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This paper describes the development and modeling of a remotely operated scaled multi-wheeled combat vehicle (ROMWCV) using system identification methodology for heading angle tracking. The vehicle was developed at the vehicle dynamics and crash research (VDCR) Lab at the University of Ontario Institute of Technology (UOIT) to analyze the characteristics of the full-size model. For such vehicles, the development of controllers is considered the most crucial issue. In this paper, the ROMWCV is developed first. An experimental test was carried out to record and analyze the vehicle input/output signals in open loop system, which is considered a multi-input-single-output (MISO) system. Subsequently, a fuzzy logic controller (FLC) was developed for heading angle tracking. The experiments showed that it was feasible to represent the dynamic characteristics of the vehicle using the system identification technique. The estimation and validation results demonstrated that the obtained identified model was able to explain 88.44% of the output variation. In addition, the developed FLC showed a good heading angle tracking.
This paper presents a redundantly actuated and over-constrained 2RPU-2SPR parallel manipulator with two rotational and one translational coupling degrees of freedom. The kinematics analysis is firstly carried out and the mapping relationship of the velocity, acceleration and the independent parameters between the actuator joint and the moving platform are deduced by using the vector dot product and cross product operation. By employing d′Alembert′s principle and the principle of virtual work, the dynamics equilibrium equation is derived, and the simplified dynamics mathematical model of the parallel manipulator is further derived. Simultaneously, the generalized inertia matrix which can characterize the acceleration performance between joint space and operation space is further separated, and the performance indices including the dynamics dexterity, inertia coupling characteristics, energy transmission efficiency and driving force/torque balance are introduced. The analysis results show that the proposed redundantly actuated and over-constrained 2RPU-2SPR parallel manipulator in comparison with the existing non-redundant one has better dynamic comprehensive performance, which can be demonstrated practically by the successful application of the parallel kinematic machine head module of the hybrid machine tool.
Research has demonstrated a significant overlap between sleep issues and other medical conditions. In this paper, we consider mild difficulty in falling asleep (MDFA). Recognition of MDFA has the potential to assist in the provision of appropriate treatment plans for both sleep issues and related medical conditions. An issue in the diagnosis of MDFA lies in subjectivity. To address this issue, a decision support tool based on dual-modal physiological feature fusion which is able to automatically identify MDFA is proposed in this study. Special attention is given to the problem of how to extract candidate features and fuse dual-modal features. Following the identification of the optimal feature set, this study considers the correlations between each feature and class and evaluates correlations between the inter-modality features. Finally, the recognition accuracy was measured using 10-fold cross validation. The experimental results for our method demonstrate improved performance. The highest recognition rate of MDFA using the optimal feature set can reach 96.22%. Based on the results of current study, the authors will, in projected future research, develop a real-time MDFA recognition system.
Due to the frequent occurrence of various emergencies in recent years, people have put forward higher requirements on the emergency supply chain management. It is of great significance to explore the key management indicators of emergency supply chain for its management and efficient operation. In order to reveal the essence of emergency supply chain management, production, procurement, distribution, storage, use, recycling and other emergencies, supply chain links are considered to establish an emergency supply chain management index system to identify the key influencing factors in the emergency supply chain. The emergency supply chain involves many management elements and the traditional qualitative analysis and comprehensive evaluation methods have their shortcomings in practice. In order to get a more suitable method, a novel evaluation model is proposed, based on Rough set – house of quality method. In this paper, Rough set is used to filter the indexes, eliminate redundant indicators, and simplify many management indicators of the emergency supply chain system to a few core indicators. Then, the house of quality is used to analyze and sort the core index to get the key management index of emergency supply chain. The effectiveness of the proposed evaluation model is validated through a series of numerical experiments. The experimental results also show that the proposed evaluation model can assist decision makers in optimizing the emergency supply chain procedure and improving the efficiency of accident rescue.
In this paper, we report on the identification and modeling of unknown and higher order processes into first order plus dead time (FOPDT) plants based on the limit cycle information obtained from a single relay feedback test with an online fractional order proportional integral (FOPI) controller. The parameters of the test processes are accurately determined by the state space method while the FOPI controller settings are re-tuned to achieve enhanced performance based on the identified model parameters based on the balanced-tuning method. A new performance index, integral time fractional order absolute error (ITFIAE) is introduced in this paper for balanced tuning of fractional order (FO) controllers. It requires minimum design specifications without a-priori knowledge of gain and phase crossover frequencies and is done non-iteratively without disrupting the closed loop. Four test processes and experimental analysis on a coupled tank system (CTS) validate the theory proposed.
The H∞ state estimation problem for a class of stochastic neural networks with Markovian jumping parameters and leakage delay is investigated in this paper. By employing a suitable Lyapunov functional and inequality technic, the sufficient conditions for exponential stability as well as prescribed H∞ norm level of the state estimation error system are proposed and verified, and all obtained results are expressed in terms of strict linear matrix inequalities (LMIs). Examples and simulations are presented to show the effectiveness of the proposed methods, at the same time, the effect of leakage delay on stability of neural networks system and on the attenuation level of state estimator are discussed.
The choice of fulcrums for control of socio-economic systems represented by directed weighted signed graphs is a topic of current interest. This article proposes a new method for identifying nodes of impact and influential nodes, which will provide a guaranteed positive system response over the growth model. The task is posed as an optimization problem to maximize the ratio of the norms of the accumulated increments of the growth vector and the exogenous impact vector. The algorithm is reduced to solving a quadratic programming problem with nonlinear restrictions. The selection of the most effective vertices is based on the cumulative gains of the component projections onto the solution vector. Numerical examples are provided to illustrate the effectiveness of the proposed method.
The growth of environmental energy harvesting has been explosive in wireless computing systems especially when replacing or recharging batteries manually is impracticable. This work investigates the scheduling of periodic weekly hard real-time tasks under energy constraints. Based on this motivation, we proposed a real-time scheduling algorithm, namely energy guarantee dynamic voltage and frequency scaling (EG-DVFS), that utilizes the earliest deadline-harvesting (ED-H) scheduling algorithm combined with dynamic voltage and frequency scaling. This one is qualified as real-time since tasks must satisfy their timing constraints. We assume that the preemptable tasks receive dynamic priorities according to the earliest deadline first (EDF) rule. EG-DVFS adjusts the processor′s behavior by characterizing the properties of the energy source module, capacity of the stored energy as well as the harvested energy in a future duration. Specifically, tasks are executed at full processor speed if the amount of energy in the battery is enough to finish its execution. Otherwise, the processor slows down task execution to the lowest possible processor speed while still guaranteeing to meet all the timing constraints. EG-DVFS mainly depends on the on-line computation of the slack time and the slack energy with dynamic voltage and frequency selection in order to achieve an improved system performance. Experimental results show that EG-DVFS can achieve capacity savings up of up to 33% when compared to ED-H.
This paper proposes fuzzy model predictive control (FMPC) strategies for nonlinear interconnected systems based mainly on a system decomposition approach. First, the Takagi-Sugeno (TS) fuzzy model is formulated in such a way to describe the behavior of the nonlinear system. Based on that description, a fuzzy model predictive control is determined. The system under consideration is decomposed into several subsystems. For each subsystem, the main idea consists of the decomposition of the control action into two parts: The decentralized part contains the parameters of the subsystem and the centralized part contains the elements of other subsystems. According to such decomposition, two strategies are deflned aiming to circumvent the problems caused by interconnection between subsystems. The feasibility and e–ciency of the proposed method are illustrated through numerical examples.
The optimal test sequence design for fault diagnosis is a challenging NP-complete problem. An improved difierential evolution (DE) algorithm with additional inertial velocity term called inertial velocity difierential evolution (IVDE) is proposed to solve the optimal test sequence problem (OTP) in complicated electronic system. The proposed IVDE algorithm is constructed based on adaptive difierential evolution algorithm. And it is used to optimize the test sequence sets with a new individual fltness function including the index of fault isolation rate (FIR) satisfled and generate diagnostic decision tree to decrease the test sets and the test cost. The simulation results show that IVDE algorithm can cut down the test cost with the satisfled FIR. Compared with the other algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA), IVDE can get better solution to OTP.
A Fourier kernel based time-frequency transform is a proven candidate for non-stationary signal analysis and pattern recognition because of its ability to predict time localized spectrum and global phase reference characteristics. However, it suffers from heavy computational overhead and large execution time. The paper, therefore, uses a novel fast discrete sparse S-transform (SST) suitable for extracting time frequency response to monitor non-stationary signal parameters, which can be ultimately used for disturbance detection, and their pattern classification. From the sparse S-transform matrix, some relevant features have been extracted which are used to distinguish among different non-stationary signals by a fuzzy decision tree based classifier. This algorithm is robust under noisy conditions. Various power quality as well as chirp signals have been simulated and tested with the proposed technique in noisy conditions as well. Some real time mechanical faulty signals have been collected to demonstrate the efficiency of the proposed algorithm. All the simulation results imply that the proposed technique is very much efficient.
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