System Identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification

System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.

Continuous-time System Identification with Nuclear Norm Minimization and GPMF-based Subspace Method

To improve the accuracy and effectiveness of continuous-time(CT) system identification, this paper introduces a novel method that incorporates the nuclear norm minimization(NNM) with the generalized Poisson moment functional(GPMF)based subspace method. The GPMF algorithm provides a simple linear mapping for subspace identification without the timederivatives of the input and output measurements to avoid amplification of measurement noise, and the NNM is a heuristic convex relaxation of the rank minimization. The Hankel matrix with minimized nuclear norm is used to determine the model order and to avoid the over-parameterization in subspace identification method(SIM). Furthermore, the algorithm to solve the NNM problem in CT case is also deduced with alternating direction methods of multipliers(ADMM). Lastly, two numerical examples are presented to evaluate the performance of the proposed method and to show the advantages of the proposed method over the existing methods.

Hydraulic turbine system identification and predictive control based on GASA-BPNN

Based on the characteristics of nonlinearity,multi-case,and multi-disturbance,it is difficult to establish an accurate parameter mod-el on the hydraulic turbine system which is limited by the degree of fitting between parametric model and actual model,and the design of con-trol algorithm has a certain degree of limitation.Aiming at the modeling and control problems of hydraulic turbine system,this paper proposes hydraulic turbine system identification and predictive control based on genetic algorithm-simulate anneal and back propagation neural network(GASA-BPNN),and the output value predicted by GASA-BPNN model is fed back to the nonlinear optimizer to output the control quantity.The results show that the output speed of the traditional control system increases greatly and the speed of regulation is slow,while the speed of GASA-BPNN predictive control system increases little and the regulation speed is obviously faster than that of the traditional control system.Compared with the output response of the traditional control of the hydraulic turbine governing system,the neural network predictive control-ler used in this paper has better effect and stronger robustness,solves the problem of poor generalization ability and identification accuracy of the turbine system under variable conditions,and achieves better control effect.

Nonlinear System Identification with Unknown Piecewise Time-Varying Delay

Identification of nonlinear systems with unknown piecewise time-varying delay is concerned in this paper.Multiple auto regressive exogenous(ARX) models are identified at different process operating points,and the complete dynamics of the nonlinear system is represented by using a combination of a normalized exponential function as the probability density function with each of the local models.The parameters of the local ARX models and the exponential functions as well as the unknown piecewise time-varying delays are estimated simultaneously under the framework of the expectation maximization(EM) algorithm.A simulation example is applied to demonstrating the proposed identification method.

System Identification of Heritage Structures Through AVT and OMA:A Review

In this review article,the past investigations carried out on heritage structures using Ambient Vibration Test(AVT)and Operational Modal Analysis(OMA)for system identification(determination of dynamic properties like frequency,mode shape and damping ratios)and associated applications are summarized.A total of 68 major research studies on heritage structures around the world that are available in literature are surveyed for this purpose.At first,field investigations carried out on heritage structures prior to conducting AVT are explained in detail.Next,specifications of accelerometers,location of accelerometers and optimization of accelerometer networks have been elaborated with respect to the geometry of the heritage structures.In addition to this,ambient vibration loads and data acquisition procedures are also discussed.Further,the state of art of performing OMA techniques for heritage structures is explained briefly.Furthermore,various applications of system identification for heritage structures are documented.Finally,conclusions are made towards errorless system identification of heritage structures through AVT and OMA.

Heuristic Order Reduction of NARX-OBF models Applied to Nonlinear System Identification

Nonlinear system identification concerns the determination of the model structure and its parameters.Although the designers often seek the best model for each system,it can be tricky to determine,at the same time,the best structure and the parameters which optimize the model performance.This paper proposes the use of a Genetic Algorithm,GA,and the Levenberg-Marquardt,LM,method to obtain the model parameters,as well as perform the order reduction of the model.In order to validate the proposed methodology,the identification of a magnetic levitator,operating in closed loop,was performed.The class NARX-OBF,Nonlinear Auto Regressive with eXogenous input-Orthonormal Basis Function,was used.The use of OBF functions aims to reduce the number of terms in NARX models.Once the model is found,the order reduction is performed using GA and LM,in a hybrid application,capable of determining the model parameters and reducing the original model order,simultaneously.The results show,considering the inherent trade-of between accuracy and computational effort,the proposed methodology provided an implementation with good mean square error,when compared with the full NARX-OBF model.

Mutation detection and fast identification of switching system based on data-driven method

In the engineering field,switching systems have been extensively studied,where sudden changes of parameter value and structural form have a significant impact on the operational performance of the system.Therefore,it is important to predict the behavior of the switching system,which includes the accurate detection of mutation points and rapid reidentification of the model.However,few efforts have been contributed to accurately locating the mutation points.In this paper,we propose a new measure of mutation detection—the threshold-based switching index by analogy with the Lyapunov exponent.We give the algorithm for selecting the optimal threshold,which greatly reduces the additional data collection and the relative error of mutation detection.In the system identification part,considering the small data amount available and noise in the data,the abrupt sparse Bayesian regression(abrupt-SBR)method is proposed.This method captures the model changes by updating the previously identified model,which requires less data and is more robust to noise than identifying the new model from scratch.With two representative dynamical systems,we illustrate the application and effectiveness of the proposed methods.Our research contributes to the accurate prediction and possible control of switching system behavior.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints

This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.

An integrated approach for machine-learning-based system identification of dynamical systems under control:application towards the model predictive control of a highly nonlinear reactor system

Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:the optimality of attack strategy

With the development of wireless communication technology,cyber physical systems are applied in various fields such as industrial production and infrastructure,where lots of information exchange brings cyber security threats to the systems.From the perspective of system identification with binary-valued observations,we study the optimal attack problem when the system is subject to both denial of service attacks and data tampering attacks.The packet loss rate and the data tampering rate caused by the attack is given,and the estimation error is derived.Then the optimal attack strategy to maximize the identification error with the least energy is described as a min–max optimization problem with constraints.The explicit expression of the optimal attack strategy is obtained.Simulation examples are presented to verify the effectiveness of the main conclusions.

Introducing System Identification Strategy into Model Predictive Control

As system identification theory and model predictive control are belonged to two different research fields separately,so one gap exists between these two subjects.To alleviate this gap between them,one new idea proposed in this paper is to introduce system identification theory into model predictive control.As the most important element in model predictive control is the prediction of the output value for a nonlinear system,then the problem of deriving the prediction of the output value can be achieved by system identification theory.More specifically,a Bayesian approach is applied for the nonparametric estimation by modeling the prediction as realizations of zero mean random fields.Through comparing this kind of prediction corresponding to this Bayesian approach and the former direct weight optimization identification for nonlinear system,the authors see that if the unknown weights are chosen appropriately,these two approaches are equivalent to each other.Based on the obtained prediction of the output value,the authors substitute this prediction of the output value into one cost function of model predictive control,and then a quadratic programming problem with inequality constraints is formulated.When to solve this quadratic programming problem,a detailed process about how to derive its dual form is given.As the dual problem has a simple constraint set,it is amenable to the use of the common Gauss-Seidel algorithm,whose convergence can be shown easily.Finally,one simulation example confirms the proposed theoretical results.

Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes

Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle(AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom(DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:defense scheme and its optimality

In this paper,we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations.By estimating the key parameters of the joint attack and compensating them in the identification algorithm,a compensation-oriented defense scheme is proposed.Then the identification algorithm of system parameter is designed and is further proved to be consistent.The asymptotic normality of the algorithm is obtained,and on this basis,we propose the optimal defense scheme.Furthermore,the implementation of the optimal defense scheme is discussed.Finally,a simulation example is presented to verify the effectiveness of the main results.

Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying.It is impractical to identify a model that can restore the model of real papermaking process.Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process.In this study,a strange nonchaotic particle swarm optimization(SNPSO)algorithm is proposed to identify the models of real papermaking processes,and this identification ability is significantly enhanced compared with particle swarm optimization(PSO).First,random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions.Furthermore,the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO.The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization.To illustrate the effectiveness of SNPSO,step response data are used to identify the models of real industrial processes.Compared with classical PSO,PSO with timevarying acceleration coefficients(PSO-TVAC)and modified particle swarm optimization(MPSO),the simulation results demonstrate that SNPSO has stronger identification ability,faster convergence speed,and better robustness.

One Novel Hydraulic Actuating System for the Lower-Body Exoskeleton

The hydraulic exoskeleton is one research hotspot in the field of robotics,which can take heavy load due to the high power density of the hydraulic system.However,the traditional hydraulic system is normally centralized,inefficient,and bulky during application,which limits its development in the exoskeleton.For improving the robot's performance,its hydraulic actuating system should be optimized further.In this paper a novel hydraulic actuating system(HAS)based on electric-hydrostatic actuator is proposed,which is applied to hip and knee joints.Each HAS integrates an electric servo motor,a high-speed micro pump,a specific tank,and other components into a module.The specific parameters are obtained through relevant simulation according to human motion data and load requirements.The dynamic models of the HAS are built,and validated by the system identification.Experiments of trajectory tracking and human-exoskeleton interaction are carried out,which demonstrate the proposed HAS has the ability to be applied to the exoskeleton.Compared with the previous prototype,the total weight of the HAS in the robot is reduced by about 40%,and the power density is increased by almost 1.6 times.

Gas Leakage Detection and Pressure Difference Identification by Asymmetric Differential Pressure Method

Currently,the measurement methods for pneumatic system leakage include bubbling,ultrasonic,and pressure detection methods.These methods are sensitive to high-precision sensors,long detection times,and stable external environments.The traditional differential pressure method involves severe differential pressure fluctuations caused by environmental pressure fluctuations or electromagnetic noise interference of sensors,leading to inaccurate detection.In this paper,a differential pressure fitting method for an asymmetric differential pressure cylinder is proposed.It overcomes the limitation of the detection efficiency caused by the asynchronous temperature recovery of the two chambers in the asymmetric differential pressure method and uses the differential pressure substitution equation to replace the differential calculation of the differential pressure.The improved differential pressure method proposes an innovation based on the detection principle and calculation method.Additionally,the influence of the parameters in the differential pressure substitution equation on the leakage calculation results was simulated,and the specific physical significance of the parameters of the differential pressure substitution equation was explained.The experiments verified the fitting effect and proved the accuracy of this method.Compared with the traditional differential pressure method,the maximum leakage deviation of inhibition was 0.5 L/min.Therefore,this method can be used to detect leaks in air tanks.

Model analysis and resonance suppression of wide-bandwidth inertial reference system

In the fields of earth observation,deep space detection,laser communication,and directional energyweapon,the target needs to be observed and pointed at accurately.Acquisition,tracking,and pointing(ATP)systems are usually designed to stabilize the line of sight(LOS)within sub-micro radian levels.In the case of an ATP system mounted on a mobile platform,angular disturbances experienced by the mobile platform will seriously affect the LOS.To overcome the problemthat the sampling frequency of detectors is usually limited and achieving several hundreds of hertz is difficult,thewide-bandwidth inertial reference system(WBIRS)and fast steeringmirror are usually integrated into ATP systems to mitigate these angular disturbances.To reduce the structural stress,a flexible support providing two rotational degrees of freedomis usually adopted for the system.However,the occurrence of resonant points within the bandwidthwill be inevitable.Measurements have to be taken to compensate these low-frequency resonant points to realize a wide bandwidth and high precision.In this paper,the lowfrequency resonant points of a systemwere simulated using finite element analysis and tested by a systemidentification method.The results show that the first-order resonance happened at 34.5 Hz with a gain of 28 dB.An improved double-T notch filter was designed and applied in a real-time system to suppress the resonance at 34.5 Hz.The experimental results show that the resonance was significantly suppressed.In particular,the resonance peak was reduced by 79.37%.In addition,the closed-loop system settling time was reduced by 36.2%.

Parameter estimation for dual-rate sampled Hammerstein systems with dead-zone nonlinearity

The identification of nonlinear systems with multiple sampled rates is a difficult task.The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data.Firstly,the auxiliary model identification principle is used to estimate the unmeasurable variables,and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model.Then,the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem.It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation.Finally,the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems.