Model Reference Control Research Articles

Practical one-shot data-driven design of fractional-order PID controller: Fictitious reference signal approach

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable.

Modeling and Path-Following Control for Dubins Dynamics in Complex State Space

A path-following control design in the complex state space for the Dubins vehicle is proposed. The vehicle dynamics are modeled as a bilinear system of two complex ordinary differential equations with a scalar complex control input. The developed control solution represents a feedback linearizing proportional–integral–derivative servomechanism, with real valued control gains applied to complex valued tracking error components. The design is performed within the model reference control framework. It is applied to solving path-following and waypoint routing problems for curves with bounded curvature and vehicles with bounded speeds. The path-following controller regulates the system turn rate and the reference model speed. The vehicle speed dynamics are independently maintained as desired. In that regard, the developed control policy forces the system position to track a virtual target point defined by the reference model. Verifiable sufficient conditions are formulated to enable steering the Dubins vehicle along the designated routes in the presence of unknown bounded environmental disturbances, such as wind and gust. Application examples of the developed path-following control solution to waypoint routing guidance for unmanned aerial platforms are discussed.

Distributed Constrained Consensus of Multiagent Systems With Uncertainties and Disturbances Under Switching Directed Graphs

This paper provides a distributed leaderless consensus control framework for nonlinear multi-agent systems with time-varying asymmetric state constraints, uncertainties, and disturbances under switching directed graphs. In such a framework, original constrained states of agents are first transformed into free states in a transformed state space. To deal with switching directed graphs, we drive agents towards consensus in the transformed space by leveraging a model reference control scheme, and it is sufficient that the original states reach consensus strictly subject to the time-varying constraints under mild assumptions. A single-layer neural network with weights adapted online is leveraged to approximate the uncertainties in agent dynamics. For external disturbances and reconstruction errors in the approximation, we introduce a robust term with an adaptive gain for compensation. Distributed consensus algorithms are proposed, respectively, for multi-agent systems with first- or second-order dynamics. We prove convergence to consensus via Lyapunov analysis and study the proposed algorithms' performance using numerical simulations.

Multi-ASV Coordinated Tracking With Unknown Dynamics and Input Underactuation via Model-Reference Reinforcement Learning Control.

This article studies coordinated tracking of underactuated and uncertain autonomous surface vehicles (ASVs) via model-reference reinforcement learning control. It considered how model-reference control can be incorporated with reinforcement learning to address the challenges caused by model uncertainties and input underactuation, and how existing results may be employed to realize adaptive communication amongst ASVs. It is demonstrated that the proposed algorithm has a better performance over baseline control and effectively improves the training efficiency over reinforcement learning.

Disturbance-Observer-Based LQR Tracking Control for Electro-Optical System

To improve the dynamic property and the disturbance suppression ability of an electro-optical tracking system, this paper presents a disturbance-observer-based LQR tracking control method. The disturbance-observer-based robust controller is composed of three parts: one is the LQR tracking controller, one is the reference model controller and the other is a compensatory controller designed with the output of the disturbance observer. The uncertainty and disturbances are considered in the controller design. By Lyapunov stability theory and linear matrix inequality (LMI) technique, the sufficient conditions for observer gain and controller gain of the tracking reference model of the electro-optical system are given. Simulation and experimental results show that the proposed method in this paper not only improved the disturbance suppression ability of the electro-optical tracking system but also improved the dynamic property of the electro-optical tracking system, such as rise time, settling time and system overshoot. Specially, compared with other methods in this paper, the tracking accuracy and the disturbance suppression ability of the proposed method are about two to three times higher. The method presented in this paper has important reference value in the field of electro-optical system applications. But, with the development of electro-optical system applications, the tracking accuracy and disturbance suppression ability of the proposed method cannot meet the actual requirements of an electro-optical system. The next step of this paper will consider a variety of practical requirements, such as the controller saturation problem and tracking reference target with strong maneuverability, and further optimize the proposed method.

Adaptive-critic-based model reference control for unknown nonlinear systems with input constraints

In this paper, the optimal model reference adaptive control (MRAC) problem is studied for the unknown discrete-time nonlinear systems with input constraint under the premise of considering robustness to uncertainty. Through an input constraint auxiliary system, a new adaptive-critic-based MRAC algorithm is proposed to transform the above problem into the optimal regulation problem of the auxiliary error system with lumped uncertainty. In order to realize the chattering-free sliding model control for the auxiliary error system, an action-critic variable is introduced into the adaptive identification learning. In this case, the closed-loop control system is robust to the disturbance and the neural network approximation error. The uniformly ultimate bounded property is proved by the Lyapunov method, and the effectiveness of the algorithm is verified by a simulation example.

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.

DIRECT MODEL REFERENCE TAKAGI–SUGENO FUZZY CONTROL OF SISO NONLINEAR SYSTEMS DESIGN BY MEMBERSHIP FUNCTION

What is discussed in this article is to find a way for membership functions optimally. In most scholars, these functions are constant and have a limited number. Therefore, in some cases, this limitation reduces control performance improvement. One of the best solutions is finding these functions in a differential form. This article used the Takagi-Sugeno function as a fuzzy detector to identify and control a nonlinear SISO system by direct adaptive reference model control. Using this method with Lyapunov for the stability of the control system makes output fuzzy linguistic variables optimally. Then simultaneously using these values, membership functions can be defined in differential form. Therefore, there is no other limitation in the variance and midpoint.

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.

A dual-loop active vibration control technology with an RBF-RLS adaptive algorithm

Active control strategies have been widely used in micro-vibration isolation with higher performance requirements. Aiming at the problem of insufficient vibration isolation performance due to time delay in feedback control, the ground-based feedforward active control is introduced to compensate in advance for higher pursuit. However, the inaccuracy of the feedforward control reference model severely restricts the vibration isolation performance. With the assistance of RBF neural network algorithm for accurate real-time identification of online model, a feedforward-feedback dual-loop active hybrid control (DAHC) strategy based on the RBF-RLS adaptive algorithm is proposed in this paper. The adaptive feedforward control uses the RBF neural network algorithm to identify the accurate model of the system online, and input it to the transverse filter of the RLS adaptive control algorithm for recursive calculation, which can effectively reduce the system error caused by the inaccuracy of the model. The experimental results show that the accuracy of online model affects the amplitude attenuation performance by 57.1%. DAHC strategy can greatly reduce the resonance peak by 25.02 dB in micro-vibration. Real applicative experiment further proves the effectiveness of our proposed algorithm.

Switching boundary estimation and adaptive sliding mode control for the dynamical systems with discontinuity due to the actuators.

Due to the discontinuous physical property of the control actuators, the state space of such a dynamical system is divided into many subdomains. For each subdomain, the flow of such a system is governed by the corresponding subsystem. The state boundary between the adjacent subdomains is called the physical switching boundary. The controller is designed to switch when the subsystem of such a discontinuous dynamical system is switched in order to have the optimum control performance. Since the ambiguity and uncertainty of modeling, the mathematical expressions for describing the discontinuous physical properties of the control actuators may not be accurate. Since the nominal switching boundary where the controller really switches is not exactly the corresponding physical switching boundary, the mismatch between the subsystem and the corresponding controller will occur and it may seriously affect the control performance. Therefore, a boundary estimation algorithm is proposed to estimate the physical switching boundaries based on the model reference control and error backpropagation. The simulation results show that the adaptive sliding mode control with the boundary estimation algorithm has superior control performance and strong robustness to deal with the internal uncertainty, the external interference, and the boundary ambiguity.

Adaptive Robust Path Tracking Control for Autonomous Vehicles Considering Multi-Dimensional System Uncertainty

As the bottom layer of the autonomous vehicle, path tracking control is a crucial element that provides accurate control command to the X-by-wire chassis and guarantees the vehicle safety. To overcome the deterioration of control performance for autonomous vehicle path-tracking controllers caused by modeling errors and parameter perturbation, an adaptive robust control framework is proposed in this paper. Firstly, the 2-DOF vehicle dynamic model is established and the non-singular fast terminal sliding mode control algorithm is adopted to formulate the control law. The unmeasured model disturbance and parameter perturbation is regarded as the system uncertainty. To enhance the control accuracy, the radial basis forward neural network is introduced to estimate such uncertainty in real time. Then, the dynamic model of an active front steering system is established. The model reference control algorithm is applied for the steering torque control considering model uncertainty brought by the dissipation of manufacturing and mechanical wear. Finally, the Simulink–CarSim co-simulation platform is used and the proposed control framework is validated in two test scenarios. The simulation results demonstrate the proposed adaptive robust control algorithm has satisfactory control performance and good robustness against the system uncertainty.

Data-Driven Model-Reference Control With Closed-Loop Stability: The Output-Feedback Case

We generalize a recently introduced data-driven approach for model-reference control design with closed-loop stability guarantees to the case of single-input single-output systems with inaccessible state. By considering a dynamic controller with fixed structure and leveraging a data-based description of the closed-loop dynamics, we propose a two-stage strategy for the optimization of the controller’s parameters to match the desired closed-loop behavior. By means of a benchmark simulation example, we show the potential of the proposed approach and the impact of a simple strategy to handle noisy measurements.

Safe Model-Based Multivariable Control of Peritoneal Perfusion

This paper examines the control of abdominal perfusion during medical interventions. The goal is to avoid safety risks such as excessive retained fluid volume, tissue damage/trauma, and intra-abdominal hypertension. The paper presents a perfusion system with peristaltic pumps dictating fluid inflow/outflow rates, and uses model-based control to avoid multiple safety risks simultaneously. Specifically, the paper: (i) introduces a discharge efficiency metric reflecting the risk of tissue trauma and/or outflow cavitation; (ii) identifies a dynamic model of discharge efficiency from animal test data; and, (iii) develops a safe perfusion control algorithm based on this model. The algorithm receives three user inputs: a desired inflow rate, a desired perfused volume, and a safety bound on discharge efficiency. The algorithm minimizes the deviation of the commanded inflow/outflow rates from a nonlinear model reference controller, subject to a barrier constraint on discharge efficiency. This furnishes a switching control structure providing convergence to the desired perfusion settings in the absence of outflow occlusion, and operating safely when outflow is occluded, as shown in simulation-based validation studies.

Distributed model reference control for synchronization of a vehicle platoon with limited output information and subject to periodical intermittent information

Distributed model reference control for synchronization of a vehicle platoon with limited output information and subject to periodical intermittent information

Fractional order output-feedback tube-MRAC design for a class of fractional order transfer functions with unknown parameters

In this paper, we address a class of fractional order systems defined by transfer function with unknown parameters and present a novel fractional order tube model reference adaptive control (FOTMRAC) design for their control despite the fact that the only available signals are the input and output of the system. This is the main difference with preceding authors' recent study, where the states of the system where available. Based on the output feedback, this strategy is a generalisation of the tube model reference adaptive control (TMRAC) to fractional order systems. The basic idea is to replace the single reference trajectory well known in reference model control theory by a set of admissible trajectories called tube reference. Also, an optimisation problem is resolved online in order to update the correction control signal minimising a control cost criterion. The asymptotic stability is proved using the Lyapunov extension theorem. Simulation results illustrate and confirm the effectiveness of the proposed control design.

Model reference control by recurrent neural network built with paraconsistent neurons for trajectory tracking of a rotary inverted pendulum

This investigation presents a recurrent paraconsistent neural network (RPNN), as the main element of the model reference control (MRC) strategy for the rotary inverted pendulum (RIP). The RIP characteristics, such as nonlinearity, two-degree-of-freedom (2DoF) motion, and under-actuated system, make it an ideal device to apply and test the RPNN. The designed paraconsistent neural model reference controller (PNMRC) uses three RPNNs: two of them to model the arm and pendulum angles and the third one to control the system while tracking a reference trajectory. The hidden neurons of the RPNN use the paraconsistent annotated logic by 2-value annotations (PAL2v) rules as an activation function. PAL2v, as a member of the paraconsistent logics family, deals with uncertain and contradictory data, representing a potentially robust alternative to applications of artificial neural networks in control. The PAL2v neuron is detailed and compared with other activation functions in recurrent neural networks (RNN). With real-time experiments, the PNMRC strategy is compared with classical control methodology, presenting excellent performance.

Robust model reference tracking for uncertain second‐order nonlinear systems with application to robot manipulator

AbstractIn this article, robust model reference control for uncertain second‐order nonlinear systems is investigated by applying fully actuated system approaches. A robust stabilizing control law is constructed for the uncertain systems based on the Lyapunov stability theory. With the obtained robust control results, a robust model reference tracking (RMRT) control scheme is proposed to ensure the tracking error finally converges globally into a bounded ellipsoid. The established RMRT controller is composed of three parts, the basic part cancels the known nonlinearities in the system and simultaneously assigns the linear dominant term in the closed‐loop system, the robustness part overcomes the effects of the nonlinear uncertainties in the system, the compensator part compensates the effect of the reference model state and reference control input to the tracking error. Furthermore, based on a general parametric solution to the type of Sylvester matrix equations, simple and complete parameterization of the RMRT control designs is provided. An application to robot manipulator indicates satisfactory control system performances with the proposed RMRT control scheme.

Mixed H2/H∞ model reference control of linear parameter‐varying discrete‐time systems with real‐time application to aeropendulum system

AbstractThe investigation of this paper revolves around the problem of designing mixed model reference control (MRC) systems of linear parameter‐varying discrete‐time systems. In this paper, two types of mixed MRC systems are considered. These two types are the mixed state‐feedback and the mixed dynamic output‐feedback MRC systems that are designed using a parameter‐dependent Lyapunov function. The experimental results of the aeropendulum system are used to evaluate and show the effectiveness of the proposed controllers. The results indicate the satisfactory performances of the closed‐loop systems.

Advanced control with extended Kalman filter and disturbance observer

<p>This paper describes a novel fuzzy tracking control for permanent magnet synchronous motors (PMSM) using an extended Kalman filter (EKF) and disturbance observer (DO). The goal is to create a robust controller able to drive the system’s states to track a virtual reference model and provide a low disturbance effect on the synchronous machine. First, the PMSM is represented using a fuzzy model Takagi-Sugeno (T-S) attended by the load torque variation. Next, To simplify the construction of a virtual reference model and nonlinear tracking control, a fuzzy tracking control based on virtual desired variables (VDVs) is proposed. Using this concept, a two-stage design procedure is developed: i) determine the VDVs using the desired output and the load torque, which can be estimated using DO and EKF and ii) calculate the fuzzy controller gains by solving linear matrix inequalities (LMIs). The efficiency of the suggested strategy is eventually shown through simulation results</p>