This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to input time delay and unknown nonlinear dynamics. First, predictor-based compensation is constructed address the effect of in closed-loop system. Then, logic system (FLS) employed as function approximator nonlinearities. Finally, enhance transient response, novel parameter estimation error-based finite-time (FT) algorithm developed online update FLS weights, which differs from traditional methods,...

The application of machine learning-based state-of-health (SOH) prediction is hindered by the large demand for training data. To conquer this defect, a flexible and easily transferred SOH scheme lithium-ion battery packs developed. First, charging duration predefined voltage range hired as health feature to quantify capacity degradation. Then, long short-term memory (LSTM) network transfer learning (TL) with fine-tuning strategy are incorporated constitute cell mean model (CMM) partial Next,...

Accurate estimation of the state charge (SOC) lithium-ion battery packs remains challenging due to inconsistencies among cells. To achieve precise SOC packs, first, a long short-term memory (LSTM) recurrent neural network (RNN)-based model is constructed characterize electrical performance, and rolling learning method proposed update parameters for improving accuracy. Then, an improved square root-cubature Kalman filter (SRCKF) designed together with multi-innovation technique estimate...

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on technology mainly focus avoiding obstacles; however, few studies have accounted dynamic lane changing based some certain assumptions, such as the speed constant or terminal state known in advance. In this study, typical scenario developed with consideration of preceding lagging vehicles road. Based local trajectory generated by global positioning system, path planning model are...

In this paper, we address the energy maximization problem of wave converters (WEC) subject to nonlinearities and constraints, present an efficient online control strategy based on principle adaptive dynamic programming (ADP) for solving associated Hamilton-Jacobi-Bellman equation. To solve derived constrained nonlinear optimal problem, a critic neural network (NN) is used approximate time-dependant cost value then calculate practical suboptimal causal action. The proposed novel WEC leads...

Autonomous decision-making for ships to avoid collision is core the autonomous navigation of intelligent ships. In recent years, related research has shown explosive growth. However, owing complex constraints environments, Convention International Regulations Preventing Collisions at Sea, 1972 (COLREGs), and underactuated characteristics ships, it extremely challenging design a algorithm avoidance (CA) that practically useful. Based on investigation many studies, current algorithms can be...

Boundary control strategy is developed to analyze the vibration problem of offshore ocean thermal energy conversion (OTEC) system as well constrain bottom tension and top motion. To provide an accurate dynamic behavior for OTEC system, this distributed parameter modeled formulated with a governing equation boundary conditions (PDE-ODEs model). Two robust adaptive controllers are designed disposed at endpoints stability controlled under unknown disturbances achieved. After selecting relevant...

The equivalent fuel consumption minimum strategy (ECMS) based on the Pontryagin's principle (PMP) enables real-time energy management optimization of plug-in hybrid electric vehicles (PHEVs). However, it remains challenging to accurately determine factor (EF). In this study, an analytical expression optimal EF boundary is addressed facilitate more efficient search EF. deterministic derived from Hamilton equation PMP. By combining and differential evolution algorithm, a novel fusion adaptive...

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for class of nonlinear bilateral teleoperation systems. First, an scheme is applied estimate the upper bound delay, which can resolve predicament that has significant impacts on stability Then, radial basis function neural networks (RBFNNs) are utilized estimating systems, including operator, environmental models. Novel adaptation laws introduced address...

A reliable, efficient, and simple control is presented validated for a quarter-car active suspension system equipped with an electro-hydraulic actuator. Unlike the existing techniques, this does not use any function approximation, e.g., neural networks (NNs) or fuzzy-logic systems (FLSs), while unmolded dynamics, including hydraulic actuator behavior, can be accommodated effectively. Hence, heavy computational costs tedious parameter tuning phase remedied. Moreover, both transient...

Abstract Inorganic materials assembled with rigid elements such as crystals or graphitized carbon generally show brittleness and hardness. However, it is found that both TiO 2 ceramic crystal nanofibers (NFs) NFs superior flexibility, in which the former are surprisingly knottable latter exhibit excellent bending‐resilience property. The different flexure mechanisms revealed by fabricating composite of these two constituents find can be recovered to original states after releasing external...

This paper addresses the fundamental theoretical development of a linear optimal noncausal control for wave energy converters (WECs) in closed analytic form. It is well known that WEC problem, i.e., future information contributes to present action. provides reliable, efficient, and simple controller with guaranteed stability problem. The proposed consists causal state feedback part an anticausal feedforward incorporate influence incoming waves. closed-loop system proven. contribution term...

This paper presents a robust adaptive optimal control strategy for wave energy converters (WECs). We first propose anew estimator in simple form to address modeling uncertainties and formulate the of WECs as an problem. Then, novel maximization is developed based on concept dynamic programming (ADP), where critic neural network (NN) used approximate time-dependent cost value. To achieve guaranteed convergence, recently proposed law parameter estimation error further tailored update weights...

This paper proposes a new control approach for full-car active suspension systems with unknown nonlinearities.The main advantage of this is that the uncertainties and nonlinearities in system can be handled without using any function approximator (e.g.neural networks (NNs), fuzzy logic (FLSs)), associated online adaptation.Hence, heavy computational cost sluggish learning phase to achieve convergence remedied.To maintain transient steady-state responses, coordinate error transformation...

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in intelligent battery management systems (BMSs). The current RUL methods are mainly developed based on offline training, which limited by sufficiency and reliability available data. To address this problem, paper presents a method for the capacity estimation Box-Cox transformation (BCT). Firstly, effective aging features (AFs) extracted from electrical thermal characteristics variation terms cyclic...

Ensuring running stability and minimising wheel tread wear are critical factors for enhancing the dynamic performance of high-speed trains. As an efficient method improving vehicle performances, previous studies on suspension parameter optimisation have not fully considered these two factors. It is essential to pay attention at extreme wheel-rail contact states, evolution needs be revealed instead investigating only through index like number. This research aims investigate stability/wear...

Intelligent plug-in hybrid electric vehicles (IPHEVs), with advanced environmental sensing and integrated communication capabilities, present tremendous autonomy in drive decision-making high efficiency energy economy. In this context, the key control of IPHEVs gradually evolves from management among components powertrain to co-operative velocity planning management, which, nonetheless, are strongly coupled. To tackle it, an intelligent strategy based on adaptive dynamic programming (ADP) is...