Adaptive Backstepping Control of a Pneumatic System With Unknown Model Parameters and Control Direction

Abstract

The performance of the pneumatic elements and the micro-controller steadily increases as the price of them decreases. This trend promotes high performance tracking control research on pneumatic servo systems. However, it is very difficult to obtain exact model parameters, which is one of the main obstacles to design a high performance controller. Moreover, in some application cases, the control direction is undetermined because of the possible false or incorrect valve outlets connection. At the same time, the inaccuracy of the proportional valve zero points also degrades the tracking performance of pneumatic servo systems. In this paper, an adaptive backstepping control approach is proposed for a pneumatic position servo system considering unknown model parameters, unknown control direction, and inaccurate valve zero points. The proposed method combines the Nussbaum function and backstepping technique to design a position tracking controller of a pneumatic servo system with the aforementioned uncertainties. By using the Lyapunov method, the designed controller is proved to be stable, and the tracking error asymptotically converges to zero. The experimental results demonstrate the effectiveness and the superiority of the proposed approach as compared with some existing methods, even for negative direction case for which the other methods fail. The performance of the proposed controller is further improved by considering the proportional valve zero points. INDEX TERMSPneumatic system, unknown model parameters, inaccurate proportional valve zero point, Nussbaum function, unknown control direction.

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“…However, the applied control voltage was not switched at the maximum values of +5 V and −5 V. Thus, the performance and lifetime of the pneumatic servo valve were within the requisite tolerance. Table 3 compares this study’s results, against those of [12] – [14] , for the intelligent parameter adjustment feature with sliding-mode controller—for the S curve and polynomial trajectory. Relative to other controllers, this study’s controller performed best, with a root mean square error (RMSE) of 0.1563 mm for the real-time path tracking servo system.…”

“…[13] 0.2248 Ref. [14] 0.5873 Proposed method 0.1563 Figure 10 presents the results for the circular tracking path of the fabric-clamping platform. According to the results, the fabric-clamping platform moved from (X, Y) = (0 mm, 0 mm) to (X, Y) = (100 mm, 200 mm) in 8 s along fifth-order polynomial trajectories for each axis.…”

“…Saravanakumar et al [11] reviewed recent research trends in servo pneumatic positioning systems; they focused on methods for diminishing nonlinearity in the pneumatic system to make the pneumatic servo system more efficient and accurate. Ren et al [12] – [14] proposed adaptive backstepping control, fractional order sliding-mode control, and fractional order PID control for a pneumatic position servo system, and they implemented this approach in a test rig. Mu et al [15] proposed and experimentally verified an intelligent position control mechanism, which was based on predictive fuzzy control, for a pneumatic servo system.…”

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“…Current widespread approaches are based on a backstepping method (as in [73] with an adaptive variant) and sliding mode control with some variants (reduced order, two variable, integral,...) [74]. Some solutions are also based on Fuzzy Logic (FL), neural network-based models and Neuro-Fuzzy control [30].…”

Output feedback motion control of pneumatic servo systems with desired compensation approach

“…One is based on the modification of the conventional linear controllers such as friction compensation-based linear controller, proportional-integral-derivative gain scheduling techniques and intelligent control-based controller [3][4][5]. To further enhance the achievable performance, another research effort has paid attention to the model-based nonlinear control strategies such as self-tuning control, model reference adaptive control, disturbance-observer-based control and adaptive robust control [6][7][8][9][10][11].…”