Study On Biological-Intelligent-based Motion Control For Six Degree-of-Freedom Parallel Mechanism And Its Simulation

Due to its advantages of high rigigty,large payload and high precision,six degree-of-freedom parallel mechanism has been applied in many industries widely,so more requests are proposed for its motion prcision and velocity,and intelligent and real-time control.At the same time,there exist shortcomings such as nonlinearity and strong coupling dynamics,and difficulty of solving forward kinematics for it,undoubtedly,it is hard to control six degree-of-freedom mechanism because of its drawbacks.In the dissertation,on the groundwork of research on positioning methods,some control strategies based biological regulation were presernted for it.Firstly,the progress about six degree-of-freedom machine is summarized,which includes research hotpoint,control strategies and direct kinematics solution approaches,then the current existing difficulties and the future development of it are pointed out.The forward kinematics method attaching electronic compass to Stewart platformis presented to overcome its direct kinematics difficulty.After the attachment of electronic compass to Stewart platform,its kinematics can be reduced to linearity and the dimension can be decreased,so the computation quantity can be cut down and forward kinematics would be confirmed uniquely,then the method supplies reference to other parallel manipulator.Gaussian white noise disturbance mutation is introduced to particle swarm optimization algorithm to improve its diversity and velocity in the last evolution progress.When particle swarm optimization with Gaussian white noise disturbance mutation is applied to search forward kenimatic solution,the solution can be gotten quickly and stably.To make full use of the redundant information in parallel machine,another forward kinematics method based on data fusion of multi-sensor is proposed to solve its dynamic problem according to probability theory,where electronic compass,camera and coder are mounted.Compared with sigle sensor measurement,the method has higher precision to lay the foundations of motion control without acquance of its kinematics model.At the same time, the drawbacks of all the sensors in the system are compensated,and the method can track the input dynamically,so the method is able to decrease the system price under the same positioning precision and equipment requirements as well.An advanced algorithm(PCA) with the ability of global searing is introduced to optimization functions by combining the clonal selection mechanism of the immune system with the evolution equation of particle swarm optimization.In the method,the eolution equation is used to direct the mutation,and the clonal selection mechanism to increase the diversity of the antibody populations.Finally,a optimal PID controller is designed by PCA,and it has the capability to satisfy the control requirements of the time-variant,nonlinear systems by adjusting its parameters on-line.With relation to the performance requirements of control system,an improved immune controller is presented based on immune feedback mechanism of biological system,by modifying the helpful cell model and simplifying the constraint cell model according to actual immune system.Simulation experiments witness its better dynamic control effects and anti-disturbance ability.Cosidering the nonlinearity and strong coupling of six degree-of-freedom parallel machine,inspired by biological co-evolutionary mechanism,a co-evolutionary control strategy is presented to fit to its dynamic performance.The whole solution is produced by cooperating among the real number populations constructed in virtue of the cooperative co-evolutionary model.The range of subpopulation individuals is regulated according to control properties,and mutation probability of the subpopulation is adjusted dynamically in the process of evolution.Compared with single genetic algorithm(SGA),the co-evolutionary control strategy has higher precision,at the same time,it emobodies the paraUelity of co-eolutionary model,so it has better computing ability.Due to strong coupling and difficulty of building precise dynamic model of Stewart platform,firstly,dynamic recurrent neural network(DRNN) is adopted to identify its model considering the ability of using history information and the one of dynamic approximating, then decoupling control over Stewart platform is done by designing reasonable fitness function.A mutation co-evolution clone algorithm(MCCA) is presented.In the MCCA,the mutator population is be able to co-evolute with the antibody population to ensure the direction of antibody evolution.MCCA is adopted to train the DRNN to promote the ability of global searching and execution velocity.Finally,on the the identified and actual platform,the effects of control strategy and identification is test respectively.Additionally, the decoupling control strategy can be generalized to be applied to other nonlinear,time variant,coupling objects also.The novel 6-PSS parallel manipulator is used as application object,and the motion control simulation platform is built for it.The control algorithms mentioned afront are tested on the simulation platform compositively.On the simulation platform,dynamics and control strategies of six degree-of-freedom plarallel manipulator can be researched,and it lays strong foundation of the future study of parallel manipulators.At last,a summary of the thesis is made,and the deficiency in the project and the further development are narrated respectively.