In order to solve the problems of faults and disturbances in linear switched systems, a new fault observer design method is proposed by combining robust observer design with the zonotpoes technology. First, the system fault is regarded as an augmented state, and an augmented system equivalent to the original system is constructed. With the switching signal satisfying the constraint of mean dwell time, the multi Lyapunov function method is used to make the switching system globally exponentially stable and meet the weighted $H_{\infty}$ performance index. Then, the zonotpoes technique is introduced into the interval estimation method, and the state estimation error at each time is recursively obtained by using the center and boundary generation matrix, so that the fault interval can be more accurately shrunk. Finally, a numerical example is given to demonstrate the proposed theoretical result.
This paper studies cooperative trajectory planning for multiple connected and automated vehicles (CAVs) traveling from and off multi-lane roads at a signal-free intersection with the consideration of velocity-prioritized emergency vehicles, such as ambulances and fire engines. In most of the current studies, the trajectory of each CAV is either fixed or in line with the topology of the fleet, whilst the velocity of each CAV is preset with an inflexible pattern, which severely limits the efficiency of the collaboration. Moreover, the priority of emergency vehicles is not well considered in the aforementioned strategies. This research is aimed to address the issues. The intersection control task is formulated as an optimal control problem (OCP), in which the velocities of the CAVs can be set with any reasonable aspiration, while there is no lane discipline at the intersection. When designing the control strategy, unmanned vehicles of various sizes with differentiated velocity priorities are taken into account. An adaptive stepwise optimization (ASO) method is proposed to improve the OCP solution efficiency.
For Hammerstein system with fractional linear state space model, it is very difficult to build its model by identification method. The difficulty lies in that the nonlinear state space equation model describing the system contains unknown model parameters, fractional order of the system and outside noise. In order to overcome these difficulties, we transform the fractional system from an input nonlinear pseudo-state space model to an input-output regression model, and estimate the model parameters with multi-innovation recursive gradient descent algorithm, and confirm the fractional order with the multi-innovation Levenberg-Marquardt algorithm. Finally, the effectiveness of the algorithm is verified by a numerical example.
The paper presents a fuzzy integral sliding-mode parallel tracking control method for general nonlinear systems based on the Takagi-Sugeno (T-S) fuzzy. By introducing an integral equation of the output tracking error, a corresponding fuzzy tracking model is established. Then, a fuzzy integral sliding-mode parallel tracking control strategy is designed to drive the original nonlinear system to track the expected reference signals in the H∞ sense. The proposed methodology enjoys a favorable feature that some assumptions on the system matrix required in the published approaches are eliminated. Finally, the feasibility and advantages of the developed tracking control strategy are demonstrated by a numerical example.
This paper considers a kind of multiple delayed fractional-order fuzzy cellular neural networks, the parameters in this system is mismatched. By using Lyapunov functional method and inequality technique, the synchronization of studied neural networks is achieved under two kinds of adaptive controller. And the results in this study is effective which shows by a simulation example.
Ship pipelines are the most efficient and cost-effective devices for liquid transportation. However, the lack of monitoring methods for minor or invisible pipeline leakage hinders the development of intelligent ship pipeline management. Herein, a self-powered liquid leakage sensor (SLLS) based on a liquid-solid triboelectric nanogenerator (TENG) for ship pipeline monitoring is proposed, aiming at real time detecting, locating, and identifying the invisible and minor leakage of the ship pipelines. The proposed sensor mainly consists of a steel electrode and Si02/ PT F E based superhydrophobic coating, where the contact-separation between coating and leakage droplets can generate electric signals displaying leakage droplet information, such as incident angle, temperature, height, and volume. With the help of the unique $S$ i02 / PT F E based coating, the sensor posses self-cleaning, superhydrophobic and wear-resistant characteristics, making it suitable for environments with vibration, high temperature, and humidity. Moreover, the coating can also combine with the existing steel structures of the ships to form the large scale sensor arrays for leakage information identification. Through the experimental data investigation and analysis, the SLLS has the capacity of obtaining the minor or invisible leakage droplets information with high accuracy, which indicates great promise for employment in practical ship pipelines monitoring.
This paper studies the problem of tracking control for a class of constrained nonlinear switched systems with external disturbances. The control objective is to make the state tracking error of the system converge in a finite time and satisfy the given prescribed performance conditions. A hybrid controller including robust stability controller and model predictive controller is designed for each subsystem. Based on the selected Lyapunov functions of the subsystems, a suitable switching law is designed. It is demonstrated that the system can achieve the control objective by using the Lyapunov stability theory. Simulation results verify the effectiveness of the proposed control strategy.
In recent years, with the development of unmanned vehicle technology, researches on its materials, energy, sensing and other aspects emerge constantly, of which formation control is an important direction. However, most of the existing documents only conduct static analysis on the formation when considering the energy consumption, and the dynamic relationship among formation schedule, performance requirements and energy consumption is not considered as a whole. In this paper, according to the performance requirements at different stages of formation, we divide the whole formation process into three stages and introduce a scheduling based on a hybrid controller. The controller dynamically schedules the whole formation process according to the requirements of each stage to achieve the balance between energy consumption and performance. The simulation results show our scheduling has good performance from both steady-state and dynamic perspectives with low energy consumption.
This paper mainly studies the problem of pursuit-evasion game with multiple pursuers and escapers. In order to improve the real-time performance of the algorithm, the synchronous optimal adaptive controllers are introduced, and the online tuning model is constructed to realize the convergence process of the neural network parameters. The adaptive dynamic programming method is applied to continuous system game problems, and the convergence of the method is proved by establishing a Lyapunov function. Simulation experiments verify the feasibility of the method.
As a cooperative lifting system, dual ship-mounted crane (DSMC) system is mainly used to transport large vol-ume of cargo and building materials in the marine environment. However, DSMC system is a complex nonlinear system with strong-coupling characteristics. Moreover, due to the lack of control inputs, some state variables can only be controlled indi-rectly through the coupling between states, which makes the controller design and corresponding analysis particularly difficult. Additionally, unlike cranes fixed on land, DSMC system is subject to disturbances such as ship motion caused by waves and currents, which means that the impact of waves on this system must be considered during transportation. Therefore, this paper proposes an efficient adaptive controller for DSMC system, which achieves accurate positioning of the boom and suppresses payload swing angle. The proposed control method is robust to unknown disturbances, and the stability is proved through the Lyapunov technique and LaSalle's invariance theorem. Finally, two groups of simulation results show the effectiveness of the control method.
Vehicle detection is a significant part of autonomous vehicles. Although many vehicle detection approaches have achieved impressive performance, achieving robust vehicle detection is still challenging due to the problem caused by occlusion, truncation, small-size vehicles and vehicles with a large variance of scales. In this paper, we propose a vehicle detection model based on the context attention module (CAM) and the multi-scale feature fusion module (MSFFM). Firstly, we construct the backbone of the vehicle detection network with ELAN and MAP. Secondly, we propose the context attention module (CAM) to establish long-range dependencies between pixels, which help the vehicle detection model capture useful context information to promote the robustness of model in challenging scenarios. Finally, we propose the multi-scale feature fusion module (MSFFM) to enhance the model's capability to adapt vehicles with a large variance of scales by collecting multi-scale features. Sufficient qualitative and quantitative experimental results on the KITTI dataset verify that our model achieves the impressive detection accuracy and speed, and outperforming the state-of-the-art methods.
This paper has examined the fixed-time synchronization problem for delayed complex dynamic network. A new fixed-time stability lemma is derived based on some inequality techniques and mathematical induction. On the basis of the new lemma, a simple aperiodically semi-intermittent controller with less terms is introduced, and several sufficient conditions of ensuring synchronization are developed. The setting-time is only related to design parameters, network size and node dimension. Simulation examples are employed to reveal the feasibility of derived methods.
This paper studies the safety-critical control problem for Euler-Lagrange (EL) systems subject to multiple position constraints. The major contribution lies in a new cascade design of safety-critical controllers. The outer-loop controller is developed based on quadratic programming (QP). A velocity-tracking controller is designed to form the inner loop. One major difficulty is caused by the possible non-Lipschitz continuity of the standard QP algorithm. To solve this problem, we propose a refined QP algorithm with reshaped feasible set and relaxation factor such that the outer-loop controller is locally Lipschitz. It is proved that the safety-critical control objective can be accomplished as long as the EL systems under consideration satisfy a mild initial condition. The proposed design is validated by simulation results on a 2-link planar manipulator.
This paper studies the impact angle control guidance problem with general field-of-view (FOV) constraints for a moving target. Firstly, the control Lyapunov function (CLF) is constructed based on the state-dependent Riccati equation (SDRE) technique, and a nominal controller is designed to intercept the moving target at the desired impact angle without FOV constraints. The control barrier functions (CBFs) are designed according to the FOV constraints. Secondly, the CLF that guarantees stability is combined with the CBFs that guarantee forward invariance of the constraint sets. The minimum modification framework is used to numerically solve the guidance command through quadratic programming to ensure that the look angle of the missile always remains within the constraint interval during the interception process. Finally, the effectiveness of the proposed guidance law is verified by numerical examples.
Collisions with space debris can be devastating to spacecraft. In this paper, a new algorithm for collision avoidance maneuver optimization of autonomous spacecraft is proposed. A collision avoidance controller that satisfies realtime trajectory optimization based on proximal policy optimization (PPO) is designed. The PPO guidance controllers can extract hazard information from motion states through empirical learning and map decisions directly to impulse maneuver commands, which can divert the spacecraft away from hazardous areas. Meanwhile, the collision probability and energy consumption are considered as the optimization objectives, then the reward functions is determined in the learning process. Simulation results demonstrate that the proposed method enables the spacecraft to autonomously avoid multiple space debris and meets the realtime requirement.
This article explores the leader-follower rotating consensus problem for second-order multi-agent systems that include a smart leader. The smart leader has the ability to collect and utilize information from its neighboring agents in order to enhance the system's overall performance. The two-dimensional rotation problem is first transformed into a one-dimensional nonlinear dynamic tracking problem, then a distributed observer is proposed to ensure that all followers can estimate the state of the leader in fixed time, where the predefined fixed time is irrelevant to the original conditions. The distributed control algorithm based on the observer enables all followers to converge toward the state of the smart leader by tracking the observer's estimated state. To demonstrate the effectiveness of the proposed observer and controller, the numerical simulation is carried out with and without the smart leader.
