This paper focuses on developing an advanced nonlinear controller for hydraulic system to achieve asymptotic tracking with various disturbances. To accomplish this study, a multilayer neural-networks (NNs) estimator is first developed to improve the compensation accuracy of model-based feed-forward control terms, which can greatly reduce the uncompensated disturbance, then a continuous robust integral of the sign of the error (RISE) control approach is effectively integrated with the multilayer neural-networks (NNs) estimator to deal with the residual mismatched disturbance, in which the RISE feedback gain is adapted online to further decrease the high-gain feedback. At last, by considering the inwardness of matched disturbance in hydraulic systems, it is estimated by another multilayer NNs fully with the residual functional reconstruction inaccuracies handled by a novel adaptive term. As a result, theoretical analysis reveals that the proposed controller guarantees a semi-global asymptotic stability. Extensively comparative experimental results verify the priority of the proposed control strategy, and a 0.2% dynamic tracking accuracy is achieved.

Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement learning as a feasible complex problem solver, the research on reinforcement learning control of hydraulic systems is still void. We are motivated by the challenges presented in hydraulic systems to develop a new model-based reinforcement learning controller that achieves high-accuracy tracking at performance level and with asymptotic stability guarantees at system level. In this article, the proposed design consists of two frameworks: A recursive robust integral of the sign of the error (RISE) control approach to providing closed-loop system stability framework, and a reinforcement learning approach with actor-critic structure to dealing with the unknown dynamics or more specifically, the actor neural network is used to reduce the high feedback gain of the recursive RISE control approach by compensating the unknown dynamic while the critic neural network is integrated to improve the control performance by evaluating the filtered tracking error. A theoretical guarantee for the stability of the overall dynamic system is provided by using Lyapunov stability theory. Simulation and experimental results are provided to demonstrate improved control performance of the proposed controller.

Motivated by the challenges inherent in achieving high-accuracy tracking control of practical 6-degree-of-freedom (6-DOF) hydraulic robotic manipulators, we aim to conduct research on data-driven control methods in this article. To this end, we introduce actor-critic reinforcement learning to learn the value functions and their corresponding control actions along the trajectories associated with the tracking pattern evolution in hydraulic robotic manipulators. Furthermore, we extend our investigation to encompass the provision of performance guarantees at the system level, even in instances where value functions and control actions are approximated through the utilization of actor-critic neural networks. As proved in this article, the proposed reinforcement learning controller possesses pivotal properties, such as Bellman (sub)optimality in solutions, and the convergence of approximated value functions. We evaluated the proposed reinforcement learning controller in a well-established 6-DOF hydraulic robotic manipulator platform. The experimental results attest to the consistent improvement in control performance, progressively approaching the desired performance benchmarks through iterative updates of control actions. This study makes the first attempt to explore the realization of online adaptive optimal control for hydraulic robotic manipulators within a data-driven paradigm, an area where limited work has been reported, and also sets an example of how reinforcement learning interfaces with industrial automation.

As hydraulic Euler–Lagrange systems evolve to encompass high-dimension and intricate structures, the formulation of the mechanical dynamics model becomes impractical, even though the hydraulic dynamic information remains accessible. In this article, we make the first attempt to establish model-data hybrid driven controller for hydraulic Euler–Lagrange systems. The proposed strategy commences with the formulation of model-based nonlinear robust controller, followed by the introduction of actor–critic reinforcement learning to effectively tackle uncertainties associated with unknown mechanical dynamics. Employing Lyapunov analysis, we have proven the asymptotic stability of the model-based nonlinear robust controller, along with the convergence and (sub)optimality of the introduced actor–critic reinforcement learning. Building upon this foundation, the newly devised hybrid-driven controller still guarantees asymptotic stability through the reconstruction of the error auxiliary function. We validated the efficacy of the proposed strategy on a well-established six-degree-of-freedom hydraulic manipulator and conducted a comparative analysis with the purely data-driven approach to demonstrate the superior performance of the developed controller. The integration of model-based and data-driven terms in our strategy signifies notable advancements in the control paradigm for hydraulic Euler–Lagrange systems, and the conceptual control framework also holds the potential to address control challenges within diverse and intricate industrial systems.