In this paper, a new strategy is developed to plan the smooth path for mobile robots through an improved PSO algorithm in combination with the continuous high-degree Bezier curve. Rather than ...connecting several low-degree Bezier curve segments, the use of continuous high-degree Bezier curves facilitates the fulfillment of the requirement of high-order continuity such as the continuous curvature derivative, which is critical for the motion control of the mobile robots. On the other hand, the smooth path planning of mobile robots is mathematically an optimization problem that can be dealt with by evolutionary computation algorithms. In this regard, an improved particle swarm optimization (PSO) algorithm is proposed to tackle the local trapping and premature convergence issues. In the improved PSO algorithm, an adaptive fractional-order velocity is introduced to enforce some disturbances on the particle swarm according to its evolutionary state, thereby enhancing its capability of jumping out of the local minima and exploring the searching space more thoroughly. The superiority of the improved PSO algorithm is verified by comparing with several standard and modified PSO algorithms on some benchmark functions, and the advantages of the new strategy is also confirmed by several comprehensive simulation experiments for the smooth path planning of mobile robots.
•In this paper, a new strategy is developed to plan the smooth path for mobile robots.•An improved PSO algorithm is proposed in combination with the continuous high-degree Bezier curve.•An adaptive fractional-order velocity is introduced to enforce some disturbances on the particle.•The superiority of the improved PSO algorithm is verified by comparing with several standard benchmark functions.•The advantages of the new strategy is also confirmed by simulation experiments for the smooth path planning of mobile robots.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. In order ...to cater for more realistic transmission process of the neural signals, we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters. In the addressed neural network model, the delays among the interconnections are allowed to be different, which are more general than those in the existing literature. The incomplete information under consideration includes randomly occurring sensor saturations and quantizations. For the purpose of energy saving, an event-triggered state estimator is constructed and a sufficient condition is given under which the estimation error dynamics is exponentially ultimately bounded in the mean square. It is worth noting that the ultimate boundedness of the error dynamics is explicitly estimated. The characterization of the desired estimator gain is designed in terms of the solution to a certain matrix inequality. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed event-triggered state estimation scheme.
The dynamics of microbial communities are closely related to human health. Deep learning techniques have shown great potential in bioinformatics by demonstrating powerful data analysis capabilities ...in areas such as microbial data modeling and disease Prediction since recent years. Our paper explores new approaches combining deep learning techniques with microbial data to generate more precise predictive models for early diagnosis and treatment. This study is dedicated to developing a method for modeling microbial data and predicting diseases that incorporates deep learning. Multiple deep learning models combine data from microbial communities to improve prediction accuracy through an integrated learning strategy. Experiments were conducted on two primary disease datasets: Inflammatory Bowel Disease (IBD) and Colorectal Cancer (Colorectal). This research method’s AUC values were 0.897 and 0.876, which is an improvement compared to traditional machine learning methods. A new perspective on the study of disease mechanisms was provided by identifying highly correlated microbial markers through feature selection analysis. This study can effectively enhance the use of microbial data in disease prediction by integrating deep learning models, which provides powerful technical support for future clinical diagnosis and treatment.
This paper is concerned with the distributed H ∞ filtering problem for a class of discrete-time Markovian jump nonlinear time-delay systems with deficient statistics of mode transitions. The system ...measurements are collected through a lossy sensor network subject to randomly occurring quantization errors and randomly occurring packet dropouts. The description of deficient statistics of mode transitions that account for known, unknown, and uncertain transition probabilities is comprehensive. A distributed filter design scheme is outlined by explicitly characterizing the filter gains in terms of some matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filtering scheme.
In this technical note, the consensus control problem is investigated for a class of discrete time-varying stochastic multi-agent system subject to sensor saturations. An event-based mechanism is ...adopted where each agent updates the control input signal only when the pre-specified triggering condition is violated. To reflect the time-varying manner and characterize the transient consensus behavior, a new index for mean-square consensus is put forward to quantify the deviation level from individual agent to the average value of all agents' states. For a fixed network topology, the aim of the proposed problem is to design time-varying output-feedback controllers such that, at each time step, the mean-square consensus index of the closed-loop multi-agent system satisfies the pre-specified upper bound constraints subject to certain triggering mechanism. Both the existence conditions and the explicit expression of the desired controllers are established by resorting to the solutions to a set of recursive matrix inequalities. An illustrative simulation example is utilized to demonstrate the usefulness of the proposed algorithms.
This technical note is concerned with the finite-horizon H ∞ fault estimation problem for a class of nonlinear stochastic time-varying systems with both randomly occurring faults and fading channels. ...The system model (dynamical plant) is subject to Lipschitz-like nonlinearities and the faults occur in a random way governed by a set of Bernoulli distributed white sequences. The system measurements are transmitted through fading channels described by a modified stochastic Rice fading model. The purpose of the addressed problem is to design a time-varying fault estimator such that, in the presence of channel fading and randomly occurring faults, the influence from the exogenous disturbances onto the estimation errors is attenuated at the given level quantified by a H ∞ -norm in the mean square sense. By utilizing the stochastic analysis techniques, sufficient conditions are established to ensure that the dynamic system under consideration satisfies the prespecified performance constraint on the fault estimation, and then a recursive linear matrix inequality approach is employed to design the desired fault estimator gains. Simulation results demonstrate the effectiveness of the developed fault estimation design scheme.
This technical note is concerned with the sampled-data synchronization control problem for a class of dynamical networks. The sampling period considered here is assumed to be time-varying that ...switches between two different values in a random way with given probability. The addressed synchronization control problem is first formulated as an exponentially mean-square stabilization problem for a new class of dynamical networks that involve both the multiple probabilistic interval delays (MPIDs) and the sector-bounded nonlinearities (SBNs). Then, a novel Lyapunov functional is constructed to obtain sufficient conditions under which the dynamical network is exponentially mean-square stable. Both Gronwall's inequality and Jenson integral inequality are utilized to substantially simplify the derivation of the main results. Subsequently, a set of sampled-data synchronization controllers is designed in terms of the solution to certain matrix inequalities that can be solved effectively by using available software. Finally, a numerical simulation example is employed to show the effectiveness of the proposed sampled-data synchronization control scheme.
In this paper, the state estimation problem is investigated for a class of discrete-time complex networks subject to nonlinearities, mixed delays, and stochastic noises. A set of event-based state ...estimators is constructed so as to reduce unnecessary data transmissions in the communication channel. Compared with the traditional state estimator whose measurement signal is received under a periodic clock-driven rule, the event-based estimator only updates the measurement information from the sensors when the prespecified "event" is violated. Attention is focused on the analysis and design problem of the event-based estimators for the addressed discrete-time complex networks such that the estimation error is exponentially bounded in mean square. A combination of the stochastic analysis approach and Lyapunov theory is employed to obtain sufficient conditions for ensuring the existence of the desired estimators and the upper bound of the estimation error is also derived. By using the convex optimization technique, the gain parameters of the desired estimators are provided in an explicit form. Finally, a simulation example is used to demonstrate the effectiveness of the proposed estimation strategy.
In this paper, a unified framework is established to investigate both the quantized and the saturated control problems for a class of sampled-data systems under noisy sampling intervals. A random ...variable obeying the Erlang distribution is used to describe the noisy sampling intervals. In virtue of the matrix exponential, the sampled-data control system is transformed into an equivalent discrete-time stochastic system, and the aim of this paper is to design a quantized/saturated sampled-data controller such that the resulting discrete-time stochastic system is stochastically stable when the sampling error follows the Erlang distribution. In order to deal with the case of multiple control inputs, a confluent Vandermonde matrix approach is proposed in the design process. By using the Kronecker product operation and the matrix inequality techniques, the desired quantized/saturated controller gains are designed in terms of the solution to certain matrix inequalities. Finally, a simulation example is exploited to verify the effectiveness of the proposed design approach.
PDF
