IET Control Theory and Applications最新文献

This paper investigates the control problem of spacecraft rendezvous with obstacle constraint, considering the external disturbance forces caused by orbit perturbation. Firstly, the translational dynamic model of spacecraft rendezvous is given and then rewritten into a second-order fully-actuated system form. Then, by employing the prescribed performance control method, the performance function and error transformation are determined, pre-defining the prescribed performance bounds. Moreover, the fully-actuated system approach is used to linearize the original nonlinear system, which simplifies the processes of control law design and ensures model accuracy. After that, to ensure that the spacecraft could avoid the dangerous zone during its manoeuvre, the artificial potential function is introduced, based on which a sliding mode surface is designed. Finally, the prescribed performance control–artificial potential function-based control law is derived, further adopting the neuro-adaptive method to deal with external interferences. The stability of the close-loop control system is analysed through the Lyapunov approach and the effectiveness of the proposed control scheme is verified by carrying out a numerical simulation.

This article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time-series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root-mean-square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root-mean-square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience.

This study focuses on the highway platoon driving mode and proposes a distributed adaptive control algorithm based on an observer. Firstly, the adaptive observer is designed to compensate for the effect of unknown driving resistance and thus enhance the adaptation ability of the system to uncertainty. Secondly, an auxiliary system is introduced to specifically address actuator saturation constraints, ensuring the stability of the platoon driving in extreme conditions. Lastly, combining an event-triggered mechanism, a control strategy is designed to achieve the stability of the entire platoon while maximizing the conservation of communication resources. The algorithm's viability and efficiency are confirmed through simulation outcomes.

This paper proposes an improved two-degree-of-freedom active disturbance rejection controller for the coupling problem of asynchronous motor vector system. To simplify the analysis process and accommodate observers of different types, a unified expression based on different controllers for the system output is developed. The closed-loop transfer function generated by the reference input and load disturbance is given. For the coupling problem of motor output speed and immunity, the structure of a higher-order extended state observer is reconstructed. The extended state observer estimates both the output speed and the total system disturbance, which serve as feedback and feed-forward compensation quantities. Compared to the PI controller and traditional active disturbance rejection controller, the proposed controller achieves decoupling of output response speed and immunity, simplifies the process of parameter tuning. Finally, simulation and experiment results verify the feasibility and effectiveness of the algorithm in this paper.

This article presents a reachability-based receding horizon control (RHC) method for addressing persistent monitoring problems with count requirements. An agent is assigned to monitor multiple targets in a given environment to minimize the average uncertainty metric of all targets, while ensuring the monitoring count requirements of specific targets within predetermined time windows. To account for the spatial and temporal constraints in the monitoring requirements, a persistence predicate within the signal temporal logic (STL) specifications is introduced, which incorporates cumulative target state signals to effectively describe the monitoring count constraints. Considering the complexities arising from global time domain information requirements in STL constraints validation, an STL formula segmentation method based on completion progress is proposed. Subsequently, a reachability-based controller for the agent is developed by solving a short-term RHC problem while ensuring the satisfaction of the STL formulae. Simulation results are provided to illustrate the performance of proposed method.

To address the stable grasping control issue in manipulator grasping systems, this manuscript proposes an improved multiverse optimizer-based anti-saturation model-free adaptive control (IMVO-AS-MFAC) algorithm. Initially, the manuscript converts the manipulator grasping system into an equivalent data model through dynamic linearization techniques. Then, based on the dynamic linearization model, the IMVO-AS-MFAC controller is designed. To address the actuator saturation problem that commonly occurs during the clamping process of manipulator grasping systems, a saturation parameter is introduced into the IMVO-AS-MFAC algorithm. Meanwhile, the controller parameters are optimized using an improved multiverse optimizer algorithm, which involves modifications to the initial population distribution and location update strategy. The improved algorithm demonstrates more competitive optimization performance compared to the traditional multiverse optimizer. The major advantage of the IMVO-AS-MFAC algorithm lies in the fact that only the input and output data of the manipulator grasping system are required throughout the entire control process, and the controller parameters are derived using an optimization algorithm rather than relying on empirical knowledge. Furthermore, rigorous mathematical analysis confirms the stability of the IMVO-AS-MFAC approach, and its effectiveness is validated through semi-physical experiments conducted in an environment integrating the MATLAB/Simulink module and the RecurDyn platform.

A data-driven Buck converter model identification method is proposed to deal with missing (incomplete) outputs, which is robust to the data length and percentage of missing data. A nuclear norm based convex optimization problem instead of linear interpolation, to guarantee the recovered missing data satisfying the potential model structured low-rank character, is constructed to estimate missing outputs. The alternating direction method of multiplier strategy is used to solve the nuclear norm based convex optimization problem. In this way, the high-quality missing data can be estimated, even for short data length and high percentage of missing data. Based on the recovered data, the subspace identification method provides accurate estimates of the structure and parameter of the Buck converter synchronously. By applying the proposed method to a Buck converter, experimental results demonstrate its effectiveness.

The present investigation aims to address the leader-following consensus issue for variable-order fractional multi-agent systems (VOFMASs) under actuator faults and unknown external disturbances. The Caputo definition for the variable-order fractional (VOF) derivative is used to model the non-linear dynamics of the leader and follower agents. Consequently, two lemmas are developed for the Caputo VOF derivative of the Lyapunov function. In the first case, it is assumed that the multi-agent system (MAS) operates without actuator faults and an adaptive controller is proposed. With the aid of the developed lemmas, assurance is provided for the finite-time bounded cooperative tracking of the VOFMAS despite the presence of unknown external disturbances. In the second case, a novel fault-tolerant controller is designed for the finite-time consensus of the MAS under two common kinds of actuator faults: loss of effectiveness fault and bias fault. Finally, the efficacy of the proposed controller is demonstrated through the presentation of results from three simulation examples.

This paper concentrates on synthesizing robust predictive controllers in uncertain models with time-delay and saturated input. To handle the complexities simultaneously and reach the desired objective, a state-feedback structure with unknown gains is considered the compensator. Then, to specify the instantaneous values of the regulator's gains, an optimization issue will be derived relying on linear matrix inequality. Accordingly, in uncertain continuous-time systems with some constraints and time-delays, the controller's coefficients would be found in an online way from such an optimization. Numerous continuous-time simulations are numerically performed to reveal the merit and robustness of the suggested methodology over similar techniques.