With the growing demand for robots in emerging fields, such as smart medical and home services, their ability to interact with soft environments has received increased attention. Nevertheless, an overlooked issue is that the inadequate description of soft environments using a linear model may significantly diminish the accuracy of interaction control. In this article, a neural-network-based impedance control framework is proposed for robots to physically interact with soft environments and optimize interaction performance. Specifically, a nonlinear definition of soft environments is introduced based on the Hunt–Crossley (HC) model, with parameter identification utilizing a data-driven technique. Regarding system performance evaluated by a cost function, the determination of interaction behavior described by the impedance model is transformed into an optimal control problem. Moreover, to address model uncertainties, the original optimal control problem is redefined using a modified cost function with a constructed auxiliary system. Then, a critic network is employed to approximate the nonlinear optimal solution, thereby avoiding complicated mathematical derivations. Finally, the effectiveness of the proposed impedance adaptation strategy is validated through both simulations and experiments. Numerical results indicate that both the convergent cost and total cost are significantly reduced based on the proposed method compared to the linear-model-based impedance control, particularly for materials with viscoelastic properties, achieving a reduction of up to 30%.

In this article, we propose an adaptive neural network event-triggered control (ETC) to suppress the vibration of a high-rise building under uncertainty. This neural network efficiently handles unmodeled components in the system and approximates unknown nonlinear functions. An ETC mechanism with a relative threshold strategy is introduced, balancing the control effectiveness of the active mass damper (AMD) and extending operational lifespan. The ultimate boundedness of the system is verified using the Lyapunov direct method, ensuring convergence of vibration displacement and acceleration toward zero. The efficacy of this control scheme is demonstrated through detailed numerical simulations and experimental analyses.

Flapping-wing flying robots, as a newly emerging research hotspot, have attracted more and more researchers' attention. Compared with traditional aircraft, flapping-wing flying robots have the characteristics of high flight efficiency, good concealment, and have a wide range of application prospects. As an important power mechanism of aircraft, the research of wing is very important. In this paper, we design a wing structure that can realize the active bending of wings, which can well imitate the bending pattern of wings of birds in the natural flight process. At the same time, a wind tunnel test was carried out to measure the lift resistance of the single wing and the folded wing under the same power. The results show that the folded wing has higher flight efficiency under the same power.

Bionic flapping-wing aircraft is a strongly coupled and underactuated system, and its dynamic modeling and intelligent control are still a major challenge. In this paper, we develop an 3-dimensional dynamic model for the flapping-wing aircraft designed by our team. The aerodynamic performance of the wing is analysed by the blade element method and a theoretical calculation model is obtained. Based on wind tunnel experiment, an aerodynamic model is identified for the V-Tail, the attitude control ruddervators. Further, we build a virtual simulation environment based on gym, which is verified by the outdoor flight data. This work provides the basis for intelligent control of flapping wing aircraft.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.

Automatic lane-changing is a complex and critical task for autonomous vehicle control. Existing researches on autonomous vehicle technology mainly focus on avoiding obstacles; however, few studies have accounted for dynamic lane changing based on some certain assumptions, such as the lane-changing speed is constant or the terminal state is known in advance. In this study, a typical lane-changing scenario is developed with the consideration of preceding and lagging vehicles on the road. Based on the local trajectory generated by the global positioning system, a path planning model and a speed planning model are respectively established through the cubic polynomial interpolation. To guarantee the driving safety, passenger comfort and vehicle efficiency, a comprehensive trajectory optimization function is proposed according to the path planning model and speed planning model. In addition, a dynamic decoupling model is established to solve the problems of real-time application to provide viable solutions. The simulations and real vehicle validations are conducted, and the results highlight that the proposed method can generate a satisfactory lane-changing trajectory for automatic lane-changing actions.

As a kind of large flexible structure, high-rise buildings need to consider wind-resistant and anti-seismic problems for the safety of occupants and properties, especially in coastal areas. This article proposes an infinite dimensional model and an adaptive boundary control law for an active mass damper (AMD) on this question. The dynamic model of the high-rise building is a combination of some storeys which have flexible walls and rigid floors under a series of physical conditions. Then the adaptive boundary controller is acted on an AMD which is equipped on the top floor, in order to suppress the vibration of every floor and guarantee the comfort of residents. Moreover, simulations and experiments are carried out on a two-floor flexible building to illustrate the effectiveness of the proposed control strategy.

A reliable, efficient, and simple control is presented and validated for a quarter-car active suspension system equipped with an electro-hydraulic actuator. Unlike the existing techniques, this control does not use any function approximation, e.g., neural networks (NNs) or fuzzy-logic systems (FLSs), while the unmolded dynamics, including the hydraulic actuator behavior, can be accommodated effectively. Hence, the heavy computational costs and tedious parameter tuning phase can be remedied. Moreover, both the transient and steady-state suspension performance can be retained by incorporating prescribed performance functions (PPFs) into the control implementation. This guaranteed performance is particularly useful for guaranteeing the safe operation of suspension systems. Apart from theoretical studies, some practical considerations of control implementation and several parameter tuning guidelines are suggested. Experimental results based on a practical quarter-car active suspension test-rig demonstrate that this control can obtain a superior performance and has better computational efficiency over several other control methods.

This article studies a single boundary regulator for the flexible tail of an autonomous robotic fish to implement complex oscillating body motions. The dynamic model of the flexible tail is derived by Hamilton’s principle, and conforms to the partial nonuniform Euler–Bernoulli beam with the uneven parameters. Then, a boundary control at the body–tail junction is proposed to manipulate the oscillation of the flexible tail, which is given in the form as a torque. The exponential stability of the error system is deduced by the integral Lyapunov synthesis. For further considering the boundary disturbance at the same point with control and the distributed disturbance, a disturbance observer is proposed. By appropriately choosing the designed parameters, the uniformly ultimate boundedness with disturbances is proved and the tracking error converges to a small neighborhood of 0. Finally, some simulations are presented to illustrate the effectiveness of the proposed control.

This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.

The wave excitation force estimation and prediction play an important role in improving the performance of causal and noncausal controllers for wave energy converters (WECs). This article proposes a robust adaptive sliding-mode observer (ASMO) to estimate the wave excitation force subject to unknown disturbances and parametric uncertainties for a multimotion multifloat WEC, called M4. Both the convergence time and the estimation error can be explicitly bounded within expected limits by tuning the ASMO parameters, which are essentially beneficial for causal controllers to maintain the control performance. A fixed-time convergent sliding variable is designed to drive the estimation error into a small region within a fixed time. Due to the adaptive law, the overall system is proven to be finite-time stable, which allows explicit formulations of the convergence time and the estimation error. Moreover, based on the wave force estimation by the ASMO, an improved auto-regressive (AR) model whose coefficients are updated by online training is developed to predict the wave excitation force. The prediction errors can also be explicitly estimated to achieve guaranteed control performance for the noncausal controller requiring future excitation force. From the comparison based on a realistic sea wave gathered from Cornwall, U.K., it can be found that compared with the conventional Kalman filter, the ASMO achieves a smaller steady-state estimation error and has satisfactory robustness performance against 30% model mismatch.

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

This article proposes a novel nonlinear receding horizon optimal control algorithm for wave energy converter (WECs) with nonlinear dynamics. It is well accepted that the WEC control problem is essentially a noncausal constrained optimal control problem, where the energy output can be improved by incorporating the short-term wave prediction into the control synthesis. Inspired by this fact, we suggest a new nonlinear noncausal optimal control (NNOC) for WECs based on the principle of approximate dynamic programming, which can, first, explicitly use the wave prediction to improve the energy conversion efficiency; second, handle the state and control input constraints; third, reduce the computational burden. Different to the existing linear noncausal optimal control, the derived Hamilton-Jacobi-Bellman equation for NNOC does not have an analytic solution. To tackle this problem, a critic neural network (NN) is adopted to approximate its solution in a receding horizon manor. The weights of NN are determined via a policy iteration algorithm. The resulting NNOC consists of a causal state feedback part and a noncausal feedforward part to explicit incorporate wave prediction information. Numerical simulations are provided to verify the efficacy of the proposed NNOC method.

In this paper, vibration control and output constraint are considered for a Timoshenko beam system with input backlash and external disturbances. By integrating iterative learning control (ILC) into adaptive control, two dual-loop adaptive ILC schemes are proposed in the presence of the input backlash. Two observers are designed to estimate two bounded terms, which are divided from the backlash inputs. Based on the defined barrier composite energy function, all the signals are proved to be bounded in each iteration. Along the iteration axis: 1) the endpoint transverse displacements and the endpoint angle displacements are restrained; 2) the transverse vibrations and the rotation vibrations are suppressed to zero; and 3) the spatiotemporally varying disturbance and the time-varying disturbances are rejected. Simulations are provided to manifest the effectiveness of the proposed control laws.