Input State Variables Research Articles

Transition and bifurcation mechanism of firing activities in memristor synapse-coupled Hindmarsh–Rose bi-neuron model

Memristor, especially the locally active one, is widely used in emulating the synapse-inspired activities in neural networks, in which the connection between memristor properties and neuronal electrical activities requires further investigation. In this paper, a bi-neuron model is constructed by bi-directionally connecting a locally active memristor between the membrane potentials of two deterministic 3D HR neuron models. Complex spiking/bursting firing activities and their transitions are disclosed under different memristor control parameters. The bifurcation mechanisms of the revealed synchronous and asynchronous firing activities are explored inside each individual neuron by taking the externally input state variables as modulation parameters. These results demonstrate the crucial effect of the locally active memristor synapse on firing activities of its coupled network. FPGA-based digital circuit experiment is finally performed to verify the numerical results. This research is beneficial to understanding, control and application of firing activities in neural networks.

Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems.

Optimal FLC-Sugeno Controller based on PSO for an Active Damping System

In this paper, a method for the design of an optimal Sugeno model (FLC-Sugeno) fuzzy logic controller for the active suspension system of quarter-vehicle models is presented. The parameters of the FLC-Sugeno controller are optimally searched, using the Particle Swarm Optimization (PSO) algorithm. The 16 optimized parameters include 3 parameters for adjusting the domain of the input state variables and control variables at the controller’s output, 4 fuzzy set adjustment numbers of the linguistic variables, and 9 parameters as the fuzzy rule weights of the rule system control. To compare and evaluate the effectiveness of the optimal FLC-Sugeno controller, an optimal PID controller using PSO is also implemented. Simulation results of the active damping system with the controllers when affected by the same type and standard road surface excitation show that the FLC-Sugeno controller is optimal for the quick ending of the oscillations of vehicle body displacement. The result shows that the proposed controlling scheme can be extended and applied to more complex active damping system models.

Observer-based fixed-time fuzzy tracking control for stochastic nonstrict nonlinear systems with hysteresis nonlinearity

In this paper, a new adaptive fuzzy fixed-time control strategy is proposed for stochastic nonstrict nonlinear systems with Bouc–Wen type of hysteresis input and unmeasured state variables. The Bouc–Wen model is recognized as one of the most intractable mechanical hysteresis models. The established state observer is used to assess unmeasurable states, and the fuzzy logic systems are utilized to approximate the uncertain nonlinear functions in the system. Based on the state observer and backstepping method, a fixed-time controller is recursively designed to ensure that all closed-loop signals of the system are bounded and the tracking error converges to a small neighborhood of the origin within a fixed-time. Finally, the effectiveness of the control strategy is verified by two simulation examples.

An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

Model Predictive Control of DC–DC Converters to Mitigate the Effects of Pulsed Power Loads in Naval DC Microgrids

This paper proposes the design of optimal and robust coordinated controller of hybrid energy storage system in a naval dc microgrid (MG) application. It is able to mitigate the negative effects of the pulsed power loads and meet the practical limitations of both converters input control and state variables signals based on IEEE standards. To do this, first, the dynamic model of the dc MG, which can represent in either all-electric aircraft or shipboard power systems, is developed. Second, a novel model predictive controller (MPC) for energy storage converters is proposed such that all of the mentioned hard constraints are guaranteed. Third, a linear matrix inequality approach is used to solve the MPC conditions. Finally, to evaluate the applicability and effectiveness of the proposed approach, some experimental tests are extracted. Obtained results verify better performance of the proposed approach over other state-of-the-art control techniques.

A Neuromorphic Computational Primitive for Robust Context-Dependent Decision Making and Context-Dependent Stochastic Computation

The prefrontal cortex (PFC) plays an important role in complex cognitive computations, including planning and decision making. Although recurrent spiking neural network (SNN) software models of PFC have been successful in reproducing many of its cognitive computational aspects, little attention has been devoted to the question of how such systems can perform with low-resolution parameters and be robust to noise and variability in their input signals and state variables. Here, we present a mixed-signal analog/digital neuromorphic implementation of a state-dependent SNN architecture that addresses these issues by construction. The network relies on synaptic dis-inhibition to ensure robust decision making even in the face of very large variability. Depending on its connectivity, the network can either perform robustly in a deterministic way or exploit the device mismatch and noise to explore stochastically multiple states in constraint satisfaction problems (CSPs). We validate the architecture by mapping it onto a network of spiking neurons in a multi-core mixed-signal neuromorphic system and presenting experimental results for three different examples of CSPs.

Modeling the fate of organic nitrogen during anaerobic digestion: Development of a bioaccessibility based ADM1

Simulating the fate of nitrogen during anaerobic digestion is required to predict the characteristics of digestates and to improve their exploitation for agricultural uses. The aim of this study was to develop a modified ADM1 model that includes bioaccessibility-based fractionation to accurately simulate the fate of nitrogen during anaerobic digestion. To this end, two complementary approaches were used: (i) changes in the bioaccessibility of protein and non-protein compounds were assessed on eight substrates during anaerobic digestion in batch experiments using the "EPS" fractionation method; (ii) experimental results were used to develop a bio-kinetic model based on anaerobic digestion model n°1. This new model incorporates bioaccessibility-based fractionation in its input state variables. The model was successfully calibrated and model evaluation showed that predicted methane production, ammonium production and changes in protein and non-protein bioaccessibility during anaerobic digestion were accurate.

Unknown-input observer for vehicle lateral dynamics using new sensor force technology

Robust estimation of important states for vehicles (steering angle and sideslip angle) is essential for vehicle handling and stability control. In the past, the estimation of vehicle lateral state with the measured steering angle has been proposed. In this paper, four unknown input observers are designed using lateral tyre force measurements based on a nonlinear vehicle model, to simultaneously estimate vehicle steering angle as unknown input and vehicle lateral state variables sideslip angle and yaw rate. All of the design conditions are formulated in terms of linear matrix inequalities (LMI) which can be solved efficiently. Simulation results through MATLAB/Simulink software based on data of the CALLAS vehicle simulator are used to evaluate the performance of observers based on nonlinear unknown input observer (ONEI).

Estimation of Steering Wheel Angle and Vehicle Lateral State from Measured Lateral Forces

This paper introduces a method to estimate the lateral dynamics parameters,which are valuable to the development of more complex and powerful driver assistance system. In the assumption of measured lateral forces, three state observer methods are designed to simultaneously estimate the steering angleas unknown input and vehicle lateral state variables. The stability conditionsof such observers are derived in terms of Linear Matrix Inequalities (LMI). Simulation results through Matlab/Simulink software based on data of the CALLAS vehicle simulator is used to evaluate the performance of observersbased on Unknown Input Observer (OEI).

H∞ model predictive control for constrained discrete‐time piecewise affine systems

SummaryThis paper investigates stability analysis for piecewise affine (PWA) systems and specifically contributes a new robust model predictive control strategy for PWA systems in the presence of constraints on the states and inputs and with l2 or norm‐bounded disturbances. The proposed controller is based on piecewise quadratic Lyapunov functions. The problem of minimization of the cost function for model predictive control design is changed to minimization of the worst case of the cost function. Then, this objective is reduced to minimization of a supremum of the cost function subject to a terminal inequality by considering the induced l2‐norm. Finally, the predictive controller design problem is turned into a linear matrix inequality feasibility exercise with constraints on the input signal and state variables. It is shown that the closed‐loop system is asymptotically stable with guaranteed robust performance. The validity of the proposed method is verified through 3 well‐known examples of PWA systems. Simulation results are provided to show good convergence properties along with capability of the proposed controller to reject disturbances.

Multi-objective optimization of a reactive batch distillation process using reduced order model

Reactive batch distillation (RBD) processes are highly nonlinear in nature and use of first principles model into the optimization problem demands high computational time. Therefore, a reduced order model for RBD process is proposed which is based on selection of states of prime importance, identification of input state variables, definition of reference trajectories for nominal case, and definition of additional variable transformation to facilitate adaptation of the model to variations in operating conditions. Further, the work focuses on development of a multi-objective optimization formulation for arriving at an optimal operation policy for RBD, and a modified version of Non-dominated sorting Genetic algorithm (NSGA-II) is developed for its solution. The proposed approach is evaluated by application to a simulation case study for production of butyl acetate. The optimization results obtained using the reduced order model are comparable to those obtained with first principles model, while requiring only 1/20th of the time.

Traffic system operation optimization incorporating buffer size

This paper presents a general framework for resolving resource utilizations conflicts of air traffic system, expressed in the form of a max-plus linear model. The general air traffic system optimization model is presented taking into account buffer size in view of different purposes. The proposed air traffic system model is very flexible and it is extensible focusing on distinct circumstances. The dynamics of air traffic systems is characterized through the occurrence of discrete events such as aircrafts entering or leaving sub-segments. We focus on input control of air traffic system modeled by max-plus algebra by controlling the inflow of aircraft into the system. The constraints between input variables, state variables and output variables were obtained. The proposed method aims to minimize system total delay to meet demand specifications, allowing the air traffic controller to obtain the best control policy, which delays the occurrence of input events or varying input rate of system resources. Through simulations we verify the performance of the proposed max-plus linear model control scheme.

Conflict-free trajectory planning based on the model predictive control theory

Model Predictive Control (MPC) is a model-based control method based on a receding horizon approach and online optimization. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. This paper proposes a max-plus general modeling framework adapted to the robust optimal control of air traffic flow in the airspace. It is shown that the problem can be posed as the control of queues with safety separation-dependent service rate. We extend MPC to a class of discrete-event system that can be described by models that are linear in the max-plus algebra with noise or modeling errors. Regarding the single aircraft as a batch, the relationships between input variables, state variables and output variable are established. We discuss some key properties of the system model and indicate how these properties can be used to analyze the behavior of air traffic flow. The model predictive control design problems are defined for this type of discrete event system to help prepare the airspace for various robust regulation needs and we give some extensions of the air traffic max-plus linear systems.

Strategic De-confliction Trajectory Planning Using Max-plus Approach

This paper proposes a max-plus general modeling framework adapted to the optimal control of air traffic flows in the airspace. It is shown that the problems of regulating aircraft operation in the airspace can be posed as the control of queues with safety separation-dependent service rate. Regarding the single aircraft as a batch, the relationships between input variables, state variables and output variable are established based on jet routes conflict point competition mechanism and space spatial configuration of jet routes. Furthermore, from the spatial logic configuration of sub-models, the whole airspace multi-aircraft max-algebra coupled models and conflict pre-deployment models are also presented. This paper uses the proposed framework to develop optimization cost functions that help prepare the airspace for various regulation needs. In particular, the model includes the management of airport or waypoint arrivals and departures subject to internal restraint mechanism which are very useful for 4D-trajectory based aircraft operation.

Influent Fractionation for Modeling Continuous Anaerobic Digestion Processes.

The first dynamic model developed to describe anaerobic digestion processes dates back to 1969. Since then, considerable improvements in identifying the underlying biochemical processes and associated microorganisms have been achieved. These have led to an increasing complexity of both model structure and the standard set of stoichiometric and kinetic parameters. Literature has always paid attention to kinetic parameter estimation, as this determines model accuracy with respect to predicting the dynamic behavior of biogas systems. As sufficient computing power is easily available nowadays, sophisticated linear and nonlinear parameter estimation techniques are applied to evaluate parameter uncertainty. However, the uncertainty of influent fractionation in these parameter optimization procedures is generally neglected. As anaerobic digestion systems are currently increasingly used to convert a broad variety of organic biomass to methane, the lack of generally accepted guidelines for input characterization adapted to the simulation model's characteristics is a considerable limitation of model application to these substrates. Directly after the introduction of the standardized Anaerobic Digestion Model No. 1 (ADM1), several publications pointed out that the model's requirement of a detailed influent characterization can hardly be fulfilled. The main shortcoming of the model application was addressed in the reliable and practical identification of the model's input state variables for particulate and soluble carbohydrates, proteins and lipids, as well as for the inerts. Several authors derived biomass characterization procedures, most of them dedicated to a particular substrate, and some of them being of general nature, but none of these approaches have resulted in a practical standard protocol so far. This review provides an overview of existing approaches that improve substrate influent characterization to be used for state of the art anaerobic digestion models.

Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

With recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. Spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques have resulted in more favourable acceptance by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydro-informatics applications ranging from data mining for pattern discovery to data driven models and numerical model error correction. The present study explores the feasibility of applying mutual information theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e. assuming that the hydrodynamic model, available at this point, is best representation of the physics in the domain of interest) by relating them to variables that reflect the state at which the predictions are made such as input data, state variables and model output. In addition, the present study explores the possibility of employing ‘genetic programming’ (GP) as an offline data driven modelling tool to capture the sea level anomaly (SLA) dynamics and then using them for updating the numerical model prediction in real time applications. These results suggest that combination of data relationship analysis and GP models helps to improve the forecasting ability by providing information of significant predicative parameters. It is found that GP based SLA prediction error forecast model can provide significant improvement when applied as data assimilation schemes for updating the SLA prediction obtained from primary hydrodynamic models.

Smith fuzzy control of teleoperation rendezvous with variable time delay

The control methods of ground-based teleoperation rendezvous with variable time delay are surveyed. With the help of root locus method, the influences of time delay on the stability and performance of the control system are analyzed. Then, a time-delay compensator is designed to transfer the variable delay into a constant. A predictive model is established based on the relative dynamic equations. After that, a multivariable Smith predictor is designed based on the principle of Smith predictor, which makes the control input state variables independent of variable time delay. At the same time, the fuzzy control method is introduced to alleviate the uncertainties in the teleoperation rendezvous system. Finally, semi-physical simulations are carried out to verify the methods presented in this paper. Simulation results show that time-delay compensation and Smith-Fuzzy control are effective in alleviating the variable time delay and model uncertainties in the process of teleoperation rendezvous, and the success probability and control accuracy can be improved.

Iterated time series prediction with multiple support vector regression models

Support vector regression (SVR) model has been widely applied to time series prediction. In general iterative methods, the multi-step-ahead prediction is based on the iteration of the exact one-step prediction. Even if the one-step prediction model is very exact, the iteration procedure would accumulate prediction errors when repeating one-step-ahead prediction, which results in bad prediction performance. This paper deals with iterated time series prediction problem by using multiple SVR models, which are trained independently based on the same training data with different targets. In other words, the n-th SVR model performs an n-step-ahead prediction. The prediction outputs of these models are considered as the next input state variables to perform further prediction. Since each SVR model is an exact prediction model, the accumulate prediction error would be reduced by using multiple SVR models possibly. Actually, the MSVR method can be taken as a compromise of the iterative method and the direct method. Experimental results on time series data show that the multiple SVR model method is effective.

A waste characterisation procedure for ADM1 implementation based on degradation kinetics

In this study, a procedure accounting for degradation kinetics was developed to split the total COD of a substrate into each input state variable required for Anaerobic Digestion Model n°1. The procedure is based on the combination of batch experimental degradation tests (“anaerobic respirometry”) and numerical interpretation of the results obtained (optimisation of the ADM1 input state variable set). The effects of the main operating parameters, such as the substrate to inoculum ratio in batch experiments and the origin of the inoculum, were investigated. Combined with biochemical fractionation of the total COD of substrates, this method enabled determination of an ADM1-consistent input state variable set for each substrate with affordable identifiability. The substrate to inoculum ratio in the batch experiments and the origin of the inoculum influenced input state variables. However, based on results modelled for a CSTR fed with the substrate concerned, these effects were not significant. Indeed, if the optimal ranges of these operational parameters are respected, uncertainty in COD fractionation is mainly limited to temporal variability of the properties of the substrates. As the method is based on kinetics and is easy to implement for a wide range of substrates, it is a very promising way to numerically predict the effect of design parameters on the efficiency of an anaerobic CSTR. This method thus promotes the use of modelling for the design and optimisation of anaerobic processes.