Model Reference Adaptive Control Method Research Articles

Singularity-free adaptive control of discrete-time linear systems without prior knowledge of the high-frequency gain

This paper proposes a new output feedback model reference adaptive control (MRAC) method for discrete-time linear time-invariant systems with arbitrary relative degrees. The proposed method does not require any prior knowledge of the high-frequency gain represented by kp, thereby completely eliminating the common design condition in the traditional discrete-time MRAC framework: the sign of kp is known, as well as an upper bound on |kp|. Specifically, an output feedback adaptive control law is developed, which incorporates a time-varying gain function to effectively address the singularity issue. The developed adaptive control law leads to the derivation of a linear estimation error equation for the closed-loop system. Consequently, a gradient algorithm based parameter update law is directly formulated by utilizing the estimation error and some other available signals without requiring any prior knowledge of kp. In comparison to the traditional MRAC and Nussbaum function-based methods, it does not necessitate any additional design conditions or involve any transient performance issues, while still ensuring closed-loop stability and asymptotic output tracking for any given bounded reference signal. The simulation study showcases the design procedure and evaluates the efficacy of the proposed control method.

A new output feedback adaptive control method for fractional order systems with inaccessible state

In this paper, the output tracking control problem for fractional-order (FO) systems with inaccessible state is investigated, and a new fractional-order model reference adaptive control (FOMRAC) method is proposed. The designed FO adaptive update law is able to adjust the controller parameters online in real time according to the system output information. Compared with the classical output feedback FOMRAC method, the proposed FOMRAC strategy only needs the online updating of a scalar function, which significantly reduces the complexity of the controller design and the online computational burden, and can achieve better control effect. Furthermore, the scheme ensures the boundedness of the closed-loop signals and that the mean value of the squared norm of the output error converges to zero. Finally, simulation results are carried out to verify the effectiveness of the established approach.

Self-organizing model reference adaptive control for aircraft with enhanced persistent excitation

Model Reference Adaptive Control (MRAC) methods are commonly applied in aerospace control systems, yet they face challenges in terms of persistent excitation, adaptation speed, and network structure design. To tackle these issues, this study introduces a novel MRAC method that employs practical techniques, including fast adaptation, experience replay, and network self-organization. Specifically, this study emphasizes three key contributions. First, it introduces a modified MRAC architecture that incorporates a fast adaptation term and a network approximation term to compensate for control errors more effectively. The fast adaptation term, which exhibits higher adaptive speed, enables quick response to dynamic variations and is stored in an experience replay buffer. Additionally, the network term is trained using batches of past samples extracted from the experience replay buffer, rather than relying solely on the current sample. This approach successfully overcomes challenges related to achieving persistent excitation and setting adaptation speed. Secondly, this paper introduces an online method for network self-organization to approximate a global model. This approach autonomously selects basis functions to enhance the organization of the network, leading to improved matching and overall performance. Finally, theoretical proofs and numerical simulations are presented to validate the effectiveness of the proposed techniques and demonstrate the advantages of the method in terms of parameter convergence, learning speed, and control stability.

Control & synchronization of a unified chaotic system using an adaptive controller with an extended Kalman–Bucy-filter based Auto-Tuner

A current challenge with adaptive controllers is to define efficient tuning methods of the controller parameters. Unlike linear systems, nonlinear systems may need parameters that are continuously tuned at different operating points to provide stability and desired behaviours. This study aims to develop a solution for tuning proportional–integral–derivative (PID) controller parameters as opposed to changing the operating points of a nonlinear system. Most tuning methods calculate parameters according to the system’s step or frequency response. However, adaptive controllers have self-tuneable parameters or control rules. The proposed algorithm in this paper contains a controller, an estimator, and a reference model, and uses the system model. Unlike the model reference adaptive control method, the proposed controller has tuneable controller parameters estimated by the extended Kalman–Bucy filter. The filter estimates the controller parameters to make the system perform like the auxiliary ideal reference model to ensure minimum-time consumption. Hence, this study aims to develop an algorithm that will automatically calculate controller parameters for each operating point of the controlled chaotic or nonlinear system to minimize settling time at each operating point. The proposed algorithm is implemented in a unified chaotic system in which the estimator and controller of the system run together. Simulation results confirm the performance of the proposed algorithm. In addition, the simulation results provide strong evidence that the proposed algorithm can be an effective tool for controlling nonlinear or chaotic systems.

Impedance Force Control of Manipulator Based on Variable Universe Fuzzy Control

Impedance control is a classic and straightforward control method that finds wide applications in various fields. However, traditional constant impedance control requires prior knowledge of the environment’s stiffness and position information. If the environmental information is unknown, constant impedance control is not capable of handling the task. To address this, this paper proposes a variable universe fuzzy model reference adaptive impedance control method that achieves effective force tracking even in the presence of unknown environmental information. A variable universe fuzzy controller was employed to determine the impedance parameters. The force tracking error and its rate of change were used as two input parameters for the variable universe fuzzy controller, which utilizes fuzzy inference to obtain the incremental values of the impedance parameters. For the introduced model reference controller, a novel adaptive law was employed to obtain the coefficients for contact force and torque. Subsequently, the contact force of the manipulator in Cartesian space was taken as the research object, and a simulation model of a six-joint manipulator was established in MATLAB/Simulink. By comparing it with the constant impedance control method, the feasibility and effectiveness of this control approach were validated.

Control of a jet engine using predictor-based adaptive strategy in different flight modes

In this article, a predictor-based model reference adaptive control method is proposed for a JetCat SPT5 turboshaft engine in full thrust, cruise, and idle modes. In the predictor-based model reference adaptive control method, in addition to the tracking error, the predictor error is also utilized in adaptive laws to achieve the desired control objectives, such as improving the tracking performance and control signals, and reducing the tracking error. The proposed method is implemented on a turboshaft engine system with actual dynamic values. First, three properly separated equilibrium points are selected on the nominal plant equilibrium manifold to linearize the plant model at the equilibrium points. Then, the predictor-based model reference adaptive control controller is designed and investigated for the three equilibrium points generating three operating modes of the engine, that is, full thrust, cruise, and idle. The stability of the control system is proved by the Lyapunov method. To evaluate the efficiency of the state predictor method in the simulation scenarios, the proposed method is compared with the classic model reference adaptive control method. The simulation results illustrate the superiority of the predictor-based model reference method due to the reduction of unwanted fluctuations in tracking performance, faster convergence of the tracking error to zero, and smoother control signals for all the equilibrium points.

Adaptive control design for active Pogo suppression of large strap-on liquid launch vehicles

In order to better solve the problem of Pogo stability considering parameter uncertainty and time-variation in the large launch vehicle, the model-reference adaptive control based on the dimensionality reduction model (MRAC-DRM) is proposed. Firstly, the state space Pogo model with an active controller is established for control system design by the improved Rubin method. Secondly, a greatly reduced dimensional model of the Pogo control system is derived from the eigenvalue transformation theory, which preserves the features of Pogo instability and is appropriate for control design. Then, the reference model and adaptive control law are designed by the model-reference adaptive control method based on the reduced dimensional model. Finally, two examples with different types of propulsion systems are used to demonstrate the effectiveness of the proposed MRAC-DRM. Simulation results demonstrate that the proposed method not only has excellent adaptability to the Pogo systems with parameter uncertainty and time-variation but also is insensitive to the initial value of feedback gain.

Deep adaptive control with online identification for industrial robots

Derivation of control equations from data is a critical problem in numerous scientific and engineering fields. The inverse dynamic control of robot manipulators in the field of industrial robot research is a key example. Traditionally, researchers needed to obtain the robot dynamic model through physical modeling methods before developing controllers. However, the robot dynamic model and suitable control methods are often elusive and difficult to tune, particularly when dealing with real dynamical systems. In this paper, we combine an enhanced online sparse Bayesian learning (OSBL) algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise; this strategy can be applied to dynamical systems. In particular, we use a sparse Bayesian approach, relying only on some prior knowledge of its physics, to extract an accurate mechanistic model from the measured data. Unmodeled parameters are further identified from the modeling error through a deep neural network (DNN). By combining the identification model with a model reference adaptive control approach, a general deep adaptive control (DAC) method is obtained, which can tolerate unmodeled dynamics. The adaptive update law is derived from Lyapunov’s stability criterion, which guarantees the asymptotic stability of the system. Finally, the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot, and the effectiveness of the proposed method is verified.

Position Estimation of Fault‐Tolerant Permanent Magnet Motor in Electric Power Propulsion Ship System

The electric power propulsion is becoming the important development direction of ship propulsion technology recent years, as it is conducive to the realization of automation, intelligence, reduced noise, enhanced propulsion efficiency, high power density, flexible arrangement, and energy saving. The good fault‐tolerant capacity and robustness of the fault‐tolerant permanent magnet motor (FTPMM) make it a potentially highly effective and economical propulsion motor in the electric power propulsion ship than other alternatives. In this paper, the structure and mathematical model of FTPMM are analyzed and the improved sliding mode observer (SMO) is constructed according to the deviation between the observed currents and actual currents. Combined with model reference adaptive control (MRAC) method and the phase‐lock loop (PLL), the switch function is replaced by the continuous Sigmoid function to reduce the chattering and finally the rotor position estimated accuracy is improved. When the motor winding open‐circuit and short‐circuit fault occur, the current vector fault‐tolerant control strategy is introduced into the improved SMO algorithm, and the rotor position is estimated accurately under faulty conditions. Finally, the proposed improved SMO position estimation algorithm under healthy and faulty conditions is verified in Matlab/Simulink. The FTPMM position sensorless technology would play a critical role in the electric power propulsion ship, and may have a profound impact on the high‐performance advanced ships and green shipping industry as well. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Secure tracking control against sensor and actuator attacks: A robust model-reference adaptive control method

This paper is concerned with secure tracking control problems for cyber-physical systems subjected to a class of sensor and actuator attacks via the model-reference control techniques. The considered attack signals are the unknown bounded signals generated from any linear or nonlinear finite-L2-gain exogenous dynamical system. The auxiliary outputs produced from a high-gain approximate differentiator are first used in co-design for the inputs of the systems and introduced filter, so that the matching condition on the system input and output matrices in the existing results is relaxed. Furthermore, with the aid of the auxiliary outputs, attack’s property and small-gain control methods, a novel model-reference controller with a robust adaptive attack compensator is proposed, such that (a) the tracking error converges to a small set around zero where the set bound can be adjusted by the design parameters, and (b) the desired disturbance attenuation performance is obtained. Different from the existing results, the developed one not only compensates the effect of the attacks automatically by online adjusting the adaptive parameter, but also improves the robustness against the attacks efficiently. Finally, the developed method is validated by a simulation example.

Model reference adaptive control for the inclined channel stablization loop of missile

This paper, A model reference adaptive control method based Lyapunov stability theory is proposed, because it is difficult for the control of inclined channel of missile, due to large disturbance and the dramatic change of missile parameter during the flight. The core strategy is that the real system can be adjusted automatically and in real time to be consistent with the reference model as far as possible, when the output of the real system is different from that of the reference model, so that the real system can run in the best state, this control method can well adapt to the changes of control parameters and external environment. The rationality and validity of the method has been verified by mathematical simulation.

Cooperative signal-free intersection control using virtual platooning and traffic flow regulation

The emerging technologies of connectivity and automation enable the potential for signal-free intersection control. In this context, virtual platooning is posited to be an innovative, decentralized control strategy that maps two-dimensional vehicle movements onto a one-dimension virtual platoon to enable intersection operations. However, the effectiveness of virtual platooning-based control can be limited or degraded by parametric inaccuracies and unparameterized disturbances in vehicle dynamics, heavy traffic congestion, and/or uncoordinated platoons in multi-lane intersections. To explicitly address these limitations, this study proposes a hybrid cooperative intersection control framework consisting of microscopic-level virtual platooning control and macroscopic-level traffic flow regulation for traffic environments with connected autonomous vehicles. In virtual platooning control, vehicles approaching an intersection are organized into coordinated independent virtual platoons to avoid potential conflicts triggered by platoon formation changes. Through coordination, vehicles in a platoon are grouped into compatible passing sets to maintain desired safe spacing when proceeding through the intersection. We propose a distributed adaptive sliding mode controller (DASMC) which uses the backstepping control method and model reference adaptive control method to address parametric inaccuracies, and the sliding mode control method to consistently suppress the negative effects of the unparameterized disturbances. Each vehicle approaching the intersection utilizes the kinematic information from neighboring vehicles to implement the DASMC in a distributed manner such that vehicles within the same virtual platoon can achieve consensus safely. However, virtual platooning control cannot preclude excessive traffic from approaching the intersection, which can cause undesired spillbacks and degrade intersection control performance. To address this issue, traffic flow regulation is integrated with the virtual platooning control using an iterative feedback loop mechanism. In each iteration of the iterative feedback loop, a constrained finite-time optimal control (CFTOC) problem is solved to determine the optimal input flow permitted to proceed through the intersection, and the virtual platooning control provides feedback on the queue status to the CFTOC to initiate the next iteration. The effectiveness of the proposed intersection control framework is evaluated through numerical experiments. The results indicate that the proposed virtual platooning DASMC controller can mitigate the effects of parametric inaccuracies and unparameterized disturbances to achieve consensus for approaching vehicles, as well as guarantee string stability. Further, the proposed framework can alleviate traffic spillbacks and travel delays effectively through traffic flow regulation.

Introducing MRAC-PSS-VI to Increase Small-Signal Stability of the Power System after Wind Power Integration

With the gradual increase of wind power penetration in the power system, the impact of wind farm on system stability is becoming more significant. This study designs the power system stabilizer (PSS) based on the model reference adaptive control (MRAC) method and virtual impedance (VI) control strategy. Then, the active power difference of the doubly fed induction generator (DFIG) is selected as input signal of MRAC-PSS-VI. The small-signal stability of the power system with the DFIG is enhanced by installing MRAC-PSS-VI on the rotor side control (RSC) control link. The controller model is built in DigSILENT/PowerFactory. The improvement effect of the controller on the low-frequency oscillation (LFO) characteristics of the power system is verified by using eigenvalue analysis and the time domain simulation method under different transmission powers of the tie line and different installed positions of the DFIG.

Model Reference Adaptive Control of Cross-Coupling Hysteresis in Piezoceramics With Dynamic Loads

During the ultraprecise cutting of micro-structure surface with fast tool servo (FTS), the hysteresis of piezoelectric actuators (PEAs) are affected by dynamic voltage excitations and real-time cutting force, which declines the servo accuracy and cutting performance. In this paper, for a multi-input-single-output (MISO) cutting system, a cross-coupling rate-dependent Prandtl-Ishlinskii (CRPI) model is proposed and identified for the dynamic hysteresis of PEAs under dynamic voltage excitation and external loads. A model reference adaptive control method is then presented to eliminate the positioning nonlinearity of PEAs. The hysteresis modeling accuracy is discussed and the adaptive controller is validated through experiments.

A Null-Space Compensation Control for Multi-input Redundant Plant Based on Model Reference Adaptive Control

In this paper, we proposed to design a null-space compensation control system for linear time invariant systems with multi-input redundant channels. In the input redundant systems, control input vector may have null-space component of the plant parameter vector. The null-space component does not contribute to the generation of the control force which drives the plant. If a control input that does not include the null-space component can be generated, efficient control is achieved in the sense of minimizing the norm of the control input. In the proposed method, a model reference adaptive control (MRAC) method was used to design the control system that compensates for the null-space component even if the plant parameters are unknown. The effectiveness of the proposed null-space compensation control system was shown by numerical examples.

Modeling and control of a hexacopter with a passive manipulator for aerial manipulation

In this paper, a multi-propeller aerial robot with a passive manipulator for aerial manipulation is presented. In order to deal with the collision, external disturbance, changing inertia, and underactuated characteristic during the aerial manipulation, an adaptive trajectory linearization control (ATLC) scheme is presented to stabilize the multi-propeller aerial robot during the whole process. The ATLC controller is developed based on trajectory linearization control (TLC) method and model reference adaptive control (MRAC) method. The stability of the proposed system is analyzed by common Lyapunov function. Numerical simulations are carried out to compare the ATLC with TLC controller facing collision, external disturbance and changing inertia during an aerial manipulation. Experimental results prove that the developed robot can achieve aerial manipulation in the outdoor environment.

A model reference adaptive variable impedance control method for robot

As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.

Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

MIT control strategy is a kind of model reference adaptive control method with the simplest structure. The reason why the structure is simple is that only the gain of the controlled object is adaptively adjusted. Because only the gain is adjusted, the ability of MIT control strategy to change the characteristics of the controlled object is limited. This also limits its application. In this paper, the simple idea of iterative learning control is introduced into MIT controller to increase the controller's ability to adjust the characteristics of the controlled object, and make it suitable for complex control objects. In the proposed control method, the output of the iterative learning controller is used to adjust the adaptive law of the MIT controller. The proposed control method is applied to the speed control system of ultrasonic motor. Experiments show that although only the gain of MIT controller can be adjusted, the learning process based on memory increases the degree of control freedom. Therefore, the dynamic characteristics of the system can be greatly changed, and the control performance can be significantly improved. Moreover, the proposed method only needs to add a simple P-type iterative learning controller to the MIT controller, and the increase of online calculation amount is small.

Adaptive fault‐tolerant control of variable pitch system of wind power generator based on clustering‐type fuzzy neural network

In view of the faults of the doubly-fed wind power generator system, the health situations of the system are described by the health factor functions. On this basis, the model reference adaptive fault-tolerant control method based on clustering-type fuzzy neural network is proposed. The proposed method can solve the adaptive control issues of wind power generator system with actuator faults, external interference and model uncertainties. The stability and the tracking performance of the system are guaranteed by using Lyapunov stability theorem. Finally, the effectiveness of the proposed method is proved by the fault-tolerant control of the pitch system of the doubly-fed wind power generator.

Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking

This paper investigates a slip ratio control problem of a vehicle braking system in which real-time road conditions are unavailable. To overcome the nonlinearity and uncertainties, the slip ratio control problem is converted to a speed tracking control problem. By introducing a road-condition observer, a model reference adaptive control (MRAC) method is proposed to overcome modeling errors and system uncertainties. The road condition observer based on the Lyapunov theory is designed and a sliding mode observer is also constructed to estimate the unknown vehicle velocity. The asymptotic and robustness property of the adaptive controller is derived by the Popov hyperstability theory. Simulations under different conditions including different road adhesion coefficient, system uncertainties and modeling errors are provided to illustrate the effectiveness and robustness of the proposed method.