Composite Learning Control With Application to Inverted Pendulums

Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem.Comment: 5 pages, 6 figures, conference submissio

Neural Network Observer-Based Finite-Time Formation Control of Mobile Robots

This paper addresses the leader-following formation problem of nonholonomic mobile robots. In the formation, only the pose (i.e., the position and direction angle) of the leader robot can be obtained by the follower. First, the leader-following formation is transformed into special trajectory tracking. And then, a neural network (NN) finite-time observer of the follower robot is designed to estimate the dynamics of the leader robot. Finally, finite-time formation control laws are developed for the follower robot to track the leader robot in the desired separation and bearing in finite time. The effectiveness of the proposed NN finite-time observer and the formation control laws are illustrated by both qualitative analysis and simulation results

Composite learning adaptive backstepping control using neural networks with compact supports

© 2019 John Wiley & Sons, Ltd. The ability to learn is crucial for neural network (NN) control as it is able to enhance the overall stability and robustness of control systems. In this study, a composite learning control strategy is proposed for a class of strict-feedback nonlinear systems with mismatched uncertainties, where raised-cosine radial basis function NNs with compact supports are applied to approximate system uncertainties. Both online historical data and instantaneous data are utilized to update NN weights. Practical exponential stability of the closed-loop system is established under a weak excitation condition termed interval excitation. The proposed approach ensures fast parameter convergence, implying an exact estimation of plant uncertainties, without the trajectory of NN inputs being recurrent and the time derivation of plant states. The raised-cosine radial basis function NNs applied not only reduces computational cost but also facilitates the exact determination of a subregressor activated along any trajectory of NN inputs so that the interval excitation condition is verifiable. Numerical results have verified validity and superiority of the proposed approach

Boosting the eco-friendly sharing economy: The effect of gasoline prices on bikeshare ridership in three U.S. metropolises

Transportation has become the largest CO2 emitter in the United States in recent years with low gasoline prices standing out from many contributors. As demand side changes are called for reducing car use, the fast-growing sharing economy shows great potential to shift travel demand away from single-occupancy vehicles. Although previous inter-disciplinary research on shared mobility has explored its multitudes of benefits, it is yet to be investigated how the uptake of this eco-friendly sharing scheme is affected by gasoline prices. In this study, we examine the impact of gasoline prices on the use of bikeshare programs in three U.S. metropolises: New York City, Boston, and Chicago. Using bikeshare trip data, we estimate the impact of citywide gasoline prices on both bikeshare trip duration and trip frequency in a generalized linear regression setting. The results suggest that gasoline prices significantly affect bikeshare trip frequency and duration, with a noticeable surge in short trips. Doubling gasoline prices could help save an average of 1933 gallons of gasoline per day in the three cities, approximately 0.04% of the U.S. daily per capita gasoline consumption. Our findings indicate that fuel pricing could be an effective policy tool to support technology driven eco-friendly sharing mobility and boost sustainable transportation

Hamiltonian-Driven Adaptive Dynamic Programming with Efficient Experience Replay

This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton–Jacobi–Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms of the convergence of the adaptive critic design and the stability of the closed-loop systems, where parameter convergence can be achieved under a weakened excitation condition. Simulation studies are investigated to verify the efficacy of the presented design scheme

Application of the Variational Mode Decomposition (VMD) method to river tides

Tides in fluvial estuaries are distorted by non-stationary river discharge, which makes the analysis of estuarine water levels less accurate when using the conventional tidal analysis method. As a powerful and widely-used method for non-stationary and nonlinear time series, the application of Variational Mode Decomposition (VMD) method to non-stationary tides is nonexistent. This paper aims to illustrate and verify the suitability of the VMD method as a new tidal analysis tool for river tides. The efficiency of VMD is validated by the measurements from the Columbia River Estuary. VMD strictly divides different tidal species into different modes, and thus avoids mode mixing. Compared to VMD, Ensemble Empirical Mode Decomposition (EEMD), which is another commonly-used method, fails to completely solve the problem of mode mixing. The observed water levels at Longview station are decomposed into 12 modes via VMD. Based on the mean periods and amplitudes of each VMD mode, the 12 VMD modes sequentially correspond to the tidal species from the sub-tides (D0), diurnal tides (D1), semi-diurnal tides (D2), and up to D11 tides. The non-stationary characteristics of tides influenced by river discharge are accurately captured by VMD without mode mixing. The results also show that the EEMD and VMD modes can capture the subtidal signals better than the nonstationary tidal harmonic analysis tool (NS_TIDE). As a general method, the VMD model can also be used for other research purposes related to non-stationary tides, such as detiding

Phylogeny of the genus Morus (Urticales: Moraceae) inferred from ITS and trnL-F sequences

Both nuclear ribosomal ITS and chloroplast trnL-F sequences were acquired from 13 mulberry genotypes belonging to nine species and three varieties, and one paper mulberry. The later belongs to genus B. papyrifera, designed as outgroup, and were analyzed. Within the genus Morus, the sequence diversity of ITS was much higher than that of trnL-F. The results of phylogenetic analyses based on these data (separately or combined) show that the genus Morus is monophyletic group. Strict consensus tree obtained through the Neighbor-joining method can be divided into five major clades in the genus Morus, according to combined sequence data. M. bombycis, M. alba var. venose formed clades A and B, respectively. Clade C comprises of 5 species; M. rotundiloba, M. atropurpurea, M. mongolica, M. australi, and M. mongolica var. diabolica. Clade D comprises of 3 species; M. wittiorum, M. laevigata, and M. alba. Clade E comprises of 2 species; M. multicaulis, and M.alba var. macrophylla. The results from cluster analysis were basically in agreement with the existing morphologic classification.African Journal of Biotechnology Vol. 4 (6), pp. 563-569, 200

The performance of the copulas in estimating the joint probability of extreme waves and surges along east coasts of the mainland China

In designing coastal and nearshore structures, the joint probability of the wave heights and storm surges is essential in determining the possible highest total water level. The key elements to accurately estimate the joint probability are the appropriate sampling of the extreme values and selection of probability functions for the analysis. This study is to provide a full assessment of the performance of the different methods employed in the joint probability analysis. The bivariate extreme wave height and surge samples are analysed using 2 different probability distributions and the performance of 4 copulas, namely: Gumbel–Hougaard copula, Clayton copula, Frank copula and Galambos copula, is assessed. The possible highest total water levels for 100-year return period along the coastline of the mainland China are estimated by the joint probability method with the Gumbel–Hougaard copula. The results show that the wave heights and surges are highly correlated in the areas of dense typhoon paths. The distributions of the possible highest total water levels show a higher value in the southeast coast and lower value in the north. The results also indicate that at the locations where the sea states are energetic, the joint probability approach can improve the accuracy of design