1,256 publications from this institution
The micro-vibrations generated by mechanical systems onboard spacecraft can affect the performance of instruments with high pointing accuracy and stability. This paper provides a micro-vibration generating control system based on Real Time Application Interface (RTAI), a real time Linux extension, for ground tests purposes. The vibration generating control algorithm is filtered-X adaptive inverse control method. A voice coil actuator is used in the vibration generating experiments. The experiments show that the system can generate different frequency precision sine vibration whose error is less than 2 mg which is in environmental and electrical noise level.
No abstract is provided for this article.
This article proposes a resource-aware triggering-based dynamic controller codesign scheme for Markov jump systems (MJSs) to fulfill both disturbance rejection and computational resources saving goals. Specifically, a self-trigger strategy is presented for precomputing the next execution time for state measurement, executing the controller, and updating the actuators using the current sampled information. To achieve a desired system performance and reduce the resource occupation, the self-triggering parameters and the gains of state feedback controller are codesigned. Moreover, a dynamic triggering technique is employed to improve the system performance by adapting the trigger coefficients to the different subsystems of MJSs. The effectiveness of the proposed method is demonstrated via two examples.
Recently, due to the difficulty of collecting condition data covering all mechanical fault types in industrial scenarios, the fault diagnosis problem under incomplete data is receiving increasing attention where no target prior information can be available. The existing open-set or universal domain adaptation (DA) diagnosis methods typically treat private fault samples in the target as a generalized “unknown” fault class, neglecting their inherent structure. This oversight can lead to confusion in latent feature space representations and difficulties in separating unknown samples. Therefore, a universal DA method with unsupervised clustering is developed to explore the intrinsic structure of the target samples for mechanical fault diagnosis, where multi-source information on different working conditions is considered to transfer complementary knowledge. First, a composite clustering metric combining single-domain and cross-domain evaluation is constructed to recognize shared and unknown health classes on source–target domains. Second, to alleviate the intra-class shift while enlarging the inter-class gap, a class-wise DA algorithm is suggested which operates on the basis of maximum mean discrepancy. Finally, an entropy regularization criterion is utilized to facilitate clustering of different health classes. The efficacy of the presented approach in the fault diagnosis issues when monitoring data is inadequate has been verified through extensive experiments on three rotating machinery datasets.
In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.
This paper is concerned with the problem of general output feedback stabilization for fractional order linear time-invariant (FO-LTI) systems with the fractional commensurate order<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mn>0</mml:mn><mml:mo><</mml:mo><mml:mi>α</mml:mi><mml:mo><</mml:mo><mml:mn>2</mml:mn></mml:math>. The objective is to design suitable output feedback controllers that guarantee the stability of the resulting closed-loop systems. Based on the slack variable method and our previous stability criteria, some new results in the form of linear matrix inequality (LMI) are developed to the static and dynamic output feedback controllers synthesis for the FO-LTI system with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mn>0</mml:mn><mml:mo><</mml:mo><mml:mi>α</mml:mi><mml:mo><</mml:mo><mml:mn>1</mml:mn></mml:math>. Furthermore, the results are extended to stabilize the FO-LTI systems with<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>α</mml:mi><mml:mo><</mml:mo><mml:mn>2</mml:mn></mml:math>. Finally, robust output feedback control is discussed. Numerical examples are given to illustrate the effectiveness of the proposed design methods.
In the operation of practical systems, it is often a challenging task to deal with limited knowledge of the system dynamics, therefore, it is needed to obtain a framework for the simultaneous learning and control of dynamic systems, able to cope with uncertain dynamics. This paper proposes a model-free aperiodic tracking control method based on MAXQ hierarchical reinforcement learning for unknown dynamics in discrete-time systems. Firstly, a MAXQ hierarchical framework is developed for aperiodic tracking control that decomposes the task into pre-control and accurate control subtasks. The former achieves sub-optimal tracking which prompts the implementation of accurate control. The latter implements aperiodic tracking based on the pre-control result to reduce resource occupation. The structure of the MAXQ framework simplifies the complexity of the tracking task, and the incorporation of pre-control accelerates the learning process in the formal control stage. Secondly, considering the disparities between the control inputs and the control update instants, pre-control is decomposed to learn the triggering strategy and control policy, respectively. Meanwhile, the control policy learns optimal control inputs by minimizing the cost function. Similarly, the triggering strategy and control policy are also separately learned in accurate control. In contrast to traditional aperiodic triggering mechanisms, in accurate control stage, the triggering strategy is developed based on the cumulative error of the pre-control result, thus avoiding the influence of accidental factors and the cumulative effects of errors, enhancing system robustness. Thirdly, the proposed tracking control is derived by using only the input, output, and reference signal data from the system, without relying on system dynamics. It is applicable to both linear and nonlinear systems, demonstrating strong generalizability. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed method.
No abstract is provided for this article.
This paper deals with the problem of observer-based controller design for a class of nonlinear systems subject to unknown inputs. A novel method is presented to design a controller using estimated state variables which guarantees all the state variables of the closed-loop system converge to the vicinity of the origin and stay there forever. This is done via satisfying several sufficient conditions in terms of nonlinear matrix inequalities. In light of linear algebra, particularly matrix decompositions, the achieved conditions will be converted to a Linear Matrix Inequality (LMI) problem to facilitate the procedure of computing the observer and controller gains. Finally, the effectiveness of the proposed method is illustrated by implementing on a highly nonlinear chaotic system.
No abstract is provided for this article.
Mobile receiver complexity persists as one of the key areas of concern in the adoption of MIMO technologies for systems such as high-speed downlink packet access in UMTS. This is particularly the case for high-order modulation schemes such as 16- and 64-QAM, where optimum a posteriori probability detection becomes too complex, and sub-optimal detection based on serial or parallel interference cancellation performs poorly. The above issues are successfully tackled by introducing a judicious layered encoding process at the transmitter which, once appropriately exploited at the receiver, allows considerable reductions in computational complexity in addition to improvements in performance. Also, the applicability of high-order modulations with layered encoding is investigated using system-level simulations for a 2-cell and a 7-cell urban scenario. It is shown that high-order modulations can be used in a substantial area of the cell for both investigated scenarios.
In this paper, a mathematical model for vehicle-occupant frontal crash is developed. The developed model is represented as a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The springs and dampers in the model are nonlinear piecewise functions of displacements and velocities respectively. More specifically, a genetic algorithm (GA) approach is proposed for estimating the parameters of vehicle front structure and restraint system. Finally, it is shown that the obtained model can accurately reproduce the real crash test data taken from the National Highway Traffic Safety Administration (NHTSA). The maximum dynamic crash of the vehicle model is 0.05% less than that in the real crash test. The displacement of the occupant is 0.09% larger than that from the crash test. Improvement of the model accuracy is also observed from the time at maximum displacement and the rebound velocities for both the vehicle and occupant.
