Automatic Control of Hydraulic Systems

For industrial, automotive, and aeronautical applications, hydraulic actuators and hydraulic valves have an essential role to deliver and regulate forces and moments. The evolution of hydraulic systems to cover the demands of Industry 4.0 through the digitation of hydraulic valves makes the automatic control of hydraulic actuators crucial for production lines and processes.

In this collection in Chapter 1 a robust asymptotic tracking controller for the accurate positioning of an industrial Hydraulic Actuator is presented. The nonlinear uncertain model of the actuator and the uncertain parameters occurring due to physical system uncertainties are analytically characterized. The uncertain vector involves supply pressure, load mass and stiffness, viscous damping, and fluid bulk modulus variations. Furthermore, unknown disturbances from the industrial environment are also considered. The nonlinear model of the actuator is linearized, and a robust asymptotic tracking controller is proposed to control the position of the actuator. Solvability conditions are derived and using a Hurwitz invariability algorithm, stability regions are characterized for the controller parameters. The best solution for the controller parameters for all uncertainties variation and load distribution is derived using a sine-cosine based swarm optimization algorithm. The combination of the Hurwitz invariability algorithm appropriately extended with a sine – cosine optimization algorithm seems to be a powerful tool for solving robust control problems. Simulation test results of the performance of the uncertain nonlinear closed-loop system, demonstrate the effectiveness of the robust controller over the hole uncertain domain and for various external disturbances. (Michael G. Skarpetis, PhD. – Associate Professor, Core Department, National and Kapodistrian University of Athens, Athens, Greece and Fotis N. Koumboulis, PhD.- Professor,  Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Athens, Greece)

In Chapter 2, the control problem for the nonlinear dynamics of robotic and mechatronic systems with electrohydraulic actuation is solved with the use of flatness-based control approach which is implemented in successive loops. The state-space model of these systems is separated into a series of subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics, as in the case of input-output linearized flat systems. In this chain of subsystems, the state variables of the subsequent (i + 1-th) subsystem become virtual control inputs for the preceding (i-th) subsystem, and so on. In turn, exogenous control inputs are applied to the last subsystem and are computed by tracing backwards the virtual control inputs of the preceding N – 1 subsystems. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of an electrohydraulic actuator, (ii) control of a multi-DOF robotic manipulator with electrohydraulic actuators (G. Rigatos – Unit of Industrial Automation, Industrial Systems Institute, 26504, Rion Patras, Greece, M. Abbaszadeh – Department of ECS Eng., Rensselaer Polytechnic Institute, 12065, Troy, New York, USA, J. Pomares – Department of Systems Engineering, University of Alicante, 03690, Alicante, Spain and P. Wira – IRIMAS, Universit´e de Haute Alsace, 68093, Mulhouse, France).

In Chapter 3, the performance of an electro-hydraulic actuator (EHA) system’s trajectory tracking employing an optimized fuzzy sliding mode controller (FSMC) is presented. In simulations, the performance of the FSMC, which is developed based on the transfer function structure of a double-acting EHA system third-order model, is assessed using a chaotic trajectory. The particle swarm optimization (PSO) algorithm identifies the design gain variables of the control law, which is developed from the concept of the exponential reaching law. The Lyapunov theorem theoretically demonstrates the stability of the control system. Simulation findings indicate that the proposed controller is highly robust and capable of accommodating system parameter change during trajectory tracking control. It also demonstrates that the proposed controller is superior to conventional PID controllers (Muhamad Fadli Ghani, – Malaysian Institute of Marine Engineering Technology (MIMET) Universiti Kuala Lumpur, Perak, Malaysia, Centre for Robotics and Industrial Automation (CeRIA), Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia, Rozaimi Ghazali, – Centre for Robotics and Industrial Automation (CeRIA), Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia, Hazriq Izzuan Jaafar, – Centre for Robotics and Industrial Automation (CeRIA), Fakulti Kejuruteraan Elektrik, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia, Chong Chee Soon, – Department of Engineering and Built Environment, Tunku Abdul Rahman University College, Penang Branch Campus, Pulau Pinang, Malaysia, Zulfatman Has, – Electrical Engineering Department, University of Muhammadiyah Malang, Malang, Indonesia

In Chapter 4, a new intelligent motion controller is presented for constrained hydraulic systems using a special combination of neural network and nonlinear disturbance observers. In this first stage, a simplified constrained sliding-mode-backstepping scheme is designed to drive the control objective to a vicinity around the origin without any physical violations. In the second stage, uncertain nonlinearities inside the system dynamics are compensated by new neural networks with fast learning rules. The neural learning errors and external disturbances are in the third stage presented as extended nonautonomous models and are then approximated by nonlinear high-order disturbance observers. Robustness of the closed-loop control system is maintained by a proper Lyapunov theory. Effectiveness and feasibility of the proposed control system for an asymptotically tracking performance are then confirmed by comparative simulation results (Dang Xuan Ba, – Department of Automatic Control, Hochiminh City University of Technology and Education (HCMUTE), Ho Chi Minh City, Vietnam, Kyoung Kwan Ahn – Department of Mechanical Engineering, University of Ulsan (UoU),
Ulsan City, Korea)