Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

Adaptive robust control (ARC) has been applied to pneumatic systems successfully for its excellent performance and strong robustness. Although many researchers are already working on studying of the ARC, the stability analyses of these works are performed by the Lyapunov method. The contraction theory is a new proposed “differential” Lyapunov-like analytic tool for the nonautonomous nonlinear system. It can provide a more convenient analysis and richer design toolbox of the nonlinear system than Lyapunov method. This article intelligently brings the philosophy of contraction theory into the ARC design framework to form a contraction theory-based ARC (CT-ARC) for a pneumatic servo system. The proposed method offers more design choices than the traditional ARC. In addition, a command filter is employed to acquire the differential signal of the virtual control law. The stability analysis of the whole system is presented elegantly by the contraction theorem with consideration of the estimation error of the command filter. Furthermore, exponential convergence of the closed-loop system can be derived under specific conditions. Finally, various experiments for the trajectory tracking control of a pneumatic cylinder demonstrate the efficiency of CT-ARC.

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

Tight porous materials are used as pneumatic components in a wide range of industrial applications. Such porous materials contain thousands of interconnected irregular micropores, which produce a large pressure drop (ΔP) between the upstream and downstream sides of the porous material when a fluid flows through it. The relationship between the pressure drop and flow rate (i.e., ΔP-G characteristics) is a very important basic characteristic. Temperature is one of the factors that affect the ΔP-G characteristics because variations in temperature change the viscosity and density of the fluid. In this study, we experimentally analyzed the ΔP-G characteristics of tight porous materials by heating them using an electromagnetic system. First, we experimentally investigated the change in the ΔP-G curve under the condition of constant heating power. Then, based on the Darcy–Forchheimer theory, we introduced an experimental method to determine the average temperature of the fluid. The results show that the temperature reaches approximately 500 K in the small flow rate range, which produces considerable changes in the ΔP-G curve. As the flow rate increases, the temperature decreases, and thus, the ΔP-G curve at constant heating power converges to the curve for the room temperature. Furthermore, we compared three porous materials with different permeability coefficients and porosities and analyzed the effect of these parameters on the ΔP-G characteristics. We also performed experiments at different downstream pressures to study the effect of the average density on the ΔP-G characteristics.