A Controllable Nonlinear Bistable "Fishtail" Boosting Robotic Swimmer with Excellent Maneuverability and High Energy Efficiency.

High maneuverability and energy efficiency are crucial for underwater robots to perform tasks in engineering practice. Natural evolution empowers aquatic species with skills of agile and efficient swimming, which can be deliberately employed for better robotic swimmers. A critical issue for efficient robotic swimmers is the design and control of an appropriate propulsion system.

Evaluation of transformation invariant loss function with distance equilibrium in prediction of imaging photoplethysmography characteristics.

Unlabelled: . Monitoring changes in human heart rate variability (HRV) holds significant importance for protecting life and health. Studies have shown that Imaging Photoplethysmography (IPPG) based on ordinary color cameras can detect the color change of the skin pixel caused by cardiopulmonary system.

Neuroadaptive Control for Active Suspension Systems With Time-Varying Motion Constraints: A Feasibility-Condition-Free Method.

This work is devoted to solving the control problem of vehicle active suspension systems (ASSs) subject to time-varying dynamic constraints. An adaptive control scheme based on nonlinear state-dependent function (NSDF) is proposed to stabilize the vertical displacement of the vehicle body. It provides a reliable guarantee of driving safety, ride comfort, and operational stability.

Energy-Saving Robust Saturated Control for Active Suspension Systems via Employing Beneficial Nonlinearity and Disturbance.

This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control scheme is established to ensure a stable performance-oriented tracking control of the overall closed-loop system. Importantly, different from most existing control methods, the designed tracking controller purposely employs beneficial nonlinear stiffness and damping of a novel bioinspired reference model and deliberately utilizes useful disturbance response on the active suspension system, so as to improve the convergence speed and reduce control energy cost simultaneously.

Online Kernel Learning With Adaptive Bandwidth by Optimal Control Approach.

Online learning methods are designed to establish timely predictive models for machine learning problems. The methods for online learning of nonlinear systems are usually developed in the reproducing kernel Hilbert space (RKHS) associated with Gaussian kernel in which the kernel bandwidth is manually selected and remains steady during the entire modeling process in most cases. This setting may make the learning model rigid and inappropriate for complex data streams.

Online Identification of Nonlinear Stochastic Spatiotemporal System With Multiplicative Noise by Robust Optimal Control-Based Kernel Learning Method.

In this paper, we propose a novel kernel method for the online identification of stochastic nonlinear spatiotemporal dynamical systems using the robust control approach. By the difference method, the stochastic spatiotemporal (SST) systems driven by multiplicative noise are first transformed into a class of multi-input-multi-output-partially linear kernel models (PLKMs) with heterogeneous random terms. With the help of techniques established for reproducing kernel Hilbert space, the online learning problem is reasonably considered as an output feedback control problem for a group of time varying linear dynamical systems.

A tracked robot with novel bio-inspired passive "legs".

For track-based robots, an important aspect is the suppression design, which determines the trafficability and comfort of the whole system. The trafficability limits the robot's working capability, and the riding comfort limits the robot's working effectiveness, especially with some sensitive instruments mounted on or operated. To these aims, a track-based robot equipped with a novel passive bio-inspired suspension is designed and studied systematically in this paper.

Identification of Nonlinear Spatiotemporal Dynamical Systems With Nonuniform Observations Using Reproducing-Kernel-Based Integral Least Square Regulation.

The identification of nonlinear spatiotemporal dynamical systems given by partial differential equations has attracted a lot of attention in the past decades. Several methods, such as searching principle-based algorithms, partially linear kernel methods, and coupled lattice methods, have been developed to address the identification problems. However, most existing methods have some restrictions on sampling processes in that the sampling intervals should usually be very small and uniformly distributed in spatiotemporal domains.

Fuzzy Tracking Control for Nonlinear Networked Systems.

This paper studies the observer-based tracking control problem for discrete-time nonlinear networked control systems with parameter uncertainties and unmeasurable state variables. A network-induced constraint, i.e.

Vibration isolation by exploring bio-inspired structural nonlinearity.

Inspired by the limb structures of animals/insects in motion vibration control, a bio-inspired limb-like structure (LLS) is systematically studied for understanding and exploring its advantageous nonlinear function in passive vibration isolation. The bio-inspired system consists of asymmetric articulations (of different rod lengths) with inside vertical and horizontal springs (as animal muscle) of different linear stiffness. Mathematical modeling and analysis of the proposed LLS reveal that, (a) the system has very beneficial nonlinear stiffness which can provide flexible quasi-zero, zero and/or negative stiffness, and these nonlinear stiffness properties are adjustable or designable with structure parameters; (b) the asymmetric rod-length ratio and spring-stiffness ratio present very beneficial factors for tuning system equivalent stiffness; (c) the system loading capacity is also adjustable with the structure parameters which presents another flexible benefit in application.

Fuzzy sampled-data control for uncertain vehicle suspension systems.

This paper investigates the problem of sampled-data H∞ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy H∞ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances.

Robust adaptive learning of feedforward neural networks via LMI optimizations.

Feedforward neural networks (FNNs) have been extensively applied to various areas such as control, system identification, function approximation, pattern recognition etc. A novel robust control approach to the learning problems of FNNs is further investigated in this study in order to develop efficient learning algorithms which can be implemented with optimal parameter settings and considering noise effect in the data. To this aim, the learning problem of a FNN is cast into a robust output feedback control problem of a discrete time-varying linear dynamic system.

Understanding neuronal systems in movement control using Wiener/Volterra kernels: a dominant feature analysis.

Although Volterra kernels have been extensively applied in modelling and analysis of biological systems, the relationship between the kernel characteristics and physiologically important features under study is still not revealed clearly. In this study, the link between Volterra kernels and dynamic response of neural systems which control animal movements was investigated and demonstrated using a dominant feature analysis. The new results show an effective but simplified method to use Volterra or Wiener kernels to understand and classify the neural systems which are responsible for the fundamental movements such as flexion and extension of animal limbs, and importantly demonstrate how the neuron pathways in locusts control joint activities of low and high frequency and perform fundamental joint movements such as position, velocity and acceleration.

Online identification of nonlinear spatiotemporal systems using kernel learning approach.

The identification of nonlinear spatiotemporal systems is of significance to engineering practice, since it can always provide useful insight into the underlying nonlinear mechanism and physical characteristics under study. In this paper, nonlinear spatiotemporal system models are transformed into a class of multi-input-multi-output (MIMO) partially linear systems (PLSs), and an effective online identification algorithm is therefore proposed by using a pruning error minimization principle and least square support vector machines. It is shown that many benchmark physical and engineering systems can be transformed into MIMO-PLSs which keep some important physical spatiotemporal relationships and are very helpful in the identification and analysis of the underlying system.

An H(∞) control approach to robust learning of feedforward neural networks.

A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise.