Intelligent Control System Of 3D Pneumatic Microgravity Simulation Platform

One of the key parts of the space robot research project is to develop microgravity simulation platform on the ground in order to test the performance of space robot. The existing microgravity simulation systems are introduced and compared in this paper. To test the performance of the developing space robot, two experimental platforms are proposed for simulating NANO six-degree of freedom floating satellite. The paper only copes with the subordinate simulation systems in the vertical direction, and it focuses on the intelligent control parts of the subordinate systems.The microgravity simulation systems are with strong nonlinearity. The nonlinearity of active simulation system is induced by friction force, compressible air, and the proportional valve. The semi-active simulation system is also a complicated system with strong nonlinearity. Its nonlinearity is introduced by friction force, parameters varying and the random pushing force of air-cylinder. Being air compressible, the inherent frequency of simulation system is very low, which makes the system's anti-disturbance performance much worse. The system's accuracy and dynamic performances become much worse for nonlinear friction force. Also, the parameters of simulation system are variable, which also worsen the control system. It's a great challenge to design control systems for such complicated systems. The traditional algorithms are based on accurate models, yet it's very difficult to develop accurate models for the simulation systems.Radial Basic Function Neural Network (RBFNN) is good at self-learning, self-organization and approximating the nonlinear system. It has been widely used in nonlinear system identification and control. Whereas, RBFNN is newly proposed, it should be improved continuously. Some new algorithms for RBFNN are proposed to improve the network's accuracy, stability, generalization performance and converging speed. Hybrid controllers based on RBFNN and traditional algorithms are proposed for the simulation systems. The simulation and experimental results show that these control algorithms are effective. The control systems are with good dynamic performance, robustness and self-adaptive performance.The paper focuses on the following parts:1. An active pneumatic servo scheme is adopted to simulate a NANO satellite how to float in the space. The mechanics, kinematics and hydromechanics of the active simulation system are analyzed. And some experiments are carried out on the simulation system. The experimental results show that the active system can performs well when the frequency of grasping force is lower than 4Hz. Yet it performs much worse at the frequency of grasping force higer than 4Hz. To meet the requirement of the hi-frequncy grasping force, an improved scheme is adopted in the paper, which is a semi-active microgravity simulation system. The mechanics, kinematics of semi-active system are analyzed. The dynamic performance of the semi-active simulation system is much better than that of active simulation system.2. The tense sensor is suffered strong electromagnetic interference and mechanic vibration interference. Some proper signal processing means are proposed for the signal of tense sensor. To balance the contradiction between the test precision, stability and dynamic response time of traditional butterworth low pass filter, the key peak points of butterworth filter are reset. The experimental results show that the new butterworth filter performs much better than the traditional one. It acquires the useful signal of tense sensor successfully.3. It's very difficult to determine the parameters of hidden layer of RBFNN. A new clustering algorithm RPCCL based on samples' density is proposed in the paper, which can determine the clustering number automatically, rapidly and accurately. It's also successfully applied in determining the parameters of hidden layer of RBFNN.4. A new Hybrid PSO algorithm based on conjugate gradient-descent algorithm is proposed for strong nonlinear system. The proposed hybrid PSO algorithm is good at global searching. It can also determine the best solution accurately and rapidly. Compared with the standared PSO algorithm, the proposed algorithm converges faster and more accurately, and it performs well in optimizing the weights of RBFNN.6. A new online sequential learning algorithm based on extreme learning machine is proposed for RBFNN. The learning mode of the proposed algorithm is very flexible, which can learn data not only one-by-one but also chunk -by-chunk. Compared with other online sequential algorithms, the proposed algorithm can converge faster and more accurately.7. The generalization performance of RBFNN is analyzed in the paper. To improve its generalization performance, a new active learning algorithm based on local generalization error is proposed. The proposed algorithm takes the localized generalization error as criterion for selecting the next sample. It takes full use of the previous knowledge, which makes the generalization performance of RBFNNmuch better.8. Control algorithms based on RBFNN are proposed for the microgravity simulation system. RBFNN is employed to approximate and compensate the uncertainties of active simlation system. It works as a feed-forward compensator to make the tracking error convergence fast. The sliding mode controller is employed to obtain robustness of the system for random disturbance and approximation error of RBFNN. The experimental results show that the control algorithm is effective. It produces good dynamic performance, sound robustness and good self-adaptive capacity. RBFNN is employed to approximate the nonlinear system of semi-active simulation system, and the parameters of PID controller is adjusted online according to the approximating result of RBFNN. The simulation and experimental results also show that the control algorithm is effective.