Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this ...Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this paper, which suffers from strong nonlinearities and system parameter uncertainties. QFT can reduce the plant uncertainties and stabilize the system, but it fails to obtain high-precision tracking. This drawback can be solved by a robust QFT control scheme based on zero phase error tracking control (ZPETC) compensation. The combined controller not only possesses high robustness, but greatly improves the system performance. To verify the effiectiveness and the potential of the proposed controller, a series of experiments have been carried out. Experimental results have demonstrated its robustness against a large range of parameters variation and high tracking precision performance, as well as its capability of restraining the load coupling among channels. The combined QFT controller can drive the radar truck leveling platform accurately, quickly and stably.展开更多
In order to meet tracking performance index of three-axis hydraulic simulator, based on classical quantitative feedback theory （QFT）, an improved QFT technique is used to synthesize controller of low gain and bandwi...In order to meet tracking performance index of three-axis hydraulic simulator, based on classical quantitative feedback theory （QFT）, an improved QFT technique is used to synthesize controller of low gain and bandwidth. By choosing a special nominal plant, the improved method assigns relative magnitude and phase tracking error between system uncertainty and nominal control plant. Relative tracking error induced by system uncertainty is transformed into sensitivity problem and relative tracking error induced by nominal plant forms into a region on Nichols chart. The two constraints further form into a combined bound which is fit for magnitude and phase loop shaping. Because of leaving out pre-filter of classical QFT controller structure, tracking performance is enhanced greatly. Furthermore, a cascaded two-loop control strategy is proposed to heighten control effect. The improved technique＇s efficacy is validated by simulation and experiment results.展开更多
A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross...A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output MIMO plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output SISO generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.展开更多
文摘Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this paper, which suffers from strong nonlinearities and system parameter uncertainties. QFT can reduce the plant uncertainties and stabilize the system, but it fails to obtain high-precision tracking. This drawback can be solved by a robust QFT control scheme based on zero phase error tracking control (ZPETC) compensation. The combined controller not only possesses high robustness, but greatly improves the system performance. To verify the effiectiveness and the potential of the proposed controller, a series of experiments have been carried out. Experimental results have demonstrated its robustness against a large range of parameters variation and high tracking precision performance, as well as its capability of restraining the load coupling among channels. The combined QFT controller can drive the radar truck leveling platform accurately, quickly and stably.
文摘In order to meet tracking performance index of three-axis hydraulic simulator, based on classical quantitative feedback theory （QFT）, an improved QFT technique is used to synthesize controller of low gain and bandwidth. By choosing a special nominal plant, the improved method assigns relative magnitude and phase tracking error between system uncertainty and nominal control plant. Relative tracking error induced by system uncertainty is transformed into sensitivity problem and relative tracking error induced by nominal plant forms into a region on Nichols chart. The two constraints further form into a combined bound which is fit for magnitude and phase loop shaping. Because of leaving out pre-filter of classical QFT controller structure, tracking performance is enhanced greatly. Furthermore, a cascaded two-loop control strategy is proposed to heighten control effect. The improved technique＇s efficacy is validated by simulation and experiment results.
基金the National Natural Science Foundation of China (No.60374037, No.60574036)the Program for New CenturyExcellent Talents in Education Ministry (NCET)the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013)
文摘A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output MIMO plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output SISO generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.
