H∞ and QFT robust control designs for level control plant

Robust µ control and repetitive control for precision motion control of a pneumatic actuating table

“…By setting uncertain bounds in the model and adjusting appropriate weightings, this technique offers an acceptable trade-off between performance and robustness, which is suitable for servo design of uncertain and complex pneumatic systems. [7][8][9][10][11][12][13][14] A nominal plant model is mandatory in the H N control design process and can be obtained by performing standard system identification techniques. 14,16 The nonlinearities and unmodeled dynamics can be simply treated as model uncertainties to formulate robust control problems.…”

“…For enhanced system ID results and Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan control performance, another common technique is to close the original open-loop system for a guaranteed stable model by first applying a classical feedback controller. 11 Studies have shown that the improved system robustness can be justified through designed experiments such as injecting external disturbances, 9,14,16 switching the system operating regions, 11 and changing system payloads 2,15 during step-command tracking. 10,12 If position control is the primary focus for investigation, conducting periodic tracking experiments is a common performance index to verify the effectiveness of the applied control strategies.…”

“…Recent studies regarding robust control of pneumatic actuating systems can be found in three primary groups: adaptive robust control, 1,2 sliding mode control, 3–6 and H ∞ control-related techniques. 7–14 To perform state feedback control based on a limited output measurement, deriving an accurate mathematical model or adding extra sensors is necessary for the first two methods. However, because air compressibility and thermal influences in parameters change, for implementation, building a perfect model to perform such model-based design is difficult.…”

“…By setting uncertain bounds in the model and adjusting appropriate weightings, this technique offers an acceptable trade-off between performance and robustness, which is suitable for servo design of uncertain and complex pneumatic systems. 7–14…”

Quantitative Feedback Theory control design using particle swarm optimization method

“…QFT emphasizes the use of feedback to reduce the effects of model plant uncertainty and to satisfy the desired system performance specifications despite plant uncertainty and plant disturbance. QFT highlights the trade-off among the simplicity of the controller structure, the minimization of the cost of feedback, the model uncertainty and the achievement of the desired performance specifications at every frequency of interest (Garcia-Sanz, 2006;Kouhi et al, 2008;Yang, 2009;Yan, 2009). On the other hand, the evolutionary-based and genetic-based automatic loop shaping for QFT control were applied by Molins and Garcia-Sanz (2009), Chen et al (1999) and Meng and Xue (2009).…”