Dual sliding mode coordinated control of manipulator grasping system with visual assistance

The operation efficiency of the manipulator is placed in the primary position in automatic production. This paper proposes a coordinated control strategy for joint servo and visual servo to enable timely transfer and accurate gripping in the working area. Aiming at the issues of chattering and slow convergence of traditional sliding mode controller, a fast variable power reaching rate on the basis of the non-singular fast terminal sliding mode controller is proposed, which can effectively reduce the convergence time and chattering. For the purpose of addressing the problem that the traditional visual servo control method is sensitive to the environment, a visual servo controller based on integral sliding mode is proposed, to ensure the favorable positioning accuracy of the manipulator. Based on the two proposed controllers mentioned above, a coordinated control strategy is used to implement the control of the manipulator. Finally, the upper computer software is developed using the C# programming language to monitor the workstation. The feasibility of the above-mentioned method is verified through multiple simulations and experiments.

Automatic modeling algorithm of stochastic error for inertial sensors

This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error. The proposed approach is based on a recently developed method called the generalized method of wavelet moments (GMWM), whose estimator was proven to be consistent and asymptotically normally distributed. This algorithm is suitable mainly (but not only) for the combination of several stochastic processes, where the model identification and parameter estimation are quite difficult for the traditional methods, such as the Allan variance and the power spectral density analysis. This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination. The best model is selected by making the trade-off between the model accuracy and the model complexity. The validation of this approach is verified by practical examples of model selection for MEMS-IMUs (micro-electro-mechanical system inertial measurement units) in varying dynamic conditions.

A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems

We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example.

Dynamical pattern recognition for univariate time series and its application to an axial compressor

In this paper, a learning and recognition approach is proposed for univariate time series composed of output measurements of general nonlinear dynamical systems. Firstly, a class of dynamical systems in the canonical form is derived to describe the univariate time series by introducing coordinate transformation. An observer-based deterministic learning technique is then adopted to achieve dynamical modeling of the associated transformed systems of the training univariate time series, and the modeling results in the form of radial basis function network (RBFN) models are stored in a pattern library. Subsequently, multiple observer-based dynamical estimators containing the RBFN models in the pattern library are constructed for a test univariate time series, and a recognition decision scheme is proposed by the derived recognition indicator. On this basis, more concise recognition conditions are provided, which is beneficial for verifying the recognition results. Finally, simulation studies on the Rossler system and aero-engine stall warning verify the effectiveness of the proposed approach.

Global output feedback control of feedforward nonlinear time-delay systems with unknown growth rate and unknown measurement sensitivity

This paper discusses the problem of global state regulation via output feedback for a class of feedforward nonlinear time-delay systems with unknown measurement sensitivity. Different from previous works, the nonlinear terms are dominated by upper triangular linear unmeasured (delayed) states multiplied by unknown growth rate. The unknown growth rate is composed of an unknown constant, a power function of output, and an input function. Furthermore, due to the measurement uncertainty of the system output, it is more difficult to solve this problem. It is proved that the presented output feedback controller can globally regulate all states of the nonlinear systems using the dynamic gain scaling technique and choosing the appropriate Lyapunov–Krasovskii functionals.

Online interactive identification method based on ESO disturbance estimation for motion model of double propeller propulsion unmanned surface vehicle

In this paper, the online parameter identification problem of the mathematical model of an unmanned surface vehicle (USV) considering the characteristics of the actuator is studied. A data-driven mathematical model of motion is very meaningful to realize trajectory prediction and adaptive motion control of the USV. An interactive identification algorithm (ESO–MILS, extended state observer–multi-innovation least squares) based on ESO is proposed. The robustness of online identification is improved by expanding the state observer to estimate the current disturbance without making artificial assumptions. Specifically, the three-degree-of-freedom dynamic equation of the double propeller propulsion USV is constructed. A linear model for online identification is derived by parameterization. Based on the least square criterion function, it is proved that the interactive identification method with disturbance estimation can improve the identification accuracy from the perspective of mathematical expectation. The extended state observer is designed to estimate the unknown disturbance in the model. The online interactive update improves the disturbance immunity of the identification algorithm. Finally, the effectiveness of the interactive identification algorithm is verified by simulation experiment and real ship experiment.

Enhanced adaptive nonlinear extended state observer for pure feedback systems with matched and mismatched disturbances

In this paper, an enhanced adaptive nonlinear extended state observer (EANESO) for single-input single-output pure feedback systems in the presence of external time-varying disturbances is proposed. In this paper, a nonlinear system with matched and mismatched disturbances is considered. The conventional extended state observer (ESO) can only be applied to systems that are in the form of integral chains. Moreover, this method has limitations in the face of mismatched disturbances. In the presence of time-varying disturbances, the traditional ESOs cannot estimate the disturbances accurately. To overcome this limitation, an EANESO is proposed in this paper. The main idea is to design the nonlinear ESO (NESO) to estimate the states of the system and multiple disturbances simultaneously. The observer gains are considered time-varying and adjusted with adaptation laws to improve the estimation accuracy and overcome the peaking phenomenon. Next, the proposed controller is designed based on output feedback to eliminate the effects of multiple disturbances and stabilize the closed-loop system. Subsequently, the stability analysis of the closed-loop system and convergence of the observer error are discussed. Finally, the proposed method is applied to the inverted pendulum system. The simulated results show good performance of the proposed method as compared with a recently published scheme in the related literature.

Unknown system dynamics estimator-based impedance control for lower limb exoskeleton with enhanced performance

In this article, an unknown system dynamics estimator-based impedance control method is proposed for the lower limb exoskeleton to stimulate the tracking flexibility with the terminal target position when suffering parametric inaccuracies and unexpected disturbances. To reinforce the robust performance, via constructing the filtering operation-based dynamic relation, i.e., invariant manifold, the unknown system dynamics estimators are employed to maintain the accurate perturbation identification in both the hip and knee subsystem. Besides, a funnel control technique is designed to govern the convergence process within a minor overshoot and a higher steady-state precision. Meanwhile, an interactive complaint result can be obtained with the aid of the impedance control, where the prescribed terminal trajectory can be adjusted into the interaction variable-based target position by the force–position mapping, revealing the dynamic influence between the impedance coefficient (stiffness and damping) and the adjusted position magnitude. A sufficient stability analysis verifies the ultimately uniformly bounded results of all the error signals, and even the angle errors can be regulated within the predefined funnel boundary in the whole convergence. Finally, some simulations are provided to demonstrate the validity and superiority including the enhanced interaction flexibility and robustness.

Mathematical modelling of ball and plate system with experimental and correlation function-based model validation

The ball and plate system is an inherently nonlinear under actuated benchmark system used for validating the performance of various control schemes. A mathematical model depicting the dynamics close to that of the system is very much required for such a test bed. In this correspondence, the complete nonlinear model, a simplified nonlinear model, and a linearized model of the ball and plate system are developed. The system comprises a ball and plate mechanism and a rotary servo unit. The ball and plate mechanism is modelled using the Euler–Lagrange method, whereas the rotary servo subsystem is modelled from the first principles. The nonlinear model of the combined system is developed by including the dynamics of the servo motor with gear and rolling resistance between the ball and the plate. The simplified nonlinear model of the system is obtained with suitable assumptions. The model is linearized around the operating point using the Jacobian. The validity of the developed models is investigated through correlation function analysis. The open-loop response of the three models, viz., nonlinear, simplified nonlinear, and linearized models, is analyzed in the MATLAB/Simulink platform. Since the open-loop system is unstable, the experimental validation of the model is performed with a double-loop PSO (particle swarm optimization) PID control scheme.

Secure state estimation for cyber-physical systems with unknown input sliding mode observer

In recent years, cyber attacks have posed great challenges to the development of cyber-physical systems. It is of great significance to study secure state estimation methods to ensure the safe and stable operation of the system. This paper proposes a secure state estimation for multi-input and multi-output continuous-time linear cyber-physical systems with sparse actuator and sensor attacks. First, for sparse sensor attacks, we propose an adaptive switching mechanism to mitigate the impact of sparse sensor attacks by filtering out their attack modes. Second, an unknown input sliding mode observer is designed to not only observe the system states, sensor attack signals, and measurement noise present in the system but also counteract the effects of sparse actuator attacks through an unknown input matrix. Finally, for the design of an unknown input sliding mode state observer, the feasibility of the observing system is demonstrated by means of Lyapunov functions. Additionally, simulation experiments are conducted to show the effectiveness of this method.

Random gradient-free method for online distributed optimization with strongly pseudoconvex cost functions

This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.

Application of system identification in a practical active noise control system

Noise pollution has become increasingly severe around the world due to fast urbanization. How to soundproof windows from outside noise is of significant interest for both academia and industry. This paper reports an experimental implementation of normalized minmax active noise control (ANC) algorithm on an open window system, where identifying the model of acoustic sound paths plays a central role. By doing this, traffic noise is attenuated by the ANC system, leading to a relatively quiet indoor environment, while the natural lighting and ventilation functions of a window are remained. Our experiments show that an average of 19 dB(A) noise reduction is achieved.

The posterior selection method for hyperparameters in regularized least squares method

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

Event-triggered H_(∞) consensus control for input-constrained multi-agent systems via reinforcement learning

This article presents an event-triggered H_(∞) consensus control scheme using reinforcement learning (RL) for nonlinear second-order multi-agent systems (MASs) with control constraints. First, considering control constraints, the constrained H_(∞) consensus problem is transformed into a multi-player zero-sum game with non-quadratic performance functions. Then, an event-triggered control method is presented to conserve communication resources and a new triggering condition is developed for each agent to make the triggering threshold independent of the disturbance attenuation level. To derive the optimal controller that can minimize the cost function in the case of worst disturbance, a constrained Hamilton–Jacobi–Bellman (HJB) equation is defined. Since it is difficult to solve analytically due to its strongly non-linearity, reinforcement learning (RL) is implemented to obtain the optimal controller. In specific, the optimal performance function and the worst-case disturbance are approximated by a time-triggered critic network;meanwhile, the optimal controller is approximated by event-triggered actor network. After that, Lyapunov analysis is utilized to prove the uniformly ultimately bounded (UUB) stability of the system and that the network weight errors are UUB. Finally, a simulation example is utilized to demonstrate the effectiveness of the control strategy provided.

Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters

A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori infor-mation of the system is proposed to model the DC-DC converters.A parsimonious model with fewer parameters is used to represent the system,and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data.Compared with traditional data-driven methods for DC-DC convert-ers,the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity.Moreover,compared with the traditional full-parameter constrained subspace approach,the proposed algorithm can accurately estimate the system parameters with a smaller variance.The experimental results on a DC-DC synchronous buck converter verify the effectiveness and superiority of the proposed method.

Oxygen uptake response to switching stairs exercise by non-parametric modeling

Oxygen uptake plays a crucial role in the evaluation of endurance performance during exercise and is extensively utilized for metabolic assessment. This study records the oxygen uptake during the exercise phase (i.e., ascending or descending) of the stair exercise, utilizing an experimental dataset that includes ten participants and covers various exercise periods. Based on the designed experiment protocol, a non-parametric modeling method with kernel-based regularization is generally applied to estimate the oxygen uptake changes during the switching stairs exercise, which closely resembles daily life activities. The modeling results indicate the effectiveness of the non-parametric modeling approach when compared to fixed-order models in terms of accuracy, stability, and compatibility. The influence of exercise duration on estimated fitness reveals that the model of the phase-oxygen uptake system is not time-invariant related to respiratory metabolism regulation and muscle fatigue. Consequently, it allows us to study the humans’ conversion mechanism at different metabolic rates and facilitates the standardization and development of exercise prescriptions.

Observer design and interval estimation of time-delay discrete-time linear systems with external disturbance and measurement noise

For a class of time-delay discrete-time linear systems with external disturbance and measurement noise, the interval estimation problems of state and measurement noise are investigated in this paper. First, the system state together with the time-delay term and measurement noise is augmented as a new state, and a singular system is then constructed. Subsequently, a kind of decoupling technique is employed to eliminate the effect of external disturbance, and an observer is designed to simultaneously estimate the system state and measurement noise. Based on the estimated state and measurement noise, the interval estimations of system state and measurement noise are obtained by reachability analysis technique. Finally, the effectiveness of the proposed method is verified by a four-tank liquid level system.

Periodic event-triggered secure consensus for networked mechanical systems under Denial-of-Service attacks

This paper concentrates on the secure consensus problem of networked mechanical/Euler–Lagrange systems.First,a new periodic event-triggered(PET)secure distributed observer is proposed to estimate the leader information.The proposed distributed observer only relies on the PET data from its neighbors,which can significantly reduce the communication and computational burden.More importantly,it is secure in the sense that it can work normally regardless of the Denial-of-Service(DoS)attacks.Second,based on the proposed distributed observer,an adaptive fuzzy control law is proposed for each Euler–Lagrange system.A PET mechanism is integrated into the controller,which can reduce the control update.This is helpful for both energy saving and fault tolerance of actuators.Moreover,the PET mechanism naturally makes the controller easy to be implemented in digital platform.The property of fuzzy logic systems and Gronwall inequality are skillfully utilized to show the stability of the closed-loop system.Finally,the proposed control scheme is verified on real Euler–Lagrange systems,which contain a robot manipulator and several servo motors.

Identification of FIR systems under difference-driven scheduled quantized observations

In networked system identification,how to effectively use communication resources and improve convergence speed is the focus of attention.However,there is an inherent contradiction between the two tasks.In this paper,the event-driven communication is used to save communication resources for the identification of finite impulse response systems,and the input design is carried out to meet the requirements of convergence speed.First,a difference-driven communication is proposed.Then,the performance of the communication mechanism is analyzed,and the calculation method of its communication rate is given.After that,according to the communication rate and the convergence rate of the identification algorithm,the input design problem is transformed into a constrained optimization problem,and the algorithm for finding the optimal solution is given.In addition,considering the case that the output is quantized by multiple thresholds,the way to calculate its communication rate is given and the influence of threshold number on communication rate is discussed.Finally,the effectiveness of the algorithm is verified by simulation.

Identification of the Hammerstein nonlinear system with noisy output measurements

In this research, we present a methodology to identify the Hammerstein nonlinear system with noisy output measurements. The Hammerstein system presented is comprised of neural fuzzy model (NFM) as its static nonlinear block and auto-regressive with extra input (ARX) model as its dynamic linear block, and a two-step procedure is accomplished using signal combination. In the first step, in the case of input–output of Gaussian signals, the correlation function-based least squares (CF-LS) technique is utilized to identify the linear block, solving the problem that the intermediate variable connecting nonlinear and linear blocks cannot be measured. In the second step, to improve the identification accuracy of the nonlinear block parameters, an improved particle swarm optimization technique is developed under input–output of random signals. The validity and accuracy of the presented scheme are verified by a numerical simulation and a practical nonlinear process, and the results illustrate that the proposed methodology can identify well the Hammerstein nonlinear system with noisy output measurements.