Indirect Model Reference Adaptive Control Research Articles

Convex combination of multiple models for discrete-time adaptive control

The principal drawback of traditional adaptive systems is the possibility of rather poor transient response. In this regard, the multiple-model methodology has been beneficial over the past couple of decades. However, the application of this methodology requires proven indirect model reference adaptive control. Such a gap exists in the case of discrete-time linear time-invariant systems with unknown parameters. The principal contribution of this paper is to fill this void by arriving at proof of global stability of discrete-time adaptive systems that use the prediction error rather than the control error to update the parameters of the prediction model. This proof is based on the theory of Lyapunov and the properties of square-summable sequences. An analysis of the available literature reveals that such a result is not available even for linear time-invariant systems. Therefore, the proof is first provided for adaptive systems with a single prediction model wherein the transfer function of the plant has an arbitrary relative degree. Subsequently, this proof is extended to the case of multiple prediction models in the context of multiple-model methodology with second-level adaptation. Two parameter update algorithms are considered. Simulation studies are included to demonstrate the improvement in transient performance.

Comparative analysis of Direct and Indirect Model Reference Adaptive Control by Extended Kalman Filter

Considerations about the increasing complexity of technological systems have stimulated the interest in hybrid systems that can successfully manage switching behaviour or approach nonlinearity. Hybrid systems are made up of two parts: a constant dynamics component and a switching mechanism. This article investigates the effectiveness of direct and indirect model adaptive control approaches for any common tool for hybrid modelling and approximation nonlinear systems. A reference model may be linear or partially refined, specifies the desired loop system behavior that the adaptive controllers are capable of achieving in the face of unknown system dynamics regardless of the system dynamics. Individual control gains are obtained for each subsystem and it is also carefully tuned to the altered behavior of each system. Through the application of dynamic gain adjustment, singularities in the principle of certainty equivalence are avoided indirectly. The state of the reference model is asymptotically monitored for both techniques by assuming that a shared Lyapunov feature is available for the switched reference model.

A Novel Non-integer Indirect Adaptive Control for Non-integer Order Systems with Non-prior Knowledge

In this study, a new non-integer indirect adaptive control method with reference model is suggested for the class of non-integer order systems. The objective of model reference control is to include the output of the given reference fractional model in tracking the output of a controlled plant by using the concept of on-line goal adaptation. The stability of the closed-loop system is analyzed via the Lyapunov method. Finally, Matlab simulation results are presented to illustrate the effectiveness of the proposed method of indirect fractional model reference adaptive control.

Indirect Model Reference Adaptive Control of Piecewise Affine Systems with Concurrent Learning

Abstract In this paper, we propose a concurrent learning-based indirect model reference adaptive control approach for multivariable piecewise affine systems as an enhancement of our previous work. The main advantage of the concurrent learning-based approach is that the linear independence condition of the recorded data suffices for the convergence of the estimated system parameters. The classical persistent excitation assumption of the input signal is not required. Moreover, it is proved that the closed-loop system is stable even when the system enters the sliding mode. The numerical example shows that the concurrent learning-based approach exhibits better tracking performance and achieves parameter convergence when compared with our previously proposed approach.

Path-guided time-varying formation control with collision avoidance and connectivity preservation of under-actuated autonomous surface vehicles subject to unknown input gains

This paper investigates the distributed time-varying formation control for a swarm of under-actuated autonomous surface vehicles subject to unknown input gains, in addition to model uncertainties and ocean disturbances. The fleet is required to follow a parameterized path with a time-varying formation while avoiding collisions and maintaining connectivity at a complex sea environment. At the kinematic level, a distributed guidance control law is proposed based on a consensus approach, a path-following design, artificial potential functions, and an auxiliary variable approach. At the kinetic level, an adaptive kinetic control law is designed based on an indirect model reference adaptive control approach where a neural estimator is developed for identifying unknown input gains, model uncertainties and ocean disturbances. The stability of the closed-loop system is proven via cascade stability analysis. The advantage of the proposed method is three-folds. First, the developed time-varying formation controller is able to achieve various coordinated behaviors such as cooperative target tracking and target enclosing. Second, the proposed time-varying formation controller is robust against model uncertainties, ocean disturbances and unknown input gains. Third, the proposed distributed controller holds the capability of collision avoidance and connectivity preservation. Simulation results substantiate the effectiveness of the proposed path-guided time-varying formation controllers.

Design of L1 Adaptive Control for PMSG Wind Turbine System

This paper focuses on the design of an L1 adaptive controller for variable speed wind turbines on a permanent magnet synchronous generator (PMSG) to maximize the wind power exploited in the presence of parameters uncertainties and wind speed turbulences. This control method based on the class of state feedback state for multiinput multi-output (MIMO) system can be applied to the current control and speed control of the system. The proposed controller successfully deals with the essential nonlinear problems of wind turbines and the effect of unknown parameters. Moreover, the L1 adaptive controller with fast adaptation is not only beneficial for both performance and robustness but also effective for the appearance of the time-delay state variables of wind turbine. Finally, with comparison to the indirect model reference adaptive control (MRAC), the performances of the system are verified by mean of several simulation results.

Adaptive Boost Pressure Control for Four-Stroke Diesel Engine Marine Application in the Presence of Dynamic Uncertainties

Robustness of the control system in marine engines with respect to its time-varying dynamics has recently become an important research topic. The variation of dynamics, caused primarily by the mechanical wear of components or their faults originating from tough operating condition, can lead to the overall control system instability (or marginal stability, depending on how severe the variation is). The biggest issue is that the marine engines cannot always be fixed fast enough (especially on large cargo vessels, spending hundreds of hours cruising). In this paper, a control adaptation (indirect model-reference adaptive control) is proposed to deal with the engine parameter variation in a way that the original system response is preserved. The stability and robustness of the proposed control system are studied by means of numerical simulations. Finally, the performance of the control concept is validated on the medium-speed diesel engine test bed at constant speed under load transients. Superior performance compared with fixed-parameter proportional–integral control is demonstrated.

Direct and Indirect Model Reference Adaptive Control for Multivariable Piecewise Affine Systems

This article proposes direct and indirect model reference adaptive control strategies for multivariable piecewise affine systems, which constitute a popular tool to model hybrid systems and approximate nonlinear systems. A chosen reference model, which can be linear or also piecewise affine, describes the desired closed-loop system behavior that is to be achieved by the adaptive controllers for unknown system dynamics. Each subsystem acquires its own set of control gains, which is tuned under careful consideration of the switching behavior. In the indirect approach, the use of dynamic gain adjustment avoids singularities in the certainty equivalence principle. It is shown for both algorithms that the state of the reference model is tracked asymptotically given a common Lyapunov function for the switched reference model is available. Furthermore, parameter convergence in both the direct and indirect approach is proven for sufficiently rich reference signals. Finally, both algorithms are evaluated in numerical simulations and their advantages and disadvantages are discussed.

Combined Fractional Adaptive Control * *The results reported in this paper have been financed by CONICYT- Chile, under the Basal Financing Program FB0809 "Ad-vanced Mining Technology Center", FONDECYT Project 1150488, "Fractional Error Models in Adaptive Control and Applications"; and FONDECYT 3150007, Postdoctoral Program 2015".

This paper presents the design of a fractional order combined model reference adaptive control scheme. This approach is based on the estimates of the plant unknown parameters and the adjusted controller parameters, which are obtained from adaptive laws using the identification error, the control error and the so called closed loop estimation errors. Fractional order indirect algebraic model reference adaptive control and fractional order indirect dynamic model reference adaptive control schemes are also proposed in this paper, which are used for comparison purposes with the combined approach. Boundedness of all the signals in the control schemes is analytically proved, as well as the convergence to zero of the mean values of the control, identification and closed loop estimation errors. Simulation results are presented to show the effectiveness of the proposed control strategies.

Transient performance improvement in indirect model reference adaptive control using perturbation‐based extremum seeking identifier

AbstractOne of the main drawbacks of model reference adaptive control (MRAC) is the weakness of its transient performance. The key reason of this imperfection is parameter's estimation error convergence. For many cases in the closed‐loop control, the plant input signal cannot satisfy the persistence of excitation (PE) condition which yields poor parameters estimation error convergence. In this paper, we use a fast perturbation‐based extremum seeking (PES) scheme without steady‐state oscillation as the parameter identifier in indirect MRAC. The estimated parameters through the PES identifier contain the additive sinusoidal signals with distinct frequencies in the transient, which satisfy the PE condition of the plant input. Therefore, convergence of the parameters estimation error to zero will be guaranteed that results in improvement of transient performance for indirect MRAC. Also, the contrary effects on the steady‐state behaviour is eliminated since the sinusoidal excitation signals amplitude exponentially converge to zero and reinitiate with every change in the unknown parameters. Simulation results for a second order example have been presented to illustrate the effectiveness of the proposed scheme.

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

Indirect model reference adaptive control for a class of fractional order systems

This article focuses on the indirect model reference adaptive control problem for fractional order systems. A constrained gradient estimation method was established firstly, since parameter estimation is part and parcel of the whole control problem. Then a novel adaptive control law is designed, from which the two problems, i.e., parameter estimation and reference tracking, can be unified perfectly. On these basis, an effective control scheme is established. The stability of the resulting closed-loop system is analyzed rigorously via indirect Lyapunov method and frequency distributed model. Finally, a careful simulation study is reported to illustrate the effectiveness of the proposed scheme.

Stability Analysis of Indirect Binary Model Reference Adaptive Controller for Plants with Relative Degree One

This paper presents the Lyapunov-based stability analysis of the Indirect Binary Model Reference Adaptive Controller (IB-MRAC) for relative-degree-one systems. The motivation for indirect adaptive control approaches relies on their capabilities of providing an easier and more intuitive controller design. IB-MRAC is a mixed controller, acting as an Indirect MRAC or as an Indirect Variable Structure MRAC depending on a design parameter. The main result herein described states that the overall system error tends exponentially fast to some small residual set.

Adaptive control of bivalirudin in the cardiac intensive care unit.

Bivalirudin is a direct thrombin inhibitor used in the cardiac intensive care unit when heparin is contraindicated due to heparin-induced thrombocytopenia. Since it is not a commonly used drug, clinical experience with its dosing is sparse. In earlier work [1], we developed a dynamic system model that accurately predicts the effect of bivalirudin given dosage over time and patient physiological characteristics. This paper develops adaptive dosage controllers that regulate its effect to desired levels. To that end, and in the case that bivalirudin model parameters are available, we develop a Model Reference Control law. In the case that model parameters are unknown, an indirect Model Reference Adaptive Control scheme is applied to estimate model parameters first and then adapt the controller. Alternatively, direct Model Reference Adaptive Control is applied to adapt the controller directly without estimating model parameters first. Our algorithms are validated using actual patient data from a large hospital in the Boston area.

A discrete‐time indirect adaptive multiple‐model actuator failure compensation scheme

SummaryA new discrete‐time actuator failure compensation control scheme is developed, using a multiple‐model adaptive control approach which has the capacity to achieve faster and more accurate compensation of failure uncertainties. An individual adaptive system, for each possible failure pattern in a failure pattern set of interest for compensation, is designed using an indirect model reference adaptive control scheme for actuator failure compensation. A multiple‐model control switching mechanism for discrete‐time systems is set up by finding the minimal performance index to select the most appropriate control law. The performance indices are based on the adaptive estimation errors of individual parameterized systems with actuator failures. Simulation results from an aircraft flight control system example are presented to show the desired closed‐loop system stability and tracking performance despite the presence of uncertain actuator failures. Copyright © 2014 John Wiley & Sons, Ltd.

Adaptive Control of Bivalirudin in the Cardiac Intensive Care Unit

Bivalirudin is a direct thrombin inhibitor used in the cardiac intensive care unit in patients who develop an allergic reaction to heparin. Since it is not a commonly used drug, clinical experience with its dosing is sparse. In earlier work (Zhao et al. [2014]) we developed a dynamic system model that accurately predicts the effect of bivalirudin when given dosage over time and patient physiological characteristics. This paper develops adaptive dosage controllers that regulate its effect to desired levels. To that end, and in the case that bivalirudin model parameters are available, we develop a Model Reference Control law. In the case that model parameters are unknown, an indirect Model Reference Adaptive Control scheme is applied to estimate model parameters first and then adapt the controller. Our algorithms are validated using actual data from a large hospital in the Boston area.

Smooth Adaptive Robust Temperature Control of a Seed Drying System

This paper addresses the problem of maintaining the temperature precisely tuned inside a seed drying system, in order to avoid thermal damage to that sensible biological entity and thus guarantee the right conditions for storage. This objective is achieved through an adaptive robust technique, known as Shunt Indirect Variable Structure Model Reference Adaptive Control (SIVS-MRAC), which has shown to be insensitive to external disturbances and unmodeled dynamics, although it presents a high frequency switching control signal that might be undesirable or even not compatible with some actuators. Here, besides the low control signal magnitude, control signal smoothness is obtained by using output error dependant linear regions and the equivalent control method. Simulations results are presented comparing the usage of such a technique.

Adaptive Control of Quadrotor UAVs: A Design Trade Study With Flight Evaluations

This brief describes the application of direct and indirect model reference adaptive control to a lightweight low-cost quadrotor unmanned aerial vehicle platform. A baseline trajectory tracking controller is augmented by an adaptive controller. The approach is validated using simulations and flight tested in an indoor test facility. The adaptive controller is found to offer increased robustness to parametric uncertainties. In particular, it is found to be effective in mitigating the effects of a loss-of-thrust anomaly, which may occur due to component failure or physical damage. The design of the adaptive controller is presented, followed by a comparison of flight test results using the existing linear and augmented adaptive controllers.

The proportional derivative indirect adaptive control

The presented paper proposes novel method of Indirect Model Reference Adaptive Control. Thanks to a recent technique called Immersion & Invariance, the parameter adjustment is run with the proportional and derivative term. The former is a standard in indirect adaptive control. The latter is introduced by authors and it provides an enhancement of the transients. Furthermore, it is shown that the proposed control scheme supports the multi model methodology. Illustrative examples are calculated to show the properties of the new control method.

Adaptive Systems with Closed–loop Reference Models: Composite control and observer feedback

A class of Closed-loop Reference Models (CRM) was shown in Gibson et al. (2013) to have improved transient performance. In this paper, we show that the introduction of CRM in Combined direct and indirect Model Reference Adaptive Control (CMRAC) leads to significant improvement in their transient response as well. We also show that CRM allow stable feedback of noise-free state estimates in CMRAC. Theoretical derivations are supported with numerical simulations.