Direct Model Reference Adaptive Control Research Articles

Autonomous Control of a Hybrid Rolling and Flying Caged Drone for Leak Detection in HVAC Ducts

In this article, a hybrid, rolling, and flying multirotor caged drone is developed for inspection and leak detection of heating, ventilation, and air conditioning (HVAC) ducts. The developed passive-cage drone is designed, manufactured, and controlled autonomously using direct model reference adaptive control (DMRAC). The platform is equipped with a 2-D LIDAR sensor that provides a scan of the surroundings in a plane. Possible leaks are detected using a thermal camera. DMRAC is used for higher level roll, pitch, and yaw control, while lower level motor speed control is achieved through Proportional-Integral-Derivative (PID) controllers. This article compares, for the first time, PID-based controllers for roll, pitch, and yaw control, with standard DMRAC and DMRAC with gain modifications, in a cascaded setting on a hybrid passive-caged drone. Several hardware tests are used to highlight the differences in the control performance when PID controllers/DMRAC with certain gain modifications are used to control the developed hybrid passive-caged drone.

Comparison Analysis Between PI and Adaptive Controllers for DC-DC Converter of Hybrid Energy Storage Systems in Electric Vehicles

A power converter is one of the important components in a hybrid electric vehicle (HEV), where it has a strong nonlinear dynamic due to the variation of load demand from different driving modes, namely acceleration, braking and cruising. To adapt with the nonlinearities, this work proposes the use of direct model reference adaptive control (DMRAC) to regulate its operation in tracking the load and current demand of the HEV. To validate the response, the control performance is benchmarked with the commonly used traditional PI controller. The system model includes a battery with a supercapacitor, and its controller was constructed using the MATLAB Simulink platform. Simulation results show that DMRAC provides better performance as compared to the PI controller in two cases, which are tracking the current and load demands according to the root mean square error (RMSE) analysis. Nevertheless, in the presence of disturbance, it is noted that DMRAC is only effective in tracking the current demand while requiring some time to adapt and surpass the PI controller in tracking the load demand. Based on these findings, it can be justified that the DMRAC has the potential to become a good alternative approach to control the HEV power converters, specifically in the presence of disturbance.

Robust adaptive quadrotor position tracking control for uncertain and fault conditions

The combination of nonlinear indirect and direct adaptive control with an active fault-tolerant framework is proposed in this paper for quadrotor position tracking control under uncertain and fault conditions. An inner loop direct model reference adaptive controller generates the required force while moving along the reference trajectory, while nonlinear indirect adaptive controller maintains the required attitude angles. Furthermore, for uncertain conditions, our proposed framework provides significantly stable characteristics. Otherwise, when a fault occurs at actuators or sensors, the quadrotor vehicle cannot guarantee global asymptotic stability. This study contributes to an active fault-tolerant control strategy for solving the complex position tracking problem using the adaptive two-stage Kalman filter (ATSKF). Based on the fault information, a fault compensation term is added to the control law to improve convergence and system robustness in the presence of uncertain and fault conditions. Finally, simulation results show satisfactory performance for quadrotor vehicle position tracking, even with actuator faults and uncertainties.

Model reference adaptive controllers with improved performance for applications in LCL-filtered grid-connected converters

This work presents a control strategy combining a direct Model Reference Adaptive Control (MRAC) with a robust state feedback for current control of grid-connected converters with LCL filters operating under grid impedance variations. The MRAC is based on a gradient law, providing properly current reference tracking and voltage disturbance rejection. The inclusion of a state feedback inner loop provides robust active damping of the filter resonance for an entire range of grid impedances. A systematic control design procedure is proposed, and the combination of both control actions in a multi-loop approach does not add significant computational complexity. The proposal allows to increase the MRAC gains adaptation dynamics, providing suitable responses under grid current reference variations and under typical grid voltage disturbances, including grid faults. Performance indexes as the grid-currents total harmonic distortion and the mean squared error are also improved. In addition, robust stability analysis based on Lyapunov functions are carried out for the inner control loop and for the outer control loop, considering the grid impedance uncertain and with unmodeled dynamics. The strategy is implemented in a commercial digital signal processor, and the compliance of the grid currents with the IEEE 1547 Std. is verified with controller hardware-in-the-loop and experimental results in a 5.4 kW prototype.

Pitch Angle Control of an Airplane Using Fractional Order Direct Model Reference Adaptive Controllers

This paper deals with the longitudinal movement control of an airplane (pitch angle) using fractional order adaptive controllers (FOACs). It shows the improvements achieved in the plane’s behavior, in terms of the minimization of a given performance index. At the same time, less control effort is needed to accomplish the control objectives compared with the classic integer order adaptive controllers (IOACs). In this study, fractional order direct model reference adaptive control (FO-DMRAC) is implemented at the simulation level, and exhibits a better performance compared with the classic integer order (IO) version of the DMRAC (IO-DMRAC). It is also shown that the proposed control strategy for FO-DMRAC reduces the resultant system control structure down to a relative degree 2 system, for which the control implementation is simpler and avoids oscillations during the transient period. Moreover, it is interesting to note that this is the first time that an FOAC with fractional adaptive laws is applied to the longitudinal control of an airplane. A suitable model for the longitudinal movement of the airplane related to the pitch angle θ as the output variable with the lifter angle (δe) as the control variable, is first analyzed and discussed to obtain a reliable mathematical model of the plane. All of the other input variables acting on the plane are considered as perturbations. For certain operating conditions defined by the flight conditions, an FO-DMRAC is designed, simulated, and analyzed. Furthermore, a comparison with the implementation of the classical adaptive general direct control (relative degree ≥ 2) is presented. The boundedness and convergence of all of the signals are theoretically proven based on the new Lemma 3, assuring the boundedness of all internal signals ω(t).

Exponentially Stable Adaptive Control of MIMO Systems with Unknown Control Matrix

The scope of this research is a problem of direct model reference adaptive control of linear time-invariant multi-input multi-output (MIMO) plants without any a priori knowledge about system matrices. To handle it, a new method is proposed, which includes three main stages. Firstly, using the well-known DREM procedure, the plant parametrization is made to obtain the linear regressions, in which the plant matrices and state initial conditions are the unknown parameters. Secondly, such regressions are substituted into the known equations for the controller parameters calculation. Thirdly, the controller parameters are identified using the novel adaptive law with the exponential rate of convergence. To the best of the authors’ knowledge, such a method is the first one to provide the following features simultaneously: 1) it is applicable for the unknown MIMO systems (e.g. without any information about state or control allocation matrices, the sign of the latter, etc.); 2) it guarantees the exponential convergence of both the parameter and tracking errors under the mild requirement of the regressor finite excitation; 3) it ensures element-wise monotonicity of the transient curves of the control law parameters matrices. The results of the conducted experiments with the model of a rubber and ailerons control of a small passenger aircraft corroborate all theoretical results.

Direct adaptive control of spacecraft near asteroids

An orbit–attitude, direct model reference adaptive control methodology is developed and applied to spacecraft trajectory tracking in the vicinity of asteroid with dynamical coupling. The asteroid gravitational field is modeled using a polyhedral gravity model, and the asteroid is assumed to be rotating. Orbit–attitude dynamical coupling due to solar radiation pressure and gravity gradient is implemented. Spacecraft and asteroid parameters such as mass, inertia, and gravitational field are assumed to be unknown. A direct adaptive controller with e modification is implemented to track hovering and orbital trajectories in the vicinity of the asteroid. The direct model reference adaptive controller does not require parameter estimation and is robust to model and disturbance uncertainties. The controller is designed separately for orbit and attitude case. A Lyapunov analysis is presented to prove the asymptotic stability of the orbit–attitude adaptive control system. A numerical simulation for asteroid Kleopatra is presented with orbital and hovering tracking maneuvers. The results show that the adaptive controller is able to successfully track trajectories in the vicinity of the asteroid without assuming system parameters.

Direct Model Reference Adaptive Control of a Boost Converter for Voltage Regulation in Microgrids

In this study, we present a Direct Model Reference Adaptive Control (DMRAC) algorithm in a boost converter used in islanded microgrids (MG) with a solar photovoltaic (PV) system. Islanded types of microgrids have very sensitive voltage and frequency variability; therefore, a robust and adaptive controller is always desired to control such variations within the MG. A DC–DC boost converter with a modified MIT rule controller is proposed in this paper, which stabilizes output voltage variations in islanded MG. Since the boost converter is a non-minimum phase, the controller design that relies only on output voltage feedback becomes challenging. Even though output voltage control can be achieved using inductor current control, such current mode controllers may also require prior knowledge of the load resistance and more states, such as output and inductor currents in feedback. Here, two control loops are used to achieve a stable output voltage; a PID controller can regulate the output voltage at a fixed level, and the outer loop is designed to implement the MIT rule for a DMRAC. To ensure that the actual system is following the desired reference model, using only an output voltage feedback sensor, a DMRAC is devised to update the PID controller parameters in real-time. Compared to a DC–DC boost converter connected to the MG, a controller, such as the one introduced in this paper, is more successful in dealing with unknown parameter fluctuations and disturbance changes. The MATLAB/SIMULINK is used to design and simulate the controller with different load disturbances and input voltage variances. The hardware validation is also carried out to show the performance of the proposed controller. Our results suggest that the DMRAC provides robust regulation against parameter variations.

Output Feedback Model Reference Adaptive Control of Piecewise Affine Systems With Parameter Convergence Analysis

In this article, the output feedback-based direct model reference adaptive control of piecewise affine systems and its parameter convergence are investigated. Under the slow switching assumption, it is shown that all the closed-loop signals are bounded and the output tracking error is small in the mean square sense. Built upon this result, the estimation error of controller parameters is proved to converge to a residual set if the input signal is sufficiently rich. The relationship between the size of this residual set and the switching frequency is established. Moreover, the convergence of the estimated controller parameters to their nominal values can be achieved for a certain subsystem given that this subsystem is activated for infinitely long time. Simulation results validate the effectiveness of the proposed approach.

Parameter Estimation and Adaptive Control of Super-coiled Polymer Artificial Muscles

Super-coiled polymer (SCP) artificial muscles demonstrate desirable properties such as high-power density, compliance, and low-cost. However, their performance is dependent on the ambient environment. It is necessary but difficult to re-identify key SCP parameters to maintain desired performance. In this work, an adaptive parameter estimation method is proposed to identify unknown SCP parameters. The SCP model identification demonstrates parameter convergence which indicates that SCPs could be controlled effectively and used in environments with variations. We further develop a control law using direct model reference adaptive control (MRAC) on the output position of the SCP actuator. The MRAC performance is compared to a proportional-integral (PI) and proportional-derivative (PD) with feedforward controllers to demonstrate the ability to reduce tracking errors. The MRAC showed superior performance in comparison to the two controllers when the SCP actuator parameters were unknown.

Direct Model-Reference Adaptive Control for Wheelchair Simulator Control via a Haptic Interface

A wheelchair locomotion simulator (WCS) is an innovative solution to assess the biomechanical cost of wheelchairs (WC) accessibility in a controlled and safe virtual environment. In this context, this paper presents a haptic feedback control architecture based on a direct model reference adaptive control (MRAC) with intelligent tuning of its adaptation gains. The control objective is to follow the reference model velocity while producing the force feedback during the push phase, in order to faithfully recreate the dynamic behavior of the WC in a virtual environment. To accomplish this, a wheelchair ergometer model with friction is used to provide realistic navigation in the virtual environment (VE), by detecting and driving the wheelchair wheels. A two-wheeled vehicle model including the rolling resistance aspect is used to describe the wheelchair dynamic behavior. Since the controller adaptation gains are operated on the tracking error between the reference model and the simulator output, the WC model is also used as a reference model to specify the desired dynamics of the adaptive control system. For an optimal solution, an intelligent metaheuristic algorithm Elephant Herding Optimization (EHO) is employed to optimize the controller gain adaptation parameter to keep the tracking error as small as possible. Finally, the simulation results obtained show the effectiveness of the proposed control strategy.

Performance Analysis of PI and DMRAC Algorithm in Buck–Boost Converter for Voltage Tracking in Electric Vehicle Using Simulation

This study introduces a Direct Model Reference Adaptive Control (DMRAC) algorithm in a buck–boost converter in the power distribution of an electric vehicle. In this study, DMRAC was used in order to overcome the system nonlinearity due to load demand variation, in case of different driving modes (such as acceleration, stable and regenerative braking system mode), and the presence of disturbances in the system. DMRAC receives popularity because of its robustness in the presence of nonlinearity and ensuring system stability. To evaluate the efficacy of DMRAC in the current system, its performance was compared with a PI controller in the MATLAB/Simulink environment. The simulation results show the superiority of DMRAC over a conventional PI control approach, in both variable load demand and disturbed system cases that were measured by tracking error. The improvement was seen in the DMRAC response, with smaller tracking error and faster transient and disturbance rejection. The main contribution of this work is in introducing DMRAC, particularly in a buck–boost converter, and its efficacy with a DC–DC converter for an electric vehicle, which has not been studied before.

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.

Adaptive tracking of uncertain nonlinear systems under different types of input delays with urban traffic perimeter control application

AbstractPragmatic design method of direct model reference adaptive control (MRAC) is developed for a class of uncertain systems, where various input channels might have different delays and taking into account the effect of control saturation. To overcome the difficulty of directly predict the plant state and cope with different kinds of input delays, the proposed control design relies on implicit time‐delay prediction by using auxiliary linear Smith‐like dynamic units with adjustable gains. The design is unified to different time delay categories, that is, constant, time‐varying, or dependent on the current plant state. Within the context of the proposed approach, various adaptive control feedback strategies are designed for problems of continuous and sample‐and‐hold control implementations. Simulation results of a perimeter control for two interacting aggregate urban traffic regions are presented.

Distributed model reference adaptive containment control of heterogeneous multi-agent systems with unknown uncertainties and directed topologies

In this paper, the containment control problem of heterogeneous uncertain high-order linear Multi-Agent Systems (MASs) is addressed and solved via a novel fully-Distributed Model Reference Adaptive Control (DMRAC) approach, where each follower computes its adaptive control action on the basis of local measurements, information shared with neighbors (within the communication range) and the matching errors w.r.t. its own reference model, without requiring any previous knowledge of the global directed communication topology structure. The approach inherits the robustness of the direct model reference adaptive control (MRAC) scheme and allows all agents converging towards the convex hull spanned by leaders while fulfilling at the same time local additional performance requirements at single-agent level, such as prescribed settling time, overshoot, etc. The asymptotic stability of the whole closed-loop network is analytically derived by exploiting the Lyapunov theory and the Barbalat lemma, hence proving that each follower converges to the convex hull spanned by the leaders, as well as the boundedness of the adaptive gains. Extensive numerical analysis for heterogeneous MAS composed of stable, unstable and oscillating agent dynamics are presented to validate the theoretical framework and to confirm the effectiveness of the proposed approach.

Improvement of transient performance in MRAC by memory regressor extension

The paper addresses the problem of transient performance improvement of direct model reference adaptive control (MRAC) of discrete linear time-invariant (LTI) plants. Two solutions to the problem are proposed and use the certainty equivalence principle and the idea of regressor recording over a past period of time. The first solution is based on the principle of augmented error, while the second one uses new scheme of high order tuner generating predicted values of adjustable parameters. The recording of the past regressor is provided by application of a special SISO filter (linear operators with “memory”) to the closed-loop error model. It is proven and demonstrated by numerical examples that the proposed solutions can provide asymptotic (not exponential) convergence of the adjustable parameters under some simple condition which is weaker than the persistent excitation one. The proposed solution can be considered as a generalization of the identification/adaptation algorithm proposed by Kreisselmeier for continuous systems [21, 22].

Robust output voltage control of a high gain DC–DC converter under applied load and input voltage uncertainties

In this study, using robust and adaptive approaches, two controllers are designed to compensate for the applied load and input voltage uncertainties of a high voltage gain dc–dc converter. These controllers are designed based on measuring only the output voltage of the converter. The permissible ranges of the uncertainties are calculated using dynamics equations analysis while practical limitations are considered. In the first approach, an input multiplicative uncertainty is considered for the model of the converter by linearisation of non-linear equations as well as using permissible ranges of the uncertainty. Then, a robust H∞ controller is designed. In the second approach, the converter model is considered as a first-order dynamics model with the unknown parameters and an unknown additive term while a direct robust model reference adaptive controller is designed for it. Finally, the non-linear equations of the converter are simulated and the performances of the controllers are compared with each other. The control methods are then implemented on a 200 W prototype of a converter for evaluating the simulations results.

Direct fractional order MRAC adaptive control design for a class of fractional order commensurate linear systems

In this paper, we introduce a direct fractional order adaptive control design based on model reference adaptive control (MRAC) structure for a class of commensurate fractional order linear systems with an arbitrary relative degree, and whose parameters are unknown. By generalising the application of standard direct MRAC strategy to plants described by fractional order models, we develop a fractional adaptive control scheme (FOMRAC) based on the output feedback. We also define an adaptation control law ensuring the stability of the closed-loop system and the good tracking of the reference trajectory. The asymptotic stability of the fractional order control system is proven using an extension of the Lyapunov theorem. Simulation results show the effectiveness of the proposed control method even for plants with model parametric variations and additive noises.

Investigation on Physical Meaning of Three-loop Autopilot

This paper aims to reveal the hidden physical meaning or the working principle of the three-loop autopilot. First, the minimal control structure that ensures the desired dynamic characteristics is analyzed. The results show that the three-loop autopilot has a redundant feedback loop from the minimal control structure standpoint. Based on this observation, its physical meaning is interpreted by utilizing the closed-loop tracking error dynamics in the presence of aerodynamic uncertainties. It turns out that the three-loop autopilot has the minimal control structure with an additional command which is based on the instantaneous direct model reference adaptive control. A clear mechanism of how the aerodynamic uncertainties are compensated is provided in the three-loop autopilot. Finally, numerical simulations are performed to validate our findings. The analysis results in this paper show the beauty of the three-loop autopilot: it can be considered as the most concise form to achieve desired dynamic characteristics and counteract model uncertainties. The potential importance of the results obtained is that they allow us to properly design the three-loop autopilot by reflecting its physical meaning and have potential directions to improve the performance of the three-loop autopilot as well as the adaptive control.

Model Reference Adaptive Controller for LTI Systems with Time-variant Delay

In this paper, a new Direct Model Reference Adaptive Control Procedure (DMRAC) for Linear Time-Invariant (LTI) delay systems is presented with the use of the concept of the command generator tracker which expands the class of processes that can now be controlled with zero output error. The stability of the error between the system and the model is guaranteed by the Lyapunov theory. The new algorithm is applied to control a perturbed delay system. Matlab simulation examples are given to demonstrate the usefulness of the algorithm.