Strictly Positive Real Research Articles

Observer-based adaptive neural iterative learning control for a class of time-varying nonlinear systems

In this paper an adaptive iterative learning control scheme is presented for the output tracking of a class of nonlinear systems. An observer is designed to estimate the tracking errors. A mixed time domain and s-domain representation is constructed to derive an error model with relative degree one for our purpose. And time-varying radial basis function neural network is employed to deal with system uncertainty. A new signal is constructed by using a first-order filter, which removes the requirement of strict positive real (SPR) condition and identical initial condition of iterative learning control. Based on property of hyperbolic tangent function, the system tracing error is proved to converge to the origin as the iteration tends to infinity by constructing Lyapunov-like composite energy function, while keeping all the closed-loop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.

A robust optimal design for strictly positive realness in recursive parameter adaptation

SummaryThis paper provides an optimization‐based approach to assure the strict positive real (SPR) condition in a set of recursive parameter adaptation algorithms (PAA). The developed algorithms and tools enable a multiobjective formulation of the SPR problem, creating new controls of the stability and parameter convergence in PAAs. In addition to assuring the SPR condition for global stability in PAAs, we provide an algorithmic solution for uniform convergence under performance constraints in PAAs. Several new aspects of parameter convergence were observed with the adoption of the algorithm in a narrow‐band identification problem. The proposed algorithm is verified in simulation and experiments on a precision motion control platform in advanced manufacturing.

Output-Feedback Controller Based Projective Lag-Synchronization of Uncertain Chaotic Systems in the Presence of Input Nonlinearities

This paper solves the problem of projective lag-synchronization based on output-feedback control for chaotic drive-response systems with input dead-zone and sector nonlinearities. This class of the drive-response systems is assumed in Brunovsky form but with unavailable states and unknown dynamics. To effectively deal with both dead-zone and sector nonlinearities, the proposed controller is designed in a variable-structure framework. To online learn the uncertain dynamics, adaptive fuzzy systems are used. And to estimate the unavailable states, a simple synchronization error is constructed. To prove the stability of the overall closed-loop system (controller, observer, and drive-response system) and to design the adaptation laws, a Lyapunov theory and strictly positive real (SPR) approach are exploited. Finally, three academic examples are given to show the effectiveness of this proposed lag-synchronization scheme.

ADAPTIVE ANTI-WINDUP CONTROLLER FOR ACTUATOR CONSTRAINED LINEAR CONTROL SYSTEMS

This paper proposes an application of self-tuning adaptive controlin the design of anti-windup controller incorporating the existingcontroller of the single input-single output linear control systemswith strict positive realness.

Observer-based robust composite adaptive fuzzy control by uncertainty estimation for a class of nonlinear systems

This paper presents an Observer–based Robust Composite Adaptive Fuzzy controller (ORCAFc) for a class of nonlinear systems with uncertainties and external disturbance. Based on the adaptive fuzzy method and modeling error between the system states observer and serial–parallel estimator, an observer-based Composite Adaptive Fuzzy (CAF) scheme is proposed that uses feedback error function as input to approximate and adaptively compensate the unknown uncertainties and external disturbance of the system. Also, an H∞ controller term is employed to compensate the both fuzzy approximation errors and observer errors. The proposed method guarantees the stability of the closed–loop system based on the Strictly Positive Real (SPR) condition and Lyapunov theory.

Neural adaptive quantized output-feedback control-based synchronization of uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities

This research is concerned with the problem of generalized function projective synchronization of nonlinear uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities. The considered problem is challenging owing to the presence of unmeasured master-slave system states, external dynamical disturbances, unknown nonlinear system functions, unknown time-varying delays, quantized outputs, unknown control directions unknown actuator nonlinearities (backlash-like hysteresis, dead-zone and asymmetric saturation actuators) and distinct fractional-orders. Under some mild assumptions and using Caputo's definitions for fractional-order integrals and derivatives, the design procedure of the proposed neural adaptive controller consists of a number of steps to solve the generalized function projective synchronization problem. First, smooth functions and the mean value theorem are utilized to overcome the difficulties from actuator nonlinearities and distributed time-varying delays, respectively. Then, a simple linear observer is established to estimate the unknown synchronization error variables. In addition, a Nussbaum function is incorporated to cope with the unknown control direction and a neural network is adopted to tackle the unknown nonlinear functions. The combination of the frequency distributed model, the Razumikhin Lemma, the neural network parameterization, the Lyapunov method and the Barbalat's lemma is employed to perform the stability proof of the closed-loop system and to derive the adaption laws. The major advantages of this research are that: (1) the Strictly Positive Real (SPR) condition on the estimation error dynamics is not required, (2) the considered class of master-slave systems is relatively large, (3) all signals in the resulting closed-loop systems are semi-globally uniformly ultimately bounded and the synchronization errors semi-globally converge to zero. Finally, numerical examples are presented to illustrate the performance of the proposed synchronization scheme.

On the Equivalence Between Strict Positive Realness and Strict Passivity of Linear Systems

This technical note presents a proof of the equivalence between the strict passivity of linear time-invariant (LTI) controllable and observable systems and the strict positive realness of their transfer function matrices, where the direct feedthrough D is possibly a non-zero matrix. Although both properties guarantee asymptotic stability, the former is a time domain condition and the latter is a frequency domain concept. A numerical example illustrates our main results.

From adaptive control to variable structure systems – seeking harmony

From adaptive control to variable structure systems – seeking harmony

Adaptive observer-based fault estimation for a class of Lipschitz nonlinear systems

Abstract Fault input channels represent a major challenge for observer design for fault estimation. Most works in this field assume that faults enter in such a way that the transfer functions between these faults and a number of measured outputs are strictly positive real (SPR), that is, the observer matching condition is satisfied. This paper presents a systematic approach to adaptive observer design for joint estimation of the state and faults when the SPR requirement is not verified. The proposed method deals with a class of Lipschitz nonlinear systems subjected to piecewise constant multiplicative faults. The novelty of the proposed approach is that it uses a rank condition similar to the observer matching condition to construct the adaptation law used to obtain fault estimates. The problem of finding the adaptive observer matrices is formulated as a Linear Matrix Inequality (LMI) optimization problem. The proposed scheme is tested on the nonlinear model of a single link flexible joint robot system.

Robust switched control based on strictly positive real systems and variable structure control techniques

SummaryThis paper is concerned with the design of variable structure controllers for uncertain switched linear plants. The proposed method is based on Lyapunov–Metzler inequalities and on properties of strictly positive real (SPR) systems, with the advantage that it can be applied in control of uncertain switched linear system. The first contribution shows that there exists a solution of the SPR synthesis for a class of linear uncertain switched systems using switched static output feedback, if and only if there exists a solution of this SPR synthesis with switched static state feedback. This result is a generalization of known conditions regarding this problem, for linear time‐invariant and known (without uncertainties) plants, and offers a possible procedure for output feedback SPR synthesis: first solve the state feedback SPR synthesis and then, using the obtained solution, find a solution for the output feedback SPR synthesis. Next are presented necessary and sufficient (sufficient) conditions for the SPR synthesis for a class of linear uncertain switched systems using switched static output feedback, for plants with the same number of inputs and outputs (with the number of outputs greater than the number of inputs). Two examples illustrate the effectiveness of the robust control system, including applications of the proposed methods, considering switched static state feedback and switched static output feedback, in the design of switching control strategies for active suspensions systems in road vehicles. Performance indices such as decay rate, guaranteed cost, and restriction in the norm of controller gains are considered in these designs. Copyright © 2016 John Wiley & Sons, Ltd.

Consensus Control for Heterogeneous Multiagent Systems

We study distributed output feedback control for a heterogeneous multi-agent system (MAS), consisting of N different continuous-time linear dynamical systems. For achieving output consensus, a virtual reference model is assumed to generate the desired trajectory that the MAS is required to track and synchronize. A distinct feature of our results lies in the local optimality and robustness achieved by our proposed consensus control algorithm. In addition our study is focused on the case when the available output measurements contain only relative information from the neighboring agents and reference signal. Indeed by exploiting properties of strictly positive real (SPR) transfer matrices, conditions are derived for the existence of distributed output feedback control protocols, and solutions are proposed to synthesize the stabilizing and consensus control protocol over a given connected digraph. It is shown that design techniques based on the LQG, LQG/LTR and H-infinity loop shaping can all be directly applied to synthesize the consensus output feedback control protocol, thereby ensuring the local optimality and stability robustness. Finally the reference trajectory is required to be transmitted to only one or a few agents and no local reference models are employed in the feedback controllers thereby eliminating synchronization of the local reference models. Our results complement the existing ones, and are illustrated by a numerical example.

Application of Passivity to Adaptive Control Compensation Systems

In this paper the Model Reference Adaptive System (MRAS) is considered. The feedforward gain adjustment is done by using adaptive technique and feedforward gain compensation by passivity-based technique is presented. The adaptation of feedforward gain is considered by using MIT rule and the effect of variation of parameter values and adaptation gain on the response of the system is simulated. But to achieve more stable system, lyapunov stability theorem is used for parameter adjustment. It has been shown that the adjustment rule guarantees that error goes to zero. Finally input-output stability theorem is used to construct adjustment rules for the adaptive system. The passivity theorem is applied to construct adjustment laws. The compensating network is introduced so that the transfer function relating the error is Strictly Positive Real (SPR). The compensator is designed by using Kalman – Yakubovich (KY) Lemma. Simulation results are furnished after implementing the adaptive techniques. Algorithm for the design of compensator using KY Lemma is also presented

Observer-based adaptive interval type-2 fuzzy control of uncertain MIMO nonlinear systems with unknown asymmetric saturation actuators

An observer-based controller is proposed for a class of uncertain MIMO nonlinear systems preceded by actuators with unknown asymmetric saturations. The gains of actuators are unknown nonlinear functions that are generally functions of states. The unknown nonlinearities are approximated by interval type-2 fuzzy systems where the consequent parameters are tuned adaptively. Meanwhile, the interval type-2 fuzzy systems due to their structure can cope with the uncertainties in antecedent membership functions. The states of the system do not need to be known. To tackle the unavailability of states an observer based on strictly positive real (SPR) theory is designed to estimate the states. To cope with the fuzzy approximation error and external disturbances an adaptive continuous robust structure is proposed. The continuous robust structure due to adaptiveness does not need the bound of uncertainties and due to continuity does not have the chattering problem. All the adaptive laws are derived via Lyapunov synthesis approach. Meanwhile, all the signals in the closed-loop system are guaranteed to be semi-globally uniformly ultimately bounded (SGUUB) and the mean-square tracking error can be made arbitrarily small. Simulation results reveal the effectiveness of the proposed method in terms of coping well with the uncertainties and saturation actuators while maintaining satisfactory performance.

Robust Controller Design Of A Wheelchair Mobile Via Lmi Approach To Erp Systems With Feedback Output

This article discusses the design of robust controller applied to Wheelchair Furniture via Linear Matrix Inequalities (LMI), to obtain Strictly Positive Real (SPR) systems. The contributions of this work were the choice of a mathematical model for wheelchair: mobile with uncertainty about the position of the center of gravity (CG), the decoupling of the kinematic and dynamical systems, linearization of the models, the headquarters building of parametric uncertainties, the proposal of the control loop and control law with a specified decay rate.

Consistent parameter estimation and convergence properties analysis of hammerstein output-error models

This paper presents an on-line bias-compensating recursive least squares (BCRLS) identification algorithm for Hammerstein output-error models disturbed by non-martingale difference sequence noise. By introducing an auxiliary vector uncorrelated with the noise, the consistent parameter estimation is obtained without the strictly positive real (SPR) condition. Convergence analysis of the recursive algorithm is performed using the ordinary differential equation (ODE) method. The simulation results validate the algorithm proposed.

Output Feedback Adaptive Neural Control Without Seeking SPR Condition

AbstractFor output‐feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low‐pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output‐feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output‐feedback control law is introduced based on a state‐feedback indirect ANC structure. An adaptive neural network (NN) observer is applied to estimate immeasurable system state variables. The output estimation error rather than the basis functions is filtered and the filter output is employed to update NNs. Under given initial conditions and sufficient control parameter constraints, it is proved that the closed‐loop system is uniformly ultimately bounded stable in the sense that both the state estimation errors and the tracking errors converge to small neighborhoods of zero. An illustrative example is provided to demonstrate the effectiveness of this approach.

Output feedback adaptive fuzzy control of uncertain MIMO nonlinear systems with unknown input nonlinearities

An adaptive fuzzy output feedback controller is proposed for a class of uncertain MIMO nonlinear systems with unknown input nonlinearities. The input nonlinearities can be backlash-like hysteresis or dead-zone. Besides, the gains of unknown input nonlinearities are unknown nonlinear functions. Based on universal approximation theorem, the unknown nonlinear functions are approximated by fuzzy systems. The proposed method does not need the availability of the states and an observer based on strictly positive real (SPR) theory is designed to estimate the states. An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances. The semi-global asymptotic stability of the closed-loop system is guaranteed via Lyapunov approach. The applicability of the proposed method is also shown via simulations.

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.

Adaptive speed/position control of induction motor based on SPR approach

A sensorless speed/position tracking control scheme for induction motors is proposed subject to unknown load torque via adaptive strictly positive real (SPR) approach design. A special nonlinear coordinate transform is first provided to reform the dynamical model of the induction motor. The information on rotor fluxes can thus be derived from the dynamical model to decide on the proportion of input voltage in the d-q frame under the constraint of the maximum power transfer property of induction motors. Based on the SPR approach, the speed and position control objectives can be achieved. The proposed control scheme is to provide the speed/position control of induction motors while lacking the knowledge of some mechanical system parameters, such as the motor inertia, motor damping coefficient, and the unknown payload. The adaptive control technique is thus involved in the field oriented control scheme to deal with the unknown parameters. The thorough proof is derived to guarantee the stability of the speed and position of control systems of induction motors. Besides, numerical simulation and experimental results are also provided to validate the effectiveness of the proposed control scheme.

Fixed‐Structure ℋ∞ Controller Design for Systems with Polytopic Uncertainty: An LMI Solution

AbstractThis note provides a new method for fixed‐structure H∞ controller design for discrete‐time single input single output (SISO) systems with polytopic uncertainty. New conditions are derived based on the concept of robust strict positive realness (SPRness) of an uncertain polynomial with respect to a parameter‐dependent polynomial. The quality of this approximation depends on this SPR‐maker. A procedure is proposed for choosing the parameter‐dependent SPR‐maker that guarantees the improvement of the performance for the designed controller in comparison with the traditional approaches employed fixed SPR‐makers. The proposed conditions are given in terms of solutions to a set of linear matrix inequalities. The effectiveness of the proposed approach is demonstrated by comparing with the existing results.