NEURAL NETWORK PREDICTIVE CONTROL WITH HIERARCHICAL GENETIC ALGORITHM

A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.

Genetic Optimization Algorithm of PID Decoupling Control for VAV Air-Conditioning System

Variable-air-volume (VAV) air-conditioning system is a multi-variable system and has multi coupling control loops. While all of the control loops are working together, they interfere and influence each other. A multivariable decoupling PID controller is designed for VAV air-conditioning system. Diagonal matrix decoupling method is employed to eliminate the coupling between the loop of supply air temperature and that of thermal-space air temperature. The PID controller parameters are optimized by means of an improved genetic algorithm in floating point representations to obtain better performance. The population in the improved genetic algorithm mutates before crossover, which is helpful for the convergence. Additionally the micro mutation algorithm is proposed and applied to improve the convergence during the later evolution. To search the best parameters, the optimized parameters ranges should be amplified 10 times the initial ideal parameters. The simulation and experiment results show that the decoupling control system is effective and feasible. The method can overcome the strong coupling feature of the system and has shorter governing time and less over-shoot than non-optimization PID control.

The Realization of the Most Economical and optimized Control System

In order to plow an access to low cost automation, the method to set up the most economical and optimized control system is studied. Such a system is achieved by adopting the field bus technologies based on net connection to form the hierarchical architecture and employing genetic algorithm to intelligently optimize the parameters of the topology structure at the field execution level and the parameters of a local controller. Praxis has proved that this realization can shorten the system development cycle, improve the system's reliability, and achieve conspicuous social economic benefits.

Neuron PI control for semi-active suspension system of tracked vehicle

A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.

Advanced in power control for distributed generation system using cooperative algorithms

To develop efficient power control strategies for a distributed generation system in order to improve the overall system efficiency, we propose a cooperative algorithm to analyze and design the controller, in which elements of conventional mathematical optimization algorithms are combined with adaptive dynamic elements drawn from intelligent control theory. In our design, the sequential quadratic programming algorithm was first utilized to obtain an optimal solution for power distribution among multiple units. Fuzzy system was then developed to implement the optimal strategies on the basis of optimal solution. In addition, parameters of the fuzzy system were adapted via a genetic algorithm. Tbe simulation results illustrate that the methodology described is useful for a range of control system designs.

An Adaptive Genetic Algorithm for Multiprocessor Real-time Task Scheduling

Real-time task scheduling is of primary significance in multiprocessor systems.Meeting deadlines and achieving high system utilization are the two main objectives of task scheduling in such systems.In this paper,we represent those two goals as the minimization of the average response time and the average task laxity.To achieve this,we propose a genetic-based algorithm with problem-specific and efficient genetic operators.Adaptive control parameters are also employed in our work to improve the genetic algorithms' efficiency.The simulation results show that our proposed algorithm outperforms its counterpart considerably by up to 36% and 35% in terms of the average response time and the average task laxity,respectively.

Cloning and characterization of a gene encoding cysteine proteases from senescent leaves of Gossypium hirsutum

A gene encoding a cysteine proteinase was iso- lated from senescent leave of cotton (Gossypium hirsutum) cv liaomian No. 9 by utilizing rapid amplification of cDNA ends polymerase chain reaction (RACE-PCR), and a set of con- sensus oligonucleotide primers was designed to anneal the conserved sequences of plant cysteine protease genes. The cDNA, which designated Ghcysp gene, contained 1368 bp terminating in a poly(A)+ trail, and included a putative 5 (98 bp) and a 3 (235 bp) non-coding region. The opening reading frame (ORF) encodes polypeptide 344 amino acids with the predicted molecular mass of 37.88 kD and theoretical pI of 4.80. A comparison of the deduced amino acid sequence with the sequence in the GenBank database has shown consider- able sequence similarity to a novel family of plant cysteine proteases. This putative cotton Ghcysp protein shows from 67% to 82% identity to the other plants. All of them share catalytic triad of residues, which are highly conserved in three regions. Hydropaths analysis of the amino acid se- quence shows that the Ghcysp is a potential membrane pro- tein and localizes to the vacuole, which has a transmembrane helix between resides 7 —25. A characteristic feature of Ghcysp is the presence of a putative vacuole-targeting signal peptide of 19-amino acid residues at the N-terminal region. The expression of Ghcysp gene was determined using north- ern blot analysis. The Ghcysp mRNA levels are high in de- velopment senescent leaf but below the limit of detection in senescent root, hypocotyl, faded flower, 6 d post anthesis ovule, and young leaf.