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This paper proposes an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for multi-degree-of-freedom (n-DOF) hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. The controller design is formulated by integrating Radial Basis Function Neural Networks (RBFNNs) with state and disturbance observers using the backstepping method. The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error, coupled with external disturbances, is estimated through the combination of state observer and disturbance observer. The unique features of the proposed controller lies in its capability to estimate both matched and unmatched lumped disturbances. The auxiliary disturbance estimation law is guided by the neural learning weights and estimated system states provided by state observers. By effectively utilizing neural networks to approximate and mitigate most nonlinear uncertainties, the workload of the disturbance observer is substantially reduced. High-gain feedback is therefore avoided and improved tracking performance can be expected. Moreover, to avoid the tedious analysis and the problem of "explosion of complexity" in the conventional backstepping method, we employ a first-order sliding-mode differentiator. Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system, ensuring guaranteed and satisfactory tracking performance under the integrated influence of unknown nonlinearities, unmeasured states, and external disturbances. Extensive simulations are conducted to verify the effectiveness of the nested control strategy. • We propose an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for n-DOF hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. • The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error is estimated through the state observer and disturbance observer. An auxiliary disturbance estimation law is proposed to estimate the lumped disturbances. • To estimate the lumped disturbances, we introduce an auxiliary disturbance estimation law and estimated system states. Since the majority of nonlinear uncertainties can be mitigated through NN approximation, the burden on the state/disturbance observer is reduced. • Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system. Simulations are conducted to verify the effectiveness of the nested control strategy. [ABSTRACT FROM AUTHOR]

In this paper, we design nonlinear output feedback control laws obtained from linear ones by using two types of replacements. These replacements use some kinds of odd nonlinear functions and do not change the tuning coefficients calculated in the linear control law. The first replacement consists of passing each component of the output vector through a nonlinear function, and the second replacement consists of passing each component of the linear control law through a nonlinear function. As a result, the proposed nonlinear control laws reduce the ultimate bound compared to the linear controller. A novel method is proposed to study such control schemes. This method consists in representing nonlinear functions as linear ones with nonlinear slopes, that allows the use of linear matrix inequality (LMI) approaches to study the stability of the closed-loop system. The stability and attraction domains are found by using a new multi-step procedure based on the verification of the LMI feasibility in subdomains, the union of which gives the searched domains. The simulations confirm the theoretical results and illustrate the efficiency of the proposed methods compared to some linear controllers. [ABSTRACT FROM AUTHOR]