提高模糊控制性能算法的研究

针对常规模糊控制器控制性能差等缺点,就提高模糊控制性能问题,探索了一些改进方法。通过仿真,获得了良好的控制效果。

ACS algorithm-based adaptive fuzzy PID controller and its application to CIP-I intelligent leg

Based on the ant colony system （ACS） algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-Ⅰ intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.

A simplified adaptive interval Type-2 fuzzy control in practical industrial application

Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control.However,heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application.By adopting novel piecewise fuzzy sets and center-average type-reduction,a simplified adaptive interval Type-2 fuzzy controller involving less computation is developed for practical industrial application.In the proposed controller,the inputs are divided into several subintervals and then two piecewise fuzzy sets are used for each subinterval.With the manner of piecewise fuzzy sets and a novel fuzzy rules inference engine,only part of fuzzy rules are simultaneously activated in one control loop,which exponentially decreases the computation and makes the controller appropriate in industrial application.The simulation and experimental study,involving the popular magnetic levitation platform,shows the predicted system with theoretical stability and good tracking performance.The analysis indicates that there is far less computation of the proposed controller than the traditional adaptive interval Type-2 fuzzy controller,especially when the number of fuzzy rules and fuzzy sets is large,and the controller still maintains good control performance as the traditional one.

Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.

Performance analysis of CDMA power control system based on fuzzy prediction

Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results, we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.