chattering effect and the difficulty in calculating the equivalent control law of SMC that requires a thorough knowledge of the parameters and dynamics of the nominal controlled plant. The first method of this method applies a method using a simplified form of the distance function proposed in (Phuah et al., 2005 a), (Phuah et al., 2005 b). Furthermore, the simplified distance function of our method uses a sliding surface in the space of the output error and its derivations, as proposed in (Yasser et al., 2006 a), (Yasser et al., 2006 c), instead of the space of the states error to construct a corrective control input. Thus, no observer is required in the proposed method. Moreover, we also propose the application of an NN to construct the equivalent control input of SMC. The weights of the NN are adjusted using a backpropagation algorithm as in (Yasser et al., 2006 b). Hence, a thorough knowledge of the parameters and dynamics of the nominal controlled plant is not required for calculating the equivalent control law. Finally, a stability analysis is carried out, and the effectiveness of this first control method is confirmed through computer simulations. This first method has been previously discussed in (Yasser et al., 2007). The second method of this paper applies an NN to produce the gain of the corrective control of SMC. Furthermore, the output of the switching function the corrective control of SMC is applied for the learning and training of the NN. There is no equivalent control of SMC is used in this second method. As in the first method, this second method applies a method using a sliding surface in the space of the output error and its derivations, as proposed in