Earticle

The DC motor is a key element for servo system, and the control quality of control system would be seriously affected by the nonlinear, the parameter uncertain, the inaccurately modeling and some uncertain existing in servo system. So it is very difficult to satisfy control requirements by using the traditional control method. In this paper, the sliding mode control algorithm with reaching law is proposed. By using the reaching law to weaken the chattering phenomenon existed in the SMC, also by using the SMC method can enhance the anti-interference ability of the servo system. In order to test and verify the effectiveness of the proposed SMC with reaching law for the DC motor, the designed controller was dynamically simulated by using the MATLAB. It is shown from the simulation results that the proposed controller offers several advantages such as good chattering inhibition capability, fast response, good disturbance rejection capability, good position tracking capability and so forth. It is also revealed that the proposed control strategy is valid for the DC servo system from simulation results. The results also provide the certain theoretical basis and application value for engineering practice.

This paper proposes a hybrid learning algorithm for tuning an Adaptive Network based Fuzzy Inference System ANFIS. The proposed scheme of adapting parameters in ANFIS employs evolutionary techniques PSO and GA to adjust the antecedent parameters. The least-squares (LSE) algorithm is used to adjust consequent parameters. The number of fuzzy rules is fixed and given by using the Xie Beni’s index. This new approach is applied to identify and control nonlinear systems with an on-line strategy. The obtained results are compared to similar ANFIS using gradient descent method GD as antecedent parameters of training algorithm and other methods applied in the same problems.

In this paper, we propose a design method of nonlinear adaptive back-stepping controller. Many back-stepping controllers design that assume to control with the d-axis current 0[A], but the suggested controller design that is possible to control the place on that the d-axis current is not 0[A]. The d-axis current increases additional terms. This controller used an approximated term with not spoil the reliability of system to overcome the shortcomings. The proposed controller is tested through experiment with a 1-hp IPMSM, and is designed to have asymptotically stability and to take a precise tracking performance for different speed, and is robust to adapt real time on load change.

The high search speed and efficiency, and simple algorithm of particle swarm optimization algorithm make it suitable for actual-value processing. Starting from the angle of weight, this paper studies several improved particle swarm optimization algorithms and divides the improvement into three types as linear decreasing weight strategy, self-adaptive weight strategy and random weight strategy. Furthermore, this paper also demonstrates the principles of these three improved algorithms and tests and analyzes the three algorithms with test function. It is suggested by the result of tests that random weight strategy can make the algorithm more stable, linear decreasing weight strategy can improve the effect of optimization, while self-adaptive weight strategy can accelerate the convergence. However, the operation of self-adaptive weight strategy takes obviously more time than that of the other two strategies.

ACA (Ant Colony Algorithm) is a smart global random search algorithm, which is combined with blind equalization algorithm. In optimizing and initializing equalizer weight vector, ACA can avoid falling into local extremum easily in Stochastic Gradient Descent Algorithm of CMA and improve the convergence speed and reduce the steady-state mean square error greatly. This paper mainly studies the ACA to initialize the equalizer weight vector and the theory of wavelet transform, comes up with the wavelet blind equalization algorithm based on Ant Colony Optimization (ACA-WTCMA).The simulation results of underwater acoustic channel verify the effectiveness of the algorithm.

In this paper, we analyze the conducted noise level of a long cable considering a subsea environment. Because conducted noise causes distortion for a signal and power line, it must be removed. As it have a negative effect on system reliability. Thus, we analyzed the effects of the conducted noise, which is caused by the temperature at the depth of the sea. To design conducted noise, we transformed the effect of the temperature changes influencing the cable into an equivalent circuit, and co-simulated it with FEM (finite element model) tools. This method allowed us to propose a proper design method for a long cable and determine the relationship between the noise and temperature.

Sliding mode control (SMC) has demonstraed good robustness, and thus, it is widely used in many control systems. However, because it is not suitable for a plant with a time delay because it is not considered a time delay control method. In this study, to solve the above problem, we propose an advanced SMC technique for plants having time delays. In our method, we introduce a simple plant predictor to the SMC method. Our method is easy to design. In simulation studies, the results show that the proposed method is applicable to plants having time delays. In an experiment using a pneumatic actuator, we confirm the practicality of the proposed method.

Chattering is one of the main shortcomings of sliding mode control. In this paper method using observer to designing continuous sliding mode control (CSMC) law for a class of nonlinear uncertain systems with uncertainty in control channel is addressed. By constructing a robust observer based on LMI method, variable structure control (VSC) law can be designed on the derivative of the control signal. Consequently chattering phenomenon is eliminated. Simulation results on a numerical example are reported to illustrate the system performance and the feasibility of the control algorithm.