Robust Adaptive Feedback Control Research Articles

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Robust adaptive regulation of magnetic levitation systems with input quantization and external disturbances

This paper investigates two intricate problems in magnetic levitation systems: the loss of the information during the transmission over a limited communication network channel and the chattering caused by external disturbances. By combining input quantization technique with two infinitesimal smooth functions, we successfully construct a continuous robust adaptive feedback controller which stabilizes the position of the levitated electromagnet at the equilibrium point. Compared with the existing methods, the proposed quantized control strategy not only guarantees the global stability with asymptotic state regulation of the system even if there are external disturbances, but also exhibits better performance than that of traditional PID control. The simulation validates the robustness and effectiveness of the proposed scheme.

Modelling and vibration control for deep-sea robot lifting system with time variable length and nonlinear disturbance observer

In this paper, we propose a mathematical model meant to eliminate the vibration of deep-sea robot lifting system with time-variant length and harsh environmental conditions. The variable-length umbilical cable is modelled as a moving string with additional lumped-mass that has a partial differential equation (PDE) theoretically, spatially discretized by the modified assumed modes method (AMM). Three features of the deep-sea robots are considered in the control schema i.e. non-matched uncertainties, rigid-flexible couplings, and input saturation. By combining Lyapunov theory and LaSalle's invariance theorem, a robust adaptive feedback control is proposed to eliminate vibration of the deep-sea robot. In-addition, a nonlinear disturbance observer (NDO) is scientifically designed to estimate the unknown boundary uncertainties and their couplings, where the stability of whole control system is simultaneously presented. A nonlinear function is further developed to settle out the potential problem of input nonlinearities for the actuator. The dynamic modelling of the system is first illustrated by the ADAMS simulation and followed by more detailed analyses of different cases to demonstrate the effectiveness of the current method.

A Novel Extended Desired Compensation Adaptive Law for High-Speed Pick-and-Throw with PKMs

This paper focuses on the development of a new revised Desired Compensation Adaptive Law (DCAL). DCAL is a model-based adaptive control strategy consisting of three main parts: (i) an adaptive feedforward term, (ii) a linear PD feedback term, and (iii) a nonlinear compensation term. In order to deal with highly nonlinear dynamic systems characterized by their abundant uncertainties and parameters variations, we propose to revise the original DCAL control law by adopting adaptive feedback gains depending on the system state errors. Besides, DCAL controller is known for its robustness against measurement noise thanks to its desired compensation design, but a large amount of external disturbances are still not compensated by such a design. Therefore, the proposed DCAL with adaptive gains (DCAL-AG) is extended with a sliding-based term to further improve its robustness and the overall performance. A model-based robust adaptive feedback controller appropriate to the control of nonlinear systems in real-time applications is thereby obtained. To demonstrate the improvements brought by the proposed control strategy, numerical simulations have been conducted on a Delta-link parallel robot named T3KR in a ”Pick-and-Throw” application task at different operating conditions.

Uniform robust exact differentiator-based output feedback adaptive robust control for DC motor drive systems

This paper studies a high-accuracy motion control method named output feedback adaptive robust control for a dc motor with uncertain nonlinearities and parametric uncertainties, which always exist in physical servo systems and deteriorate their tracking performance. As only position signal is measurable, a uniform robust exact differentiator (URED) for the unmeasurable states and adaptive control for the parametric uncertainties are integrated in the model compensation term; and the robust control is applied to handle uncertain nonlinearities and stabilize the system. Then, the stability of the closed-loop system is proved theoretically. Finally, simulation and experimental results are studied for a dc motor system to prove the control performance of the proposed control method.

Output feedback adaptive robust control of hydraulic actuator with friction and model uncertainty compensation

This paper studies output feedback control of hydraulic actuators with modified continuous LuGre model based friction compensation and model uncertainty compensation. An output feedback adaptive robust controller is constructed which combines the adaptive control part and the robust control part seamlessly. The adaptive part is constructed to handle the parametric uncertainties existed in the model. The residuals coming from parameter adaption and the unmodeled dynamics are taken into consideration by the robust part. Since only the position signal is available, the velocity, pressure, and internal friction states are obtained by observation or estimation. The errors coming from observation and estimation are also dealt with the robust part. Furthermore, the convergence of the closed-loop controller–observer scheme is achieved by the Lyapunov method in the presence of parametric uncertainties only. Extensive comparative experiments performed on a hydraulic actuator demonstrate the effectiveness of the proposed controller–observer scheme.

Robust trajectory tracking control of cable-driven parallel robots

In this paper, a robust tracking controller is designed for fully constrained cable-driven parallel robots (CDPRs). One of the main challenges of controller design for this type of robotic systems is that the cables should always be in tension, where this tension is generally generated through an actuator mechanism coupled with gearboxes. On the other hand, the presence of parametric and nonparametric modeling uncertainties is a common problem in designing a precise nonlinear tracking controller for these manipulators. To deal with these problems, in this paper two separate controllers are designed for the subsystems of the robot. First, an adaptive robust feedback controller with an adaptive feedforward term is designed for the dynamics of the CDPR, constituting the outer-loop dynamics. This controller is robust with respect to the modeling uncertainties of the system. Furthermore, the output of this controller is bounded, which guarantees a saturated desired input for the inner-loop dynamics. Next, a high-gain robust controller is developed for the inner-loop dynamics, which include the actuator–gearbox model. The stability of the overall system is analyzed through a theory of cascaded systems, and it is shown that the system is uniformly practically asymptotically stable. Finally, the effectiveness of the proposed control scheme is validated through simulations on a 4-cable planar robot in both nominal and perturbed conditions.

Optimisation of the synchronisation of a class of chaotic systems: combination of sliding mode and feedback control

This paper addresses the problem of optimisation of the synchronisation for a class of uncertain chaotic systems from a control theoretic point of view. A robust adaptive feedback which accomplishes the synchronisation of chaotic systems using an optimal tuning scheme based on Riccatti equations is successfully adapted. The underlying idea is to optimise the synchronisation of chaotic systems by accounting the control effort despite the uncertainties. The approach developed considers incomplete state measurements and no detailed model of the systems to guarantee robust stability. This approach includes a high-order sliding mode estimator and leads to a robust adaptive feedback control scheme. A finite horizon can be arbitrarily established by ensuring that the chaos synchronisation is achieved at established time. An advantage is that the studied scheme accounts the energy wasted by the controller and the closed-loop performance on synchronisation. Both mathematic proof and numerical simulations are presented o show the feasibility of the optimisation strategy for establishing the synchronisation of chaotic systems even if there are some modelling mismatches and parametric variations.

High-order sliding-mode observer based output feedback adaptive robust control of a launching platform with backstepping

ABSTRACTA kind of launching platform driven by two permanent magnet synchronous motor (PMSM) motors which is used to launch kinetic load to hit the target, always faces strong parameter uncertainties and strong external disturbance such as the air current impulsion, which would degrade their tracking accuracy greatly. In this paper, an adaptive robust nonlinear controller is proposed for high-accuracy motion control of the launching platform, in which the adaption law is designed to estimate the unknown coupling coefficients of torque disturbance and feed-forward cancellation technique is used to compensate the coupling torque disturbance and some other constant disturbances. In addition, a nonlinear robust feedback term is designed to inhibit the influence of the parameter estimation error and the other model uncertainty to stabilise the closed-loop system. Considering that some system states are immeasurable due to cost-reduction, volume/weight limitations and structure restriction or heavy measurement noise is usually associated with the measurements, which may also deteriorate the achievable performance of full-state feedback controllers; a high-order sliding-mode observer is used to estimate the unmeasured system states, and it is synthesised with the adaptive robust controller via feed-forward cancellation method. The intermediary virtual control law and the final control law are derived by integrating the backstepping method. Furthermore, the controller theoretically guarantees a prescribed tracking transient performance and final tracking accuracy while achieving asymptotic tracking performance in the presence of parametric uncertainties only, which is very important for high-accuracy tracking control of launching platform. Extensive comparative experimental results are obtained to verify the high performance nature of the proposed control strategy.

Robustly adaptive synchronization for stochastic Markovian neural networks of neutral type with mixed mode-dependent delays

A robust adaptive feedback controller is designed to achieve complete synchronization of a class of neutral-type stochastic Markovian jump neural networks with mode-dependent and mixed delays. The mixed delays consist of time-varying discrete delays and continuously distributed delays. By employing Wirtinger-based integral inequality, a reciprocally convex combination technique and a generalized LaSalle-type invariance principle for stochastic Markovian differential delay equations, this paper establishes two globally almost surely asymptotical stability conditions of the error dynamical system in the mean-square sense for all admissible uncertainties. An example and its numerical simulation are given to demonstrate the effectiveness of the theoretical results.

Constraint robust adaptive stabilization for a class of lower triangular systems with unknown control direction

This paper studies the constrained robust adaptive stabilization problem for a class of lower triangular systems with unknown control direction. A robust adaptive feedback control law for the systems is proposed by incorporating the technique of Barrier Lyapunov Function with Nussbaum gain. Such a controlled system arises from the study of the constrained robust output regulation problem for a class of output feedback systems with the unknown control direction and a nonlinear exosystem. An application of the constrained robust adaptive stabilization design leads to the solution of the constrained robust output regulation problem in the sense that the output tracking error is constrained within the prescribed barrier limit while asymptotically approaching to zero and the closed loop signals are all bounded for all the time. A numerical example is provided to illustrate the performance of the proposed control.

Robust Adaptive Exponential Synchronization of Stochastic Perturbed Chaotic Delayed Neural Networks with Parametric Uncertainties

This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI) approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs). The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.

Voltage Regulation of a Boost Converter in Discontinuous Conduction Mode: A Simple Robust Adaptive Feedback Controller

Ideal switches in power converters are typically implemented using unidirectional semiconductor devices that may lead to a new operation mode generically called discontinuous conduction mode (DCM). The DCM arises when the ripple, that is, sustained oscillations of small amplitude, is large enough to cause the polarity of the signal (current or voltage) applied to the switch to reverse. Due to the presence of diodes, switches are assumed to operate unidirectionally, but in DCM this unidirectionality assumption is violated. In classicalconverter topologies, DCM appears very frequently in low load operating modes. More interestingly, to achieve high performance some new converters are purposely designed to operate all the time in DCM [1].

Accommodation of unknown actuator faults using output feedback‐based adaptive robust control

AbstractIn this paper, we solve the problem of output tracking for linear uncertain systems in the presence of unknown actuator failures using discontinuous projection‐based output feedback adaptive robust control (ARC). The faulty actuators are characterized as unknown inputs stuck at unknown values experiencing bounded disturbance and actuators losing effectiveness at unknown instants of time. Many existing techniques to solve this problem use model reference adaptive control (MRAC), which may not be well suited for handling various disturbances and modeling errors inherent to any realistic system model. Robust control‐based fault‐tolerant schemes have guaranteed transient performance and are capable of dealing with modeling errors to certain degrees. But, the steady‐state tracking accuracy of robust controllers, e.g. sliding mode controller, is limited. In comparison, the backstepping‐based output feedback adaptive robust fault‐tolerant control (ARFTC) strategy presented here can effectively deal with such uncertainties and overcome the drawbacks of individual adaptive and robust controls. Comparative simulation studies are performed on a linearized Boeing 747 model, which shows the effectiveness of the proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.

Internet Traffic Control Using Dynamic Neural Networks

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

Output Feedback Adaptive Robust Control of Uncertain Linear Systems With Disturbances

Abstract In this paper, a discontinuous projection based output feedback adaptive robust control (ARC) scheme is constructed for a class of linear systems subjected to both parametric uncertainties and disturbances that might be output dependent. An observer is first designed to provide exponentially convergent estimates of the unmeasured states. This observer has an extended filter structure so that on-line parameter adaptation can be utilized to reduce the effect of possible large disturbances that have known shapes but unknown amplitudes. Estimation errors that come from initial state estimates and uncompensated disturbances are dealt with via certain robust feedback at each step of the backstepping design. Compared to other existing output feedback robust adaptive control schemes, the proposed method explicitly takes into account the effect of disturbances and uses both parameter adaptation and robust feedback to attenuate their effects for an improved tracking performance. Experimental results on the control of an iron core linear motor are presented to illustrate the effectiveness and achievable performance of the proposed scheme.