Output Tracking Performance Research Articles

Intelligent Tracking Control for a Class of Uncertain High-Order Nonlinear Systems.

This brief is concerned with the problem of intelligent tracking control for a class of high-order nonlinear systems with completely unknown nonlinearities. An intelligent adaptive control algorithm is presented by combining the adaptive backstepping technique with the neural networks' approximation ability. It is shown that the practical output tracking performance of the system is achieved using the proposed state-feedback controller under two mild assumptions. In particular, by introducing a parameter in the derivations, the tracking error between the time-varying target signal and the output can be reduced via tuning the controller design parameters. Moreover, in order to solve the problem of overparameterization, which is a common issue in adaptive control design, a controller with one adaptive law is also designed. Finally, simulation results are given to show the effectiveness of the theoretical approaches and the potential of the proposed new design techniques.

Design and Performance Analysis of Incremental Networked Predictive Control Systems.

This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

Nonfragile H∞ output tracking control for uncertain singular Markovian jump delay systems with network‐induced delays and data packet dropouts

This article deals with the problem of nonfragile H∞ output tracking control for a kind of singular Markovian jump systems with time‐varying delays, parameter uncertainties, network‐induced signal transmission delays, and data packet dropouts. The main objective is to design mode‐dependent state‐feedback controller under controller gain perturbations and bounded modes transition rates such that the output of the closed‐loop networked control system tracks the output of a given reference system with the required H∞ output tracking performance. By constructing a more multiple stochastic Lyapunov–Krasovskii functional, the novel mode‐dependent and delay‐dependent conditions are obtained to guarantee the augmented output tracking closed‐loop system is not only stochastically admissible but also satisfies a prescribed H∞‐norm level for all signal transmission delays, data packet dropouts, and admissible uncertainties. Then, the desired state‐feedback controller parameters are determined by solving a set of strict linear matrix inequalities. A simple production system example and two numerical examples are used to verify the effectiveness and usefulness of the proposed methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 396–411, 2016

Adaptive output feedback fault-tolerant control design for hypersonic flight vehicles

In this paper, an adaptive output feedback fault-tolerant controller is developed for the longitudinal dynamics of a generic hypersonic flight vehicle in the presence of parameter uncertainties, actuator faults and external disturbances. Firstly, the derivatives of the output are calculated repeatedly so that the relative degree of the system is obtained. Then feedback linearization is used to design the nominal controller. Considering the occurrence of actuator faults, a fault-tolerant controller is developed based on the nominal feedback linearization controller to accommodate the effect of actuator fault, ensure system stability and recover desirable tracking performance. Since some of the states are difficult to measure during actual hypersonic flight, the high-gain observer technique is adopted to achieve output feedback fault-tolerant control. Adaptive laws are designed for updating the controller parameters when both the plant parameters and actuator fault parameters are unknown. Closed-loop stability and output tracking performance are analyzed rigorously. Simulation results verify the effectiveness of the proposed adaptive fault-tolerant control scheme.

State observer based sensor less control using Lyapunov's method for boost converters

This study proposes a state observer based sensorless controller using the Lyapunov's direct method for boost converters. The proposed controller derived from the Lyapunov method considers a large-signal model and the non-linearity of the boost converter, which allows accurate output tracking performance and stability. In addition, a state observer is constructed to estimate the inductor current using an output voltage, an input voltage and a switch control signal. The developed observer enables the proposed controller to be realised with no current sensor for the inductor current, and results in a low cost as well as reliable system free from the noise problem associated with the sensor. The non-linear controller incorporated into the observer is designed with appropriate observer gains determined by linear matrix inequalities. Experimental results are presented to verify the stable operation and output tracking capability for large-signal transients of the proposed sensorless controller based on the Lyapunov method.

RISE-Based Precision Motion Control of DC Motors With Continuous Friction Compensation

Continuous friction compensation along with other modeling uncertainties is concerned in this paper, to result in a continuous control input, which is more suitable for controller implementation. To accomplish this control task, a practical method, named as robust integral of the sign of the error controller, is synthesized with a continuous differentiable friction model for high-accuracy motion control of a dc motor. To reduce the noise sensitivity and further improve the tracking accuracy, a desired compensation technique is employed in the proposed controller, in which the model compensation term depends on the reference trajectory only, and its global stability is guaranteed by a proper robust feedback law. Furthermore, the proposed controller theoretically guarantees an asymptotic output tracking performance even in the presence of modeling uncertainties, which is very important for high-accuracy control of motion systems. Comparative experimental results are obtained for the motion control of a dc motor drive system to verify the high-performance nature of the proposed control strategy.

Output-Tracking-Error-Constrained Robust Positioning Control for a Nonsmooth Nonlinear Dynamic System

An output-tracking-error-constrained dynamic surface control (DSC) is proposed for the robust output positioning of a multiple-input-multiple-output nonlinear dynamic system in the presence of both friction and deadzone nonsmooth nonlinearities. An error transformation method and simple barrier Lyapunov function are also proposed to ensure the prescribed output tracking performance and stability without requiring specific observations of the friction and deadzone parameters. In addition, a new adaptive cerebellar model articulation controller-echo state neural networks system is proposed to deal with an unknown nonlinear function to improve the positioning performance. The boundedness of the overall closed-loop signals and the prescribed performance constraints were guaranteed, and precise positioning performance was also ensured regardless of the effects of friction and deadzone. The proposed control scheme was evaluated by simulation and experiment.

Output Tracking Control for Networked Systems: A Model-Based Prediction Approach

This paper studies the problem of output tracking for networked control systems with network-induced delay, packet disorder, and packet dropout. The round-trip time (RTT) delay is redefined to describe these communication constraints in a unified way. By including the output tracking error as an additional state, the output tracking problem is converted into the stabilization problem of an augmented system. Based on the observer of the original state increment and the feedback of the output tracking error, a model-based networked predictive output tracking control (NPOTC) scheme is proposed to actively compensate for the random RTT delay. The closed-loop stability is proved to be independent of the RTT delay, and the separation principle for the design of the observer-based state feedback controller is still held in the NPOTC system. A two-stage controller design procedure is presented, which not only guarantees the stability of the closed-loop NPOTC system but also achieves the same output tracking performance as that of the local control system for time-varying reference signals. Both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

Adaptive control of stochastic Hammerstein systems with dead-zone input non-linearity

This article presents a new control scheme for H systems with symmetric dead-zone input non-linearity and coloured noise. Based on the parameterized H model, we derive a dead-zone compensating control law and a recursive estimator. Theoretical analysis shows that global convergence and closed-loop stability can be guaranteed using the proposed algorithms. What is more, theoretical results show that the output tracking error is convergent with a specific residue, which is related to the variance of the noise and the parameter estimate. Simulation results confirm that the proposed method leads to satisfactory output tracking performance.

Second-Order Sliding Mode Strategy for Air–Fuel Ratio Control of Lean-Burn SI Engines

Higher fuel economy and lower exhaust emissions for spark-ignition engines depend significantly on precise air-fuel ratio (AFR) control. However, the presence of large time-varying delay due to the additional modules integrated with the catalyst in the lean-burn engines is the primary limiting factor in the control of AFR. In this paper, the engine dynamics are rendered into a nonminimum phase system using Padé approximation. A novel systematic approach is presented to design a parameter-varying dynamic sliding manifold to compensate for the instability of the internal dynamics while achieving desired output tracking performance. A second-order sliding mode strategy is developed to control the AFR to remove the effects of time-varying delay, canister purge disturbance, and measurement noise. The chattering-free response of the proposed controller is compared with conventional dynamic sliding mode control. The results of applying the proposed method to the experimental data demonstrate improved closed-loop system responses for various operating conditions.

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.

Optimal adaptive control schemes for PHB production in fed-batch fermentation of Ralstonia eutropha

A dynamic model for a fed-batch fermentation of Ralstonia eutropha for PHB production is presented. Based on the simplified kinetics, some of kinetic parameters are very sensitive to uncertainties. To develop the robust and feasible feeding strategy, the specific optimal adaptive control strategy is determined by solving the constrained discrete-time optimization algorithm using the genetic algorithm solver in Matlab. The proposed control methods are implemented to the feed flow manipulation with stepwise changes. Through Lyapunov stability analysis, the closed-loop stability regarding the state estimation errors and parametric uncertainties is investigated. Finally, simulations show that the satisfactory output tracking performance is achieved under the two-input control configuration.

Exponential [formula omitted] output tracking control for positive switched linear systems with time-varying delays

The problem of exponential L1 output tracking control for positive switched linear systems with time-varying delays is addressed in this paper. The exponential L1-gain performance index is introduced to study such a problem. By resorting to the average dwell time approach, and also by constructing an appropriate piecewise co-positive type Lyapunov–Krasovskii functional, a new delay-dependent exponential stability criterion is developed, and the exponential L1-gain performance is analyzed. Based on the results obtained, a state feedback controller is constructed such that the exponential L1 output tracking performance is satisfied. A numerical example is given to demonstrate the effectiveness of the proposed method.

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme.

Learning from NN output feedback control of nonlinear systems in Brunovsky canonical form

In this paper, we investigate the learning issue in the adaptive neural network (NN) output feedback control of nonlinear systems in Brunovsky canonical form with unknown affine term. With only output measurements, a high-gain observer (HGO) is employed to estimate the derivatives of the system output which may be associated with the generation of peaking phenomenon. The adverse effect of peaking on learning and its elimination strategies are analyzed. When the gain of HGO is chosen too high, it may cause the failure of learning from the unknown closed-loop system dynamics. Hence, the gain of HGO is not chosen too high to relieve peaking and guarantee the accuracy of the estimated system states. Then, learning from the unknown closed-loop system dynamics can be achieved. When repeating the same or similar control tasks, a neural learning controller is presented which can effectively recall and reuse the learned knowledge to guarantee the output tracking performance. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Robust nonlinear multivariable tracking control design to a manipulator with unknown uncertainties using operator-based robust right coprime factorization

In this paper, an operator-based robust nonlinear multivariable tracking control for a manipulator with uncertainties is proposed by using a robust right coprime factorization approach. In general, there exist unknown modelling errors in measuring structural parameters of the manipulator and external disturbances in real situations. In the present control system design, the effect of the modelling errors and disturbance on system performance is considered to be uncertainties in the manipulator dynamics. Considering the uncertainties, an operator-based robust nonlinear multivariable tracking control using robust right coprime factorization is studied. That is, firstly a robustly stable control based on robust right coprime factorization is designed. Secondly, a robust nonlinear multivariable tracking system is proposed for improving the trajectory of the manipulator, and the output tracking performance is evaluated based on the exponential iteration theorem. Finally, the effectiveness of the designed system is confirmed by simulation results.

Robust Adaptive Fuzzy Control of MIMO Nonlinear Systems with Higher-Order and Unmatched Uncertainties

In this paper, a robust adaptive fuzzy controller is designed for the output tracking control problem of mu lti -input mult i-output (MIMO) nonlinear systems with higher-order and un matched uncertainties. For real mechanical systems, the strength of the unmatched uncertainties is bounded by the pth-order polynomials in states, in contrast to other works assuming that these uncertainties are bounded by the first-order polynomials in states. Based on the combination of the H ∞ optimal control with fuzzy systems and some feasible adaptation laws, the proposed robust adaptive fuzzy controller can not only guarantee that all the signals in the whole closed-loop systems are bounded, but also obtain that the output tracking performance of M IMO nonlinear uncertain systems is well established. A series of computer simulat ions are illustrated to demonstrate the validity of the proposed control scheme.

Adaptive Actuator Failure Compensation of Uncertain Nonlinear Systems with Dead-Zone Inputs

As is well known, dead-zone nonlinearity often exists in practical actuators and unknown actuator failures may occur during system operation. However, available results based on adaptive approaches to compensate unknown failures of such actuators are still very limited. In this paper, we address such a problem by considering a class of uncertain nonlinear systems with dead-zone hysteresis nonlinearities inputs. An adaptive control scheme is proposed by using backstepping technology to compensate the uncertainties caused by unknown failures of dead-zone actuators. The boundedness of all signals is established for the closed loop system and the desired output tracking performance is maintained for any unknown actuator failure.

Output Tracking Control of Switched Hybrid Systems: A Fliess Functional Expansion Approach

The output tracking problem is investigated for a nonlinear affine system with multiple modes of continuous control inputs. We convert the family of nonlinear affine systems under consideration into a switched hybrid system by introducing a multiple-valued logic variable. The Fliess functional expansion is adopted to express the input and output relationship of the switched hybrid system. The optimal switching control is determined for a multiple-step output tracking performance index. The proposed approach is applied to a multitarget tracking problem for a flight vehicle aiming for one real target with several decoys flying around it in the terminal guidance course. These decoys appear as apparent targets and have to be distinguished with the approaching of the flight vehicle. The guidance problem of one flight vehicle versus multiple apparent targets should be considered if no large miss distance might be caused due to the limitation of the flight vehicle maneuverability. The target orientation at each time interval is determined. Simulation results show the effectiveness of the proposed method.

How to extend the travel range of a nanobeam with a robust adaptive control scheme: A dynamic surface design approach

In this study, an adaptive robust tracking control scheme for a nanomechanical beam is proposed. The mathematical nanobeam model takes into consideration the presence of parametric uncertainties, and external disturbances, and fringing field effects and intermolecular forces, are also incorporated. The proposed backstepping controller is enhanced with an adaptive dynamic surface technique and an H∞ control scheme. The overall effectiveness of the designed controller is checked and illustrated with the presented results. These results show that the desired transient output tracking performance is achieved, and that the closed loop system exhibits good robustness to system uncertainties. Particular attention has been paid, so as to identify any relevance between the variations in the controller's design parameter and the system's output response and performance.