Observer-Based Control for Nonlinear Time-Delayed Asynchronously Switching Systems: A New LMI Approach

This paper designs an observer-based controller for switched systems (SSs) with nonlinear dynamics, exogenous disturbances, parametric uncertainties, and time-delay. Based on the multiple Lyapunov–Krasovskii and average dwell time (DT) approaches, some conditions are presented to ensure the robustness and investigate the effect of time-delay, uncertainties, and lag issues between switching times. The control parameters are determined through solving the established linear matrix inequalities (LMIs) under asynchronous switching. A novel LMI-based conditions are suggested to guarantee the H∞ performance. Finally, the accuracy of the designed observer-based controller is examined by simulations on practical case-study plants.

Output Reachable Set Analysis for Periodic Positive Systems

In this study, we use the union of the bounding hyperpyramids to estimate the output reachable set for periodic positive systems under two classes of exogenous disturbances. Optimization algorithms are used to obtain the smallest bounding hyperpyramids possible. Finally, numerical examples are given to verify the theoretical results.

An Improved Equivalent-Input-Disturbance Method for Uncertain Networked Control Systems with Packet Losses and Exogenous Disturbances

In a networked control system (NCS), time delays, uncertainties, packet losses, and exogenous disturbances seriously affect the control performance. To solve these problems, a modified disturbance suppression configuration of NCS was built. In the configuration, a proportional–integral observer (PIO) reproduces the state of a plant and reduces the observation error; an equivalent input disturbance (EID) estimator estimates and compensates for the disturbance in the control input channel. The stability conditions of the NCS are given by using a linear matrix inequality, and the gains of the PIO and state feedback controller are obtained. Numerical simulation results and an application of a magnetic levitation ball system verifies the effectiveness of the presented method. Comparison with the conventional PIO and EID methods shows that the presented method reduced the tracking error to about one-fifth and two-thirds of their original values, respectively. This demonstrates the validity and superiority of the presented method.

Reset and prescribed performance approach to spacecraft attitude regulation

Purpose This paper aims to investigate the attitude regulation for spacecraft in the presence of time-varying inertia uncertainty and exogenous disturbances. Design/methodology/approach The high gain approaches are typically used in existing researches for theoretical advantages, bringing better performance but sensitive to parameter selection, making the controller conservative. A reset-control policy is presented to achieve the spacecraft attitude control with easy control parameter tuning. Findings The reset-control policy guarantees satisfying control performance despite using performance tuning function and saturation function besides reducing the conservativeness of the controller, thus reducing the effort in tuning control parameters. Originality/value Notably, the adaptive function owns a reset mechanism, which is reset to a preset condition when the controlled variable crosses zero. The mathematical analysis also shows the system trajectory can converge to a set centered at the origin.

Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs

This paper addresses an attitude tracking control design applied to multirotor unmanned aerial vehicles (UAVs) based on an ADRC approach. The proposed technique groups the endogenous and exogenous disturbances into a total disturbance, and then this is estimated online via an extended state observer (ESO). Further, a quaternion-based feedback is developed, which is assisted by a feedforward term obtained via the ESO to relieve the total disturbance actively. The control law is bounded; consequently, it takes into account the maximum capabilities of the actuators to reject the disturbances. The stability is analyzed in the ISS framework, guaranteeing that the closed loop (controller-ESO-UAV) is robustly stable. The simulation results allow validation of the theoretical features.

A Robust Hybrid Control for Autonomous Flying Robots in an Uncertain and Disturbed Environment

With the aim of efficiently achieving complex trajectory tracking missions in the presence of model uncertainties and exogenous disturbances, this paper proposes a robust hybrid control for the orientation and position of flying robots by adopting insights from sliding mode, geometric tracking, and nonlinear feedback control strategies. Various retrofits are implemented to the composite control scheme in order to tackle the system uncertainties, eliminate the chattering effects, and enhance the trajectory tracking performance. The convergence and stability analysis demonstrated the asymptotic stability of the proposed control algorithm. To reveal the promising performance of the developed control schemes, a qualitative comparative analysis of different proposed control approaches is performed. The comparative analysis examines highly maneuverable trajectories for various tracking scenarios in the presence of uncertain disturbances. The simulation results demonstrated the versatility, robustness, and convergence of the developed control laws that allow autonomous flying robots to effectively perform agile maneuvers.

Robust Passivity Cascade Technique-Based Control Using RBFN Approximators for the Stabilization of a Cart Inverted Pendulum

This paper proposes a novel passivity cascade technique (PCT)-based control for nonlinear inverted pendulum systems. Its main objective is to stabilize the pendulum’s upward states despite uncertainties and exogenous disturbances. The proposed framework combines the estimation properties of radial basis function neural networks (RBFNs) with the passivity attributes of the cascade control framework. The unknown terms of the nonlinear system are estimated using an RBFN approximator. The performance of the closed-loop system is further enhanced by using the integral of angular position as a virtual state variable. The lumped uncertainties (NN—Neural Network approximation, external disturbances and parametric uncertainty) are compensated for by adding a robustifying adaptive rule-based signal to the PCT-based control. The boundedness of the states is confirmed using the passivity theorem. The performance of the proposed approach was assessed using a nonlinear inverted pendulum system under both nominal and disturbed conditions.

Bipartite Consensus of Linear Discrete-Time Multiagent Systems with Exogenous Disturbances under Competitive Networks

This paper investigates the bipartite consensus of linear discrete-time multiagent systems (MASs) with exogenous disturbances. A discrete-time disturbance-observer- (DTDO-) based technology is involved for attenuating the exogenous disturbances. And both the state feedback and observer-based output feedback bipartite consensus protocols are proposed by using the DTDO method. It turned out that bipartite consensus can be realized under the given protocols if the topology is connected and structurally balanced. Finally, numerical simulations are presented to illustrate the theoretical findings.