This paper uses the adaptive dynamic programming (ADP) method to achieve optimal trajectory tracking control for quadrotors. Relying on an established mathematical model of a quadrotor, the approximate optimal trajectory tracking control, which consists of the steady-state control input and the approximate optimal feedback control input, is designed for a nominal system. Considering the compound disturbances in position and attitude dynamic models, disturbance observers are introduced. The estimated values are used to design robust compensation inputs to suppress the effect of the compound disturbances for good trajectory tracking performance. Theoretically, the Lyapunov theorem demonstrates the stability of a closed-loop system. The robustness and effectiveness of the proposed controller are confirmed by the simulation results.

This paper studies the containment control problem of linear multi-agent systems (MASs) subject to external disturbances, where the communication graph is a directed graph with the followers being undirected connections. In order to save communication costs and energy consumption, a distributed disturbance observer-based event-triggered controller is employed based on the relative outputs of neighboring followers. Compared with conventional controllers, our observer-based controller utilizes the relative outputs of neighboring followers at the same triggered instant. Furthermore, it is shown that Zeno behavior can be avoided. Finally, the validity of our proposed methodology is demonstrated by a simulation example.

This paper presents a practical study on how to improve the ${\mathscr{H}}_{\infty }$ performance and meet the input–output constraints of the two-degrees-of-freedom (DOF) flexible-joint manipulator system (FJMS) with parameter uncertainties and external disturbances. For this reason, a robust constrained moving-horizon ${\mathscr{H}}_{\infty }$ controller is designed to improve the system ${\mathscr{H}}_{\infty }$ performance while still satisfying the input–output constraints of the uncertain system. First, the uncertain controlled system model of the two-DOF FJMS is established via the Lagrange equation method, Spong’s assumption, and the linear fractional transformation (LFT) technique. Then, the control requirements and input–output constraints of the uncertain system are transformed into the linear matrix inequality (LMI) via the theory of ${\mathscr{H}}_{\infty }$ control and the full-block multiplier technique. Next, the LMI optimization problem refreshed by the current state is addressed at each sample moment with the idea of the moving-horizon control of the model predictive control (MPC), and the calculated gain is implemented to the nonlinear closed-loop system under the state feedback structure. The validity and feasibility of the designed control scheme is finally verified via the results of simulation experiments.

This article investigates a class of multi-agent systems (MASs) with known dynamics external disturbances, where the communication graph is directed, and the followers have undirected connections. To eliminate the impacts of external disturbance, the technologies of disturbance observer-based control are introduced into the containment control problems. Additionally, to save communication costs and energy consumption, a distributed disturbance observer-based event-triggered controller is employed to achieve containment control and reject disturbance. Furthermore, designing the event-triggered function using an exponential function is beneficial for a time-dependent term while ensuring the exclusion of Zeno behavior. Finally, a numerical simulation is provided to validate the effectiveness of the theoretical analysis.

This paper proposes a robust barrier-function-based fuzzy adaptive super-twisting integral terminal sliding-mode control (BF-FAST-ITSMC) for robotic manipulators subject to external disturbances. Initially, an integral terminal sliding-mode manifold is designed to ensure finite-time error convergence and eliminate steady-state offsets. To reduce model dependence, the unknown nonlinear function is approximated and compensated using a fuzzy approximator. By combining the super-twisting algorithm (STA) and the barrier-function-based adaptive gains, the designed BF-FAST-ITSMC can suppress actuator chattering effectively, which allows control gains to increase automatically as the error approaches the prescribed boundary. This mechanism ensures that tracking errors are strictly confined within a predefined bound. Comparative simulations on an inverted pendulum and robotic manipulators with one to three degrees of freedom demonstrate that the proposed method provides superior tracking precision, smooth control torque, and enhanced robustness compared to conventional and fuzzy ITSMC schemes.