The multiple dynamic targets enclosing control protocol is proposed in this paper for a class of nonlinear uncertain multiagent systems. To accomplish the mission of hunting multiple targets with ...double-integral nonlinear dynamics, a distributed estimator is established firstly for each agent by employing the signum function, leading to an estimation of the average position of multiple targets. Specifically, only partial information about the dynamic targets is utilized to construct the enclosing formation reference beacon. Furthermore, due to the existence of the system uncertainties, it is a challenging task to design the enclosing controller. To overcome this difficulty, an augmented system is constructed to compensate for the effect caused by the uncertainties, which is introduced afterwards into the control protocol design along with comprehensive theoretical analysis. Sufficient conditions are derived and proved under Lyapunov stability criterion to ensure the closed-loop stability of nonlinear multiagent systems. The effectiveness of the proposed control scheme is finally verified through the numerical simulations. More precisely, by appropriately picking control parameters α and β based on Lemma 3.1, the designed formation reference beacon estimator is validated by achieving convergence of errors between the actual state and estimated states to zero. Meanwhile, the feasibility of the proposed enclosing control protocol is demonstrated simultaneously as it enables the regulation of enclosing errors using a self-designed parameter γ0. Additionally, the sensitivity analysis of the control parameters is presented, which indicates that the proposed reference beacon estimator remains robust to the variation of its control parameters, while larger values for the control parameters in the designed controller result in faster error convergence rates.
Summary
This paper is devoted to tracking control of nonlinear multiple‐input multiple‐output systems subject to unknown state delays and non‐repetitive uncertainties. An adaptive iterative learning ...control scheme is proposed by integrating a P‐type feedback term, an iterative updating term and an additional compensation term. Among these terms, the first one is to alleviate the ill effect caused by non‐repetitive uncertainties and the third one is utilized to handle the unwell learned part. The composite energy function methodology is employed to elaborate the bounded convergence of the tracking error. The validity of the proposed method is compared with existing results via simulation studies.
Summary
This paper will develop a new robust topology optimization (RTO) method based on level sets for structures subject to hybrid uncertainties, with a more efficient Karhunen‐Loève hyperbolic ...Polynomial Chaos–Chebyshev Interval method to conduct the hybrid uncertain analysis. The loadings and material properties are considered hybrid uncertainties in structures. The parameters with sufficient information are regarded as random fields, while the parameters without sufficient information are treated as intervals. The Karhunen‐Loève expansion is applied to discretize random fields into a finite number of random variables, and then, the original hybrid uncertainty analysis is transformed into a new process with random and interval parameters, to which the hyperbolic Polynomial Chaos–Chebyshev Interval is employed for the uncertainty analysis. RTO is formulated to minimize a weighted sum of the mean and standard variance of the structural objective function under the worst‐case scenario. Several numerical examples are employed to demonstrate the effectiveness of the proposed RTO, and Monte Carlo simulation is used to validate the numerical accuracy of our proposed method.
A concurrent topology optimization for thermoelastic structures with random and interval hybrid uncertainties is discussed in this work. A robust topology optimization method is proposed for ...structures composed of periodic microstructures under thermal and mechanical coupled loads. The robust objective function is defined as a linear combination of the mean and standard variance under the worst case for the robust optimization model. An efficient hybrid orthogonal polynomial expansion (HOPE) method is developed to evaluate the robust objective function. The sensitivities for the robust topology optimization are then calculated based on the uncertainty analysis. Three numerical examples are provided to verify the effectiveness of the proposed method, and the Monte Carlo scanning test is used to validate the numerical accuracy of our proposed method. For comparison purpose, the topology optimizations under deterministic assumptions are also provided for these examples to show the importance of considering hybrid uncertainties.
A series of work for distributed dynamic load identification is investigated in this paper considering unknown-but-bounded uncertainties in the aircraft structure. To facilitate the analysis, the ...complicated rudder structure is simplified to a plate structure based on the robust equivalence principle of mechanical property under multi-cases of flight environments. Aiming at the plate structure, a time domain–based model for distributed dynamic load identification is established through the acceleration response measured by sensors. Among them, the spatial distributed load is approximated by Chebyshev orthogonal polynomials at each sampling time, and load boundaries can be calculated by the Taylor-expansion-based uncertain propagation analysis. As keys to improve the reliability of recognition results, the optimization process for sensor placement is constructed by the particle swarm optimization algorithm, taking the robustness evaluation index and sensor distribution index into consideration. The validity and the feasibility of the proposed methodology are demonstrated by several numerical examples, and the results reveal that designer can make a rational tradeoff choice among the cost of sensor placement and the performance of load identification in a systematic framework.
•The complex deformation mechanisms of the small punch tensile and creep tests are clarified.•Specific features on the uncertainties of the test method in data interpretation are ...highlighted.•Critical comments on the applicability of the test method are given.•Recommendations and the opportunities for future developments are addressed.
The objective of this paper is to clarify the complex nature which involves the interaction of several non-linear deformation mechanisms experienced in small punch tensile and small punch creep tests, and as a consequence, the associated difficulties in data interpretation. The paper starts with a highlight of some specific features experienced during small punch deformation, e.g., large deformation, high local plasticity, contact, the effect of initial plasticity, possible early cracking etc. and the consequences of them in data interpretation. Although several test standards have been available, uncertainties in data interpretation still exist and a universally accepted conversion method has not been available. On this basis, for the first time, critical comments on the uncertainties and applicability of the test methods in determining the tensile and creep properties and in high-temperature structural integrity assessment are addressed. Finally, recommendations on future developments are provided.
•An adaptive model predictive control is developed to address parameter uncertainties.•A sensitivity analysis is studied to provide insights into the impact of uncertainties.•The proposed method is ...validated and discussed in both simulation and experiments.•In the experiment, the improvement of power losses can be as high as 15%.
Hybrid energy storage systems have been widely used in transportation, microgrid and renewable energy applications to improve system efficiency and enhance reliability. However, parameter uncertainty can significantly affect system performance. In order to address this issue, an adaptive model predictive control is developed in this paper. Online parameter identification is used to mitigate parameter uncertainty, and model predictive control is used to optimally split power, deal with constraints, and achieve desired dynamic responses. A sensitivity analysis is conducted to identify major impact factors. In order to validate the proposed method, both simulation and experiments are performed to show the effectiveness of the proposed adaptive model predictive control. Compared to the model predictive control without online parameter identification, the power loss reduction can be as high as 15% in the experiments. This study focuses on all-electric ship energy management to mitigate load fluctuations and improve system efficiency and reliability. The proposed method could also be used in other applications.
•A comprehensive framework for short-term electrical load forecasting is presented.•RNN with attention reduced forecasting errors by 20–45% from the state of the art.•Robust against different ...building types, locations, weather and load uncertainties.•One month of data is enough to give satisfactory results.•Clustering and 15-min data give better results in hour-ahead load forecasting.
This paper presents a robust short-term electrical load forecasting framework that can capture variations in building operation, regardless of building type and location. Nine different hybrids of recurrent neural networks and clustering are explored. The test cases involve five commercial buildings of five different building types, i.e., academic, research laboratory, office, school and grocery store, located at five different locations in Bangkok-Thailand, Hyderabad-India, Virginia-USA, New York-USA, and Massachusetts-USA. Load forecasting results indicate that the deep learning algorithms implemented in this paper deliver 20–45% improvement in load forecasting performance as compared to the current state-of-the-art results for both hour-ahead and 24-ahead load forecasting. With respect to sensitivity analysis, it is found that: (i) the use of hybrid deep learning algorithms can take as less as one month of data to deliver satisfactory hour-ahead load prediction, (ii) similar to the clustering technique, 15-min resolution data, if available, delivers 30% improvement in hour-ahead load forecasting, and (iii) the formulated methods are found to be robust against weather forecasting errors. Lastly, the forecasting results across all five buildings validate the robustness of the proposed deep learning framework for the short-term building-level electrical load forecasting tasks.
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