Search

We consider the problem of output regulation for the class of minimum-phase nonlinear systems described in normal form. We assume that the ideal steady state control input fulfills a nonlinear regression law that is linearly parametrized in the uncertain parameters and we propose an internal model-based design that combines high-gain and identification tools. The identification tool by which the internal model is updated, is discrete-time by thus obtaining a hybrid internal model. The present paper is part of a wider research activity of the authors in which the attempt is to combine high-gain tools typically used in the context of nonlinear output regulation with identification tools that are here used to estimate the optimal regression law by best fitting the friend and its time derivatives.

A nonlinear model predictive control scheme guaranteeing robust constraint satisfaction is presented. The scheme is applicable to polynomial or rational systems and guarantees that state, terminal, and output constraints are robustly satisfied despite uncertain and bounded disturbances, parameters, and state measurements or estimates. In addition, for a suitably chosen terminal set, feasibility of the underlying optimization problem at a time instance guarantees that the constraints are robustly satisfied for all future time instances. The proposed scheme utilizes a semi-infinite optimization problem reformulated as a bilevel optimization problem: The outer program determines an input minimizing a performance index for a nominal nonlinear system, while several inner programs certify robust constraint satisfaction. We use convex relaxations to deal with the nonlinear dynamics in the inner programs efficiently. A simulation example is presented to demonstrate the approach.

Nonlinear PDEs domain discretization yields finite but high dimensional nonlinear systems. Proper Orthogonal Decomposition (POD) is widely used to reduce the order of such systems but it assumes that data belongs to a linear space and therefore fails to capture the nonlinear degrees of freedom. To overcome this problem, we develop a Space Vector Clustering (SVC) POD and use the reduced order model to design the controller which will then be applied to the full order system. A space vector is the solution at a particular space location over all times. The solution space is grouped into clusters where the behavior has significantly different features, then local POD modes will be constructed based on these clusters. We apply our method to reduce and control a nonlinear convective PDE system governed by the Burgers' equation over 1D and 2D domains and show a significant improvement over global POD. We also design a reference tracking controller and compare the controlled systems. We show that the controller based on our SVC local POD reduced system yields more accurate tracking results over the one based on global POD.

Advanced controllers such as model predictive control are in use for a wide range of application in the process industry. The potential utilization of such advanced predictive controllers is far from exhausted. One barrier for more widespread implementation is the lack of simple methodologies for advanced control design development which may be used by non experts in control theory. This paper presents and illustrates the use of a simple methodology to design an offset-free MPC based on ARX models. Hence a mechanistic process model is not required. The forced circulation evaporator by Newell and Lee is used to illustrate the offset-free MPC based on ARX models for a nonlinear multivariate process.

It is well known that in most bilateral teleoperation systems, variable time-delays arise in the communications. Motivated by this fact, the present work reports the extension to the (asymmetric) variable time-delays case of the adaptive controller for bilateral teleoperators with uncertain parameters and constant time-delays, reported in Nuno et al (2010) [Nuno, E., Ortega, R. and Basanez, L. (2010). An adaptive controller for nonlinear teleoperators. Automatica, 46(1), 155–159]. Compared to the previous work, the stability analysis does not rely on the cascade interconnection structure of the local and remote nonlinear dynamics and the linear interconnection map. Instead, the paper employs a different Lyapunov candidate function that incorporates a strictly positive term, the local and remote position error. With the only assumption that a bound of the time-delays is known, the paper also presents a sufficient condition that ensures the asymptotic convergence of position errors and velocities to zero. Some simulations, in free space and interacting with a stiff wall, illustrate the performance of the proposed control scheme in the presence of variable time–delays.

In this paper, a high dynamic test-bed control problem for high dynamic vehicle simulation is studied in order to improve the tracking of speed and torque. As the system is nonlinear and square (2x2) with two inputs and two outputs, a multi-input multi-output (MIMO) system-identification problem is discussed. The test-bed data are processed using a fast frequency domain methodology to build a nominal linear plant and a perturbation model. Based on this perturbed plant, the CRONE Control-System Design approach (a fractional order of differentiation based methodology) is used to design a robust MIMO controller, which maintains performance around a wide set of operating points. Finally, experimental results show the relevance of the proposed methodology.

This work presents a strategy to estimate and to correct dynamics variations in nonlinear time variant systems. This correction is carried out by estimating the internal parameters of the process and determining the differences with an available nonlinear model of the system. The proposed approach has a double functionality; on the one hand, it allows a better performance of using nonlinear models for control purposes, like for nonlinear predictive controllers; and on the other hand, it can be used as a diagnosis mechanism since it provides relevant information about the current state of the system. Thus, in order to use this technique with nonlinear time variant systems, a nonlinear model predictive control strategy has been used. The estimator proposed within the framework of this work is similar to the Moving Horizon Estimation strategy. Experimental results on a real tank process are presented to show the main properties of the proposed architecture.

This paper considers the problem of reconstructing, simultaneously occurring actuator and sensor faults in a nonlinear system. The theory which will be developed in this paper is based on a sliding mode observer formulation, designed around an LPV model approximation of the nonlinear system. It extends previous work which has independently considered actuator and sensor faults reconstruction. Here a single observer structure will be synthesized which can cope with simultaneous actuator and sensor faults. The paper will describe the development of the LPV model, the theory and synthesis of the sliding mode observer, and the results of applying this technique to the RECONFIGURE benchmark problem.

Biological oscillation is generated by different genetic circuit structures, such as the smooth oscillator and the relaxation oscillator. Studies on the complex interactions among genes and proteins are essential for understanding the generating mechanism underlying the circadian clock or other biological oscillators. In this paper, the biological oscillation is investigated from a new perspective. It is studied here, through principles in the nonlinear system and control theory, the important factors for the existence of oscillation in the smooth oscillation system. Conclusions have been drawn that both the degradation rates of genes and proteins and the switch-like behavior of the translation process are essential for generating oscillation. The prediction in the Goodwin oscillators model, that the degradations of the clock mRNA and clock protein play important roles in regulating the oscillation period, is discussed here through classic control theory, i.e., the relay control tuning method.

After the enlargement of the European ENTSO-E power system towards Turkey at the end of 2010, the East-West Inter-Area Oscillation mode in the enlarged the European ENTSO-E power system has been identified in the frequency range of 0.15 Hz (T P = 7s) accompanied by insufficient damping. By the end of 2012, more than 107 GW of wind generation capacity had been installed across Europe, representing about 25% of the peak demand of ENTSO-E power system. In this paper, the impact of large scale wind power generation in the European ENTSO-E system on the North-South Inter-Area Oscillation mode using a detailed dynamic model of the European ENTSO-E system is investigated by gradually replacing the power generated by the synchronous generators in the system either Full Size Converter or Doubly Fed Induction Generator (DFIG) wind turbines. Because the whole system is extremely nonlinear, the analysis method in state space is senseless; therefore the damping behavior of Inter-Area-Oscillations of the whole system was analyzed in detail using the analysis method in time domain. The model was created using DIgSILENT software.

Rao-Blackwellied particle filter is a stochastic filter combining Kalman filters with particle filters. It is suitable for models that could be decomposed into linear and nonlinear part. Since the conditionally linear part can be solved by the Kalman filter, the sequential Monte Carlo is run only on the non-linear subspace. The resulting algorithm is a parallel evaluation of multiple Kalman filters with resampling. The parallel nature of this algorithm allows for very efficient implementation in hardware supporting parallel computation processes. In this contribution, we present implementation of the algorithm in the Field Programmable Gate Array (FPGA). Due to the used model and optimized implementation, the execution time of the filter is in units of microseconds and scales very favorably with the number of particles. This is demonstrated experimentally on a laboratory prototype of sensorless drive with permanent magnet synchronous machine (PMSM) of rated power of 10.7kW.

The system under scrutiny in this paper is a thermal power plant in Savannah Sugar Company Numan (SSCN) factory, Nigeria. The final power output of the plant is affected by random events such as equipment failures. Whenever such random events occur, the power plant's output is unstable due to the electrical power demand by the factory instruments and estate. Unavailability of plant means shorter production periods and hence profit loss, these effects should be minimized, which is not a trivial matter because the plant is a highly complex system. This paper presents the principal dynamic phenomena that determine the model of boiler-turbine-generator system. The formation of the model is based on fundamental physical and thermodynamic laws. The nonlinear nature of the model is made up of differentials and algebraic equations, steam tables and the use of algebraic polynomial formulae provided the means of obtaining required data for the modelling. Raw data was taken from this power plant for period of two years. The derived model is realized in the MATLAB/SIMULINK 7.10 environment using the SSCN power plant raw input data. Validation result shows that the plant's outputs (Steam Pressure and Electrical Power) are within acceptable range of the manufacturer's recommended values. This model can be said to be the true representation of SSCN power plant.

A method to ensure recursive feasibility and asymptotic stability for a distributed model predictive control scheme in case of temporary communication failure is presented. The considered networks are made up of dynamically uncoupled nonlinear systems, coupled through constraints and objectives. The proposed method substitutes affected coupling constraints in such a way that the scheme can not become infeasible, while a decrease in the cost is guaranteed.

A neuro-adaptive augmented nonlinear dynamic inversion approach is proposed in this paper for robust automatic landing of unmanned aerial vehicles. Following the philosophy of indirect adaptive control, a set of linear in weight neural networks are used to rapidly learn the unknown part of the system model online. This continuously updated model is simultaneously used in a dynamic inversion framework that results in an adaptive controller which is quite robust to the modeling inaccuracies (i.e. parametric uncertainties of the model) and external wind shear disturbance. The training rule of neural networks is obtained from Lyapunov stability theory, where a Sobolev norm based Lyapunov function is chosen. This leads to 'directional learning', resulting in fast learning without exciting too much of transient oscillations. Robust performance of this neuro-adaptive augmented nonlinear controller is successfully validated through six degree-of-freedom simulation studies.

This study deals with the problem of controlling DC-to-DC switched power converter of boost type associated with a fuel cell generator. The system is modelled using the PWM technique. The aim is to tightly regulate the output voltage of the converter to a desired reference. The voltage-current characteristic is subject to parameter uncertainty. The system load is in turn time-varying. A nonlinear adaptive controller is designed using the Lyapunov approach, based on the nonlinear model of the system. The controller is formally shown to meet its control objectives.

In this work, a Linear Parameter Varying (LPV) model of the Selective Catalytic Reduction (SCR) system, used for NOx reduction placed as after-treatment systems in diesel engines is developed. The LPV model structure is formed utilising the physical properties of the system yielding only 7 unknown parameters for a third order model structure of the SCR, which is a significant reduction in number of parameters in comparison with other nonlinear models used in this paper. The states of the model however, do not possess any physical interpretation. The LPV model structure is validated using real measured data from cell tests at Scania AB with promising results. The proposed model is compared to previous global nonlinear models of the system, i.e. a nonlinear state space model and a Hammerstein-Wiener model of the system.

This study focuses on the individual sedation model identification problem, and proposes a subspace-based Wiener system identification method. The traditional compartmental pharmacokinetics-pharmacodynamics hypnosis model is considered as a specific Wiener system with the Hill equation nonlinear term. To deal with the Hill nonlinear term, the proposed method employs a set of specific bases to turn the Wiener system into a linear one, and then the subspace orthogonal projection identification method has been implemented to identify the transformed linear model. Compared with the traditional anesthesia model identification methods, the proposed method can effectively overcome the shortage of measurement data, get rid of the estimation of the effect compartment concentration which is impossible to be measured, and improve the individualization performance of the identified model. A simulation study on 24 various virtual patients from the Wang's Simulator has been conducted and validates the efficiency and robustness of the proposed method, and a drug infusion instruction has been provided in order to get relatively accurate identification performance.

Hybrid systems with memory are dynamical systems exhibiting both hybrid and delay phenomena. We present a general modelling framework for such systems using hybrid functional inclusions, whose generalized solutions are defined on hybrid time domains and evolve in the phase space of hybrid memory arcs equipped with the graphical convergence topology. We prove a general existence result based on some basic conditions on the data of hybrid systems. We further establish sufficient conditions for the stability analysis of hybrid systems with memory using Lyapunov-Razumikhin (L-R) functions. We demonstrate the stability results on a general nonlinear system with switching dynamics and state jumps.

In this paper we consider the problem of output control of nonlinear systems in the presence of structural disturbances caused by so called unmodelled dynamics. This paper develops results published in Bobtsov (2002). In Bobtsov (2002) conditions of efficiency of consecutive compensator were found for the case of output stabilization of linear parametrically uncertain plant under conditions of unmodelled asymptotically stable dynamics. We added disturbances as smooth nonlinear function meeting the conditions of sector restriction to the model and synthesized regulator for this case.

In this paper, an MPC scheme for a missile pitch axis autopilot is proposed. The scheme uses a nonlinear prediction model to give it an ability to push the controlled missile very close to its operating limits, and is stabilised through the use of an ellipsoidal terminal constraint. Tracking performance and computational load of the scheme are compared to that with a linear prediction model and other types of terminal constraint. Specifically, the choice of ellipsoidal, polytopic, or no terminal constraint is discussed. The terminally constrained nonlinear MPC scheme achieves comparable solution times to that with a linear prediction model, whilst being more aggressive to give a superior tracking performance.

The paper addresses the disturbance decoupling problem for MIMO discrete-time nonlinear systems. A sufficient conditions are derived to solve the problem by dynamic measurement feedback, i.e. the feedback that depends on measurable outputs only. The solution to the disturbance decoupling problem, described in this paper, is based on the input-output linearization, which is used to linearize certain functions. Two examples are added to illustrate the results.

Wave energy converters (WECs) require active control to maximise energy capture over a wide range of sea conditions, which is generally achieved by making the device resonate. The exaggerated device motion arising at resonance, however, may result in nonlinear effects that are ignored by the linear models that are typically employed. In particular, nonlinear viscous forces are significant for particular device types, such as hinged flaps, which we take as a case study in this paper. The paper develops a general nonlinear WEC control methodology based on pseudospectral methods. The continuous time energy maximization problem is fully discretised (both state and control), and the optimal solution is obtained by solving the resulting finite dimensional optimization problem. By way of example, the nonlinear viscous damping for a hinged flap WEC is incorporate into the control model. It is shown that the ratio of energy captured to energy dissipated is significantly increased with the nonlinear controller, compared to the linear case.

This paper presents Lyapunov-based pursuit guidance law against stationary targets. To design a nonlinear guidance law, Lyapunov candidate function is introduced to reduce the angle between the velocity vector of a missile and the distance vector between the missile and the target. Therefore, the proposed guidance laws have the characteristic of pursuit guidance. To attack a target from a predefined direction, the guidance law should be designed with impact angle constraint at the final phase. To deal with this, the impact angle error is augmented to the Lyapunov candidate function. The proposed guidance laws have simple forms to be implemented easily. Numerical simulations are performed to demonstrate the performance of the proposed guidance laws.

Utilizing the well-established linear quadratic regulator (LQR) theory and augmenting it with online trained neural networks, an innovative nonlinear online trajectory optimization technique is presented in this paper. Neural networks are trained online using close form expression for weight update rule and do not require any iterative process. The overall structure leads to robust optimal control synthesis and works well despite the presence of un-modeled dynamics. This technique is subsequently applied to the challenging problem of satellite formation ying. Simulation studies show that the presented control synthesis approach is able to ensure close formation ying catering for large initial separation, high eccentricity orbits, uncertain semi-major axis of chief satellite and J 2 gravitational e_ects, which is usually considered as an exogenous perturbation.

This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectation maximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.

This paper addresses the forward kinematic model of a class of hyper-redundant continuum robot, namely Compact Bionic Handling Assistant (CBHA). Inspired from the elephant trunk, it can reproduce some biological behaviors of trunks, tentacles, or snakes. Such systems, like the CBHA are subjected to a set of nonlinearities (flexibility, elasticity, redundancy,…) and uncertainties (parameters and modeling), making difficult to build an accurate analytical model, which can be used to develop control strategies. Hence, learning method becomes a suitable approach for such scenarios in order to capture un-modeled nonlinear behaviors of this continuum arm. The proposed approach makes use of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) Neural Networks for the approximation of forward kinematic model (FKM) of CBHA trunk. The experiments have been conducted on the CBHA in order to validate the forward kinematic model where the arm trajectories are generated using a physical coupling with a rigid manipulator. A comparison of both qualitative approaches with a quantitative geometric approach, according to the model accuracy is given at the end of the experiment.

An attempt to evaluate accuracy of Fridman's sampling interval estimates for nonlinear discrete-continuous systems where the controlled plant belongs to a class of cascade passifiable Lurie systems. Numerical results obtained for master-slave configuration of two mobile robots demonstrate good accuracy of Fridman's estimates: error of the sampling interval estimate is less than 25% of the value obtained from extensive simulation. In contrast, the error obtained by conventional method from quadratic Lyapunov function is more than 75% of the value obtained from simulation.

This paper deals with the purely magnetic attitude control problem. Nonlinear equations of attitude motion under environmental disturbances are presented. It is observed that the environmental disturbances affecting the control system appear as unmatched uncertainties. The controlled dynamic system can be represented in regular state-space form, which allows appropriate controller design. Attenuation of the disturbance effects on the steady-state behavior of the attitude angles is still an important problem in small satellite missions. Therefore, the integral sliding mode control method is used to solve the purely magnetic attitude control problem. The control torque vector at the output of the controller acts on the spacecraft after successive manipulations in magnetic actuation and interaction steps. The magnetic attitude control system is designed by using Lyapunov's direct method in the framework of sliding mode control theory. The performance of the resulting control system is evaluated through realistic simulations, and it is seen that the integral sliding mode controller has a superior steady-state performance with respect to the nominal controller.

A new algorithm is proposed for computing locally the linearizing output of single-input and multi-input nonlinear affine system. The algorithm modifies the extended Goursat normal form to iteratively obtain the successive integrations of one dimensional distributions of control system. The algorithm takes ideas from both vector field approach of feedback linearization and exterior differential system tools, hence the name Blended Algorithm. The proposed algorithm leads to a tower like structure depending upon the number of system inputs. Within individual tower, the coordinates are reduced one by one by finding the annihilators of vector fields at each step. The process is repeated till the single vector field is obtained for exact linearizable system. The scheme exhibits reduced computational complexity over the existing methods and can be extended to address feedback linearization of various class of control systems.

Control of the current profile in tokamak plasmas has been shown to play an important role in achieving advanced scenarios that could enable steady-state operation. The nonlinearity and spatially distributed nature of the current profile dynamics motivate the use of model-based control designs. In this work, we consider a control-oriented model of the current profile evolution in DIII-D high-confinement (H-mode) discharges, and the problem of regulating the current profile around a desired trajectory. The PDE model is discretized in space with a finite difference method and a backstepping design is applied to obtain a transformation from the original system into a particular target system with desirable properties. The resulting boundary condition control law is complemented with control laws for the available distributed actuators. The combined control strategy uses nonlinear combinations of the total plasma current, total power, and line averaged density as actuators. Simulation and experimental results show the ability of the controller to track desired targets and to reject input disturbances.

A recent breakthrough in aerial mobile robotics appears with the quadrotor platform. It is particularly suitable for indoor exploration because of its high stability, manoeuvrability and because of the possibility to hover. However quadrotors suffer from their energy consumption and can only fly about 15 minutes. A new design of quadrotor is then proposed in this paper and allows it to move on the ground for energy saving purposes. This paper only focuses on the terrestrial control. A 3DOF non linear dynamic modeling of the terrestrial displacement is firstly obtained and then control laws for trajectory tracking and point stabilization are designed. A flatness approach is used for tracking purpose and a non linear time-varying control law is proposed for point stabilization. The second approach is justified by the fact that the system does not satisfy Brockett's theorem. Simulation results also underline the similar dynamic behaviour of our system with hovercrafts.

In this paper, we develop a tube-based economic MPC framework for nonlinear systems subject to unknown but bounded disturbances. We show that just transferring the design procedure of tube-based stabilizing MPC to an economic MPC framework might not be the optimal choice in terms of the achievable asymptotic average performance. Instead, the asymptotic average performance can possibly be improved by considering the influence of the disturbance explicitly within the design of the control input. This will be done by using a specifically defined integral stage cost, which is the key feature of our proposed robust economic MPC algorithm. Furthermore, we show that for this algorithm, similar results as in nominal economic MPC (i.e., without disturbances) can be established, in particular with respect to bounds on the asymptotic average performance of the resulting closed-loop system as well as stability.

The problem Wiener systems identification is addressed in presence of hysteresis nonlinearities, presently described by the Bouc-Wen model. The latter is nonlinear differential equation involving unknown parameters, some of which coming in nonlinearly. Except for stability, the linear subsystem is arbitrary and, in particular, it is not given a particular structure. By using sine excitations, the identification problem is reformulated as a set of nonlinear prediction-error optimization problems. The latter will be coped with using nonlinear least squares estimators which will formally be shown to be consistent.

This paper tries to address the problem of high computational burden posed by the basis extraction procedure in Proper Orthogonal Decomposition (POD). A proposal for generating approximate snapshots of the nonlinear system, to reduce this burden is presented. Simulation results are shown which demonstrate that a substantial reduction in computational time with no or insignificant deterioration in performance is easily obtained.

In this paper, a new fault diagnosis methodology based on nonlinear system modelling and frequency analysis is developed. The method is derived via resolving several fundamental issues associated with conducting system fault diagnosis using the NARMAX (Nonlinear Auto-Regressive Moving Average with eXougenous inputs) modelling and NOFRFs (Nonlinear Output Frequency Response Functions) based frequency analysis, which was recently proposed and extends the well-known linear system modelling and FRF (Frequency Response Function) based frequency analysis to the nonlinear case. Simulation studies verify the effectiveness of the new method and demonstrate the performance of the method when applied to address a mechanical structural system fault diagnosis problem. The new methodology has the potential to resolve a wide range of input output data based engineering system fault diagnosis problems

The work focuses on the state feedback synthesis of hybrid mechanical systems under unilateral constraints. The problem of robust control of mechanical systems is addressed under unilateral constraints by designing a nonlinear state feedback ** ℋ ∞ -controller developed in the hybrid setting, covering impact phenomena. Performance issues of the developed nonlinear ℋ ∞ -tracking controller are illustrated with numerical tests on a seven-link biped robot.

An approach of iterative predictive learning control (IPLC) is studied with consideration to both precise train trajectory tracking and energy efficient operation. Through designing the predictive cost function, the IPLC approach for input-affine nonlinear systems is formulated and solved in this paper. Its application to train operation is detailed to compromise between punctuality and energy consumption. Rigorous theoretical analysis confirms that the proposed approach can guarantee the asymptotic convergence of train speed and position to desired profiles along iteration axis. Simulation result shows its effectiveness and enegry efficiency.

We propose the stabilization of nonlinear systems with singularities via switching piecewise bilinear (PB) control. The approximated model is fully parametric. Input-output (I/O) feedback linearization is applied to stabilize PB control systems. We construct piecewise Lyapunov-like function for each piecewise region and stabilize the state space using a hybrid control method. Examples confirm the feasibility of our proposals.

Based on the Lyapunov stability theorem, a sliding mode controller is proposed in this paper for a class of uncertain multi-input multi-output (MIMO) nonlinear systems to solve regulation problems. The perturbed plant contains partly unmeasurable states and unknown mismatched and matched perturbations. By utilizing the designed auxiliary dynamic equations for state estimation, the proposed sliding mode controller is able to drive the state's trajectory towards a designated sliding surface in finite time and achieve asymptotic stability. Furthermore, the upper bounds of perturbations are not required in the design process due to some adaptive mechanisms are embedded in the auxiliary dynamic equations and controller. A numerical example is also demonstrated for showing the applicability of the proposed design technique.

A novel passivity-based sliding mode controller for active suspension system with uncertainties is presented in this paper to decrease vibration and increase robustness. Based on the characteristics of the suspension system, interconnection and damping assignment passivity- based control approach is used to construct integral sliding surface. By elaborately designing desired interconnection matrix, damping matrix and energy function, an original integral sliding surface is completed and a subcontroller is obtained to achieve sliding mode dynamic. A simplified form of subcontroller is provided when some of designable parameters are chosen properly. Combining the subcontroller with a nonlinear component, passivity-based sliding mode controller is created. Robustness analysis is given and it is proofed that the integral sliding surface is robustly stable and can be reached. Simulation under three cases circumstances validates the effect of the proposed method.

This paper considers synchronization as a control problem. In particular, a (robust) controller for achieving synchronization in pairs of second order nonlinear systems is designed. The design is inspired by the classical experiment on synchronization of pendulum clocks, as described by Christiaan Huygens. In the proposed control scheme, the systems do not interact directly but through an exogenous system. Ultimately, it is demonstrated that Huygens' controller, can be used to perform in-phase and anti-phase synchronized tasks, with the advantage that 'small' control gains are required. The stability of the closed-loop system is analyzed using perturbation theory and the proposed controller is experimentally validated on a pair of Cartesian robots.

The problem of improvement of existing approaches for evaluation of inferential models used in the advanced process control systems is considered. The method of finding the identifiability index limit, based on the physically-chemical essence of the industrial distillation process, for the inferential models containing nonlinear transformations of measured inputs is given. The problem of handling the narrow variability range of key inputs in the training sample is considered. The paper contains the example of evaluating the inferential models based on the proposed method applied to the nonlinear industrial distillation plant.

This paper presents a method of designing a suboptimal Kalman filter (SKF) for nonlinear systems, including uncertainties on the system parameter dynamics. In general, governing dynamics behind real-world phenomena has significant associated uncertainties and all we can presume is the approximation to system models. We thus need a robust filter that is capable of rapidly following up the unknown system model parameters while adequately estimating the system states as well. The SKF is suboptimal in terms of standard square errors that the KF minimizes at each time step and this suboptimization results in the enhancement of filter's parameter adaptation ability. The nonlinearity of system models is handled by using an unscented transforming statistical linearization without losing the Gaussianity of states to be estimated. We confirm the effectiveness of the proposed filter by numerical simulations.

We propose a torque overlay based robust steering wheel angle control of electric power steering (EPS) for lateral control using backstepping design. The main contribution of this paper is that the proposed method is designed based on torque overlay and that the global uniform ultimate boundedness of the steering wheel angle tracking error is guaranteed using only steering wheel angle feedback with external disturbances. The key idea is to make the EPS dynamics be simplified. Then, the external disturbances, system function, and input gain uncertainty are regarded as a disturbance. An augmented observer is designed to estimate the full state and the disturbance. A nonlinear damping controller is developed via backstepping to suppress a position tracking error using input-to-state stability property. The proposed method uses only steering wheel angle feedback and nominal value of the input gain. The proposed method is simple to implement in real-time control and robust to the parameter uncertainties and the external disturbances. Since the proposed method is designed based on torque overlay as add-on type, it can be integrated with the conventional EPS system facilitating driver's intervention.

In this paper, we consider the leader-following consensus problem for multiple uncertain rigid spacecraft systems with the attitude being represented by unit quaternion. Existing results on this problem rely on the assumption that all parameters of the rigid spacecraft system are known exactly. By employing a nonlinear distributed observer for the leader system, we first convert the leader-following consensus problem into a global adaptive stabilization problem of a well defined error system. Then, under the standard assumption that the state of the leader can reach every follower through a path, we further show that this stabilization problem is solvable by a distributed adaptive control law.

This paper presents observer-based fuzzy control for nonlinear fractional-order systems with the fractional order α satisfying 1

An application of adaptive fuzzy sliding mode control is presented in this paper. The parameters of sliding mode control (the extended feedback gain and switching gain) are adapted during the control process using a fuzzy self-learning mechanism such that better control performance in comparison with sliding mode control with fixed parameters without chattering is achieved. Moreover, the nonlinear functions describing state space model of the controlled plant do not need to be known. The method is applied on control of a laboratory model of a pendulum on a cart.

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with particle swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

In this paper we introduce the concept of universal stabilizability: the condition that every solution of a nonlinear system can be globally stabilized. We give sufficient conditions in terms of the existence of a control contraction metric, which can be found by solving a pointwise linear matrix inequality. Extensions to approximate optimal control are straightforward. The conditions we give are necessary and sufficient for linear systems and certain classes of nonlinear systems, and have interesting connections to the theory of control Lyapunov functions., Comment: Conference submission

An enhanced Fault Detection and Isolation (FDI) technique based on 't' statistical test is presented here employing a residual based R-Adaptive Unscented Kalman Filter (AUKF). Although the use of AUKF is common for estimation problem, employing AUKF for FDI duty is quite rare. Adaptive Unscented Kalman filtering framework is chosen here due to its derivative free calculations in the algorithm and reportedly better accuracy. Monitoring the 't' statistic of the innovation sequence, measurement faults are detected and corresponding alarm signals are generated here. The presented 't' statistic based FDI technique comprising of consistency checking and abruptness checking is straight forward, demands less computational burden and minimizes false alarms. The superiority of the proposed AUKF based FDI method compared to non adaptive, nonlinear filtering based FDI techniques is demonstrated here by an extensive simulation study executed on a nonlinear LEO satellite planar model. The use of AUKF has provided faster convergence speed and is also found to be advantageous for the situations where the measurement noise statistics are unknown.