Ultra-local Model Research Articles

Coordinated control design for steering and torque-vectoring in Model-Free Control structure*

Model-Free Control (MFC) approach is a novel technique to handle nonlinearities and uncertainties in control systems in order to provide enhanced performance level. The design of MFC is based on an ultra-local model, which is an approximation of the dynamics of the controlled system for a short period of time. This structure also involves a model-based robust control, which guarantees the accurate tracking performance of the closed-loop system. In this paper, a novel technique is proposed for designing the robust control for taking into account the varying characteristics of the ultra-local model. A novel modeling method is presented, which combines the original plant of the controlled system and the ultra-local model. Then, a robust control design is proposed, by which the tracking performance and the robustness of the closed-loop system can be guaranteed. The design steps and effectiveness of the proposed control strategy is demonstrated through a vehicle-oriented control problem using the high-fidelity simulation software, CarMaker.

Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model

In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control. In industries, the control design must be easy, and it is important that the control law can be applied to nonlinear systems. The MFC-ULM has most of these features. However, in practice, trial-and-error tuning of MFC-ULM design parameters is necessary. To address this problem, we adopt a data-driven tuning approach. In the proposed method, the MFC-ULM design parameters can be tuned from single-experiment data without requiring system identification, and optimal parameters for the MFC-ULM are obtained using the least-squares method. Additionally, we adopt L2-norm regularization to avoid overlearning. The effectiveness of this method was examined using simulations of two nonlinear systems. The results revealed that the MFC-ULM design parameters can be obtained directly without knowing the characteristics of the controlled object.

LPV control design based on ultra-local model for trajectory tracking problem

Model Free Control (MFC) is a novel technique to overcome some modeling and control challenges of highly nonlinear systems. The MFC control strategy consists of two parts, i.e., ultra-local model-based control and state feedback control. This paper proposes the robust Linear Parameter-Varying (LPV) method for design the state feedback control part of the strategy. In the design of robust LPV control the effect of the ultra-local model, formed as a disturbance, is involved. The contribution of this extension to the original concept of MFC design is that a desired performance of the closed-loop system can be achieved. The effectiveness of the presented control strategy is demonstrated through a trajectory tracking problem of autonomous vehicles using the high-fidelity simulation software, IPG CarMaker.

Design of Model-Free Speed Regulation System for Permanent Magnet Synchronous Linear Motor Based on Adaptive Observer

To simplify the system structure of permanent magnet synchronous linear motor (PMSLM), optimize the kinematic performance of the control system, and further improve the speed tracking accuracy of PMSLM, a model-free speed regulation system design of PMSLM based on the adaptive observer is proposed. Aiming at the control instability problem caused by the parameter uncertainty of PI control in PMSLM, a model-free speed controller (MFC) based on the ultra-local model is proposed, which has strong robustness to the uncertainty of motor parameters. Meanwhile, the actual model of PMSLM is used to build the reference model of the adaptive observer, and the adjustable model is designed with the current equation of PMSLM, to identify the speed of PMSLM. The design not only reduces the dependence of the speed controller on the motor parameters, but also simplifies the complexity and cost of the control system, and improves the control performance and anti-interference ability of the control system. The MFC and the observer based on the model reference adaptive system (MRAS) are built by simulation software and applied to the PMSLM system to verify the superiority of the designed control system. Compared with PI controller, sliding mode controller (SMC), and sliding mode observer (SMO), this control method not only simplifies the control system in structure, but also improves the accuracy of PMSLM tracking speed, improves the dynamic response performance of PMSM control system, and optimizes the control ability of the control system.

A new method for fault detection in a free model context

This paper presents a new method of fault detection based on residual signal generation. Most of the existing diagnostic methods that use the residual to detect a failure are often based on the knowledge of the system model. The developed method does not require a precise knowledge or deep information about the system model. It is based on the reconstruction of the system output via an ultra-local model and a model-free controller. The reconstructed/estimated output is used to build the residual signal which is the fault indicator. Several simulation tests have been performed to evaluate the potential of the proposed approach for fault diagnosis. A fault on an actuator of the system is simulated in linear, non-linear and multi-input non-linear case studies. The simulation results reveal that the fault is successfully detected for all these systems under a noisy environment.

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Ultra-Local Model-Based Intelligent Robust Control of PKMs: Theory and Simulations

In this paper, we propose a novel Intelligent Robust Control (IRC) suitable for controlling highly nonlinear Multiple-Input-Multiple-Output (MIMO) systems. The proposed IRC scheme takes advantage of the Robust Integral of the Sign of the Error (RISE) control law and Model-Free Control (MFC). The MFC scheme is mainly composed of: (i) a nonlinear function estimated from an ultra-local model representing the input-output behavior of the system, (ii) the νth derivative of the reference trajectory as a feedforward term, and (iii) a feedback control term. MFC is characterized by its simple concept and its ability to compensate for the modeled and unmodeled system dynamics through its nonlinear compensation term. The proposed IRC approach consists of redesigning the feedback term of MFC scheme based on RISE feedback law to further improve its robustness against external disturbances and to guarantee a semi-global asymptotic tracking despite the presence of disturbances and uncertainties. Numerical simulations under different operating conditions have been conducted on T3KR parallel manipulator, in a pick-and-throw task, to validate the relevance of the proposed IRC strategy. The comparison with a model-based feedforward RISE and a feedforward super-twisting sliding mode control, by exploiting different performance indices, confirms the superiority of the proposed IRC approach.

Robust predictive current control of PWM rectifiers with LCL filters under unbalanced and distorted network conditions

Pulse-width modulation (PWM) rectifiers with LCL filters can achieve better filtering and have lower total inductance than its counterpart of L filters. However, the control complexity is also increased due to the higher system order. Furthermore, the performance may deteriorate significantly when the inductance used in the controller deviates from its actual value due to saturation, temperature, and so on. This paper proposes a robust predictive current control (RPCC) scheme based on the ultra-local model for LCL-filtered PWM rectifiers. Excluding the converter voltage related term, the other part of the system F including the disturbance caused by parameter mismatches and other dynamic process, can be estimated using differential algebra (DA) or an extended state observer (ESO). After obtaining F, the reference voltage vector can be calculated based on the principle of deadbeat current control. The proposed RPCC is extended to an unbalanced and distorted grid by modifying the grid current reference; thus, the three-phase grid currents are balanced and sinusoidal. The proposed RPCC based on DA and ESO is compared to the conventional model-based predictive current control (MPCC), and the experimental results confirm its robustness and effectiveness under mismatched parameters and network disturbances.

Adaptive sliding mode observer–based integral sliding mode model-free torque control for elastomer series elastic actuator–based manipulator

In this brief, an adaptive nonsingular terminal sliding mode observer–based adaptive integral terminal sliding mode model-free control is proposed for the trajectory tracking control of the output torque of elastomer series elastic actuator–based manipulator. Considering the tip load and its external disturbance, an elastomer series elastic actuator–based manipulator model is established. In order to realize the output torque tracking control of elastomer series elastic actuator–based manipulator, by using the characteristics of elastomer series elastic actuator, the output torque control is transformed into position control. Based on the idea of model-free control, an ultra-local model is applied to approximate the dynamic of the manipulator, and all the model information is considered as an unknown lumped disturbance. The adaptive nonsingular terminal sliding mode observer is designed to estimate the lumped disturbance, and the absolute value of the tracking error is introduced into the sliding surface to make the selection of parameters more flexible. Then, on the basis of adaptive nonsingular terminal sliding mode observer, the adaptive integral terminal sliding mode model-free control is proposed under model-free control framework. The design and analysis of both observer and controller do not rely on accurate model information. Finally, the performance of the proposed method is verified by simulation results.

A robust model-free controller for a three-phase grid-connected photovoltaic system based on ultra-local model

In this paper, a robust model-free controller for a grid-connected photovoltaic (PV) system is designed. The system consists of a PV generator connected to a three-phase grid by a DC/AC converter. The control objectives of the overall system are to extract maximum power from the PV source, to control reactive power exchange and to improve the quality of the current injected into the grid. The model-free control technique is based on the use of an ultra-local model instead of the dynamic model of the overall system. The local model is continuously updated based on a numerical differentiator using only the input–output behavior of the controlled system. The model-free controller consists of a classical feedback controller and a compensator for the effects of internal parameter changes and external disturbances. Simulation results illustrate the efficiency of the controller for grid-connected PV systems.

A novel intelligent ultra-local model control-based type-II fuzzy for frequency regulation of multi-microgrids

Due to the random nature of renewable energy in the micro-grid (MG) configuration, the energy generators in such systems are not able to offer an uninterrupted power and a mismatch between the demand and generation will be occurred. To alleviate this unbalance, technically, conventional storage devices are one of the practical solutions to this issue. However, the implementation of storage system is not effective from the economic preceptive and, as an alternative, the battery of electric vehicle can be also embedded in the structure of the MGs. In this work, an ultra-local model (ULM) controller based on a single-interval type-II fuzzy logic (SIT2-FL) controller is applied for frequency regulation of multi-MG(s) with electric vehicles. In this scheme, the ULM controller plays a critical role in the reducing the dependency of the controller to the MG model. An extended observer error is embedded in the structure ULM controller to remove the uncertainties included in the system. The SIT2-FL is established as supplementary controller to remove the error of observer and further ameliorate the multi-MG performance. Comparative simulation analysis and robustness examination are made to ascertain the usefulness of the suggested ULM controller-based SIT-FL.

Trajectory tracking control of quadrotor under variable payloads with model-free controller and sliding mode control technology

Purpose This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads. Design/methodology/approach In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated. Findings The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity. Practical implications In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads. Originality/value The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.

Model-Free Non-Cascade Integral Sliding Mode Control of Permanent Magnet Synchronous Motor Drive with a Fast Reaching Law

Electric vehicle has become the main trend of the development of automobile industry. As a highly symmetrical system, the characteristics of drive motor will have a great impact on the driving comfort. Aiming at the control regulation of permanent magnet synchronous motor (PMSM) drive, a model-free and non-cascade sliding mode control with a fast-reaching law is proposed in this paper. Firstly, the mathematical model of PMSM is constructed as an ultra-local model without considering any motor parameters. Then, to improve the response speed, an integral sliding mode method with a fast reaching law is proposed, and the fast convergence can be realized. The controller system adopts single loop non-cascade control, which greatly simplifies the system structure. In order to further improve the anti-disturbance performance of the system, the dual disturbance observers are used to compensate the disturbance through feed-forward control. The stability of the system is proved by Lyapunov. Finally, the experimental results show that the proposed method has faster convergence speed and stronger anti-disturbance ability.

Robust predictive current control of induction motors based on linear extended state observer

Model predictive current control can achieve fast dynamic response and satisfactory steady-state performance for induction motor (IM) drives. However, many motor parameters are required to implement the control algorithm. Consequently, if the motor parameters used in the controller are not accurate, the performance may deteriorate. In this paper, a new robust predictive current control is proposed to improve robustness against parameter mismatches. The proposed method employs an ultra-local model to replace the mathematical model of the IM. Additionally, to improve the control performance, a linear extended state observer is developed for disturbance estimation. Experimental tests confirm that satisfactory tracking performance can still be obtained although the motor parameters may not be accurately set in the controller.

Model-Free Control Using Improved Smoothing Extended State Observer and Super-Twisting Nonlinear Sliding Mode Control for PMSM Drives

This paper proposes a novel model-free super-twisting nonlinear sliding mode control (MFSTNLSMC) strategy with an improved smoothing extended state observer (SESO) for permanent magnet synchronous motor (PMSM) drives. First of all, the improved SESO is introduced to estimate the unknown term of the PMSM ultra-local model. Secondly, a novel nonlinear sliding mode surface (NLSMS) is designed, which can effectively overcome the disadvantages of simple and rough signal processing of the conventional linear sliding mode surface. At the same time, a super-twisting (ST) structure is chosen to suppress the chattering phenomenon and improve system robustness. Then, the Lyapunov stability theorem is used to prove the stability of the proposed control algorithm. Finally, both comparative simulations and experimental demonstrations verify the excellent speed tracking performance and robustness of the proposed control strategy.

Nonsingular Terminal Sliding Mode Control With Ultra-Local Model and Single Input Interval Type-2 Fuzzy Logic Control for Pitch Control of Wind Turbines

As wind energy is becoming one of the fastest-growing renewable energy resources, controlling large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. The main goal of the current work is to propose an intelligent control of the wind turbine system without the need for model identification. For this purpose, a novel model-independent nonsingular terminal sliding-mode control (MINTSMC) using the basic principles of the ultra-local model (ULM) and combined with the single input interval type-2 fuzzy logic control (SIT2-FLC) is developed for non-linear wind turbine pitch angle control. In the suggested control framework, the MINTSMC scheme is designed to regulate the wind turbine speed rotor, and a sliding-mode (SM) observer is adopted to estimate the unknown phenomena of the ULM. The auxiliary SIT2-FLC is added in the model-independent control structure to improve the rotor speed regulation and compensate for the SM observation estimation error. Extensive examinations and comparative analyses were made using a real-time software-in-the-loop (RT-SiL) based on the dSPACE 1202 board to appraise the efficiency and applicability of the suggested model-independent scheme in a real-time testbed.

An Improved Model-Free Current Predictive Control Method for SPMSM Drives

Traditional model predictive current control (MPCC) method depends on motor model for predictive control, when the motor parameters change with the working conditions, the predictive performance of MPCC will be deteriorated. To improve the parameter robustness of MPCC, a model-free current predictive control method that combines ultra-local model and sliding mode observer is proposed. First, the prediction model of MPCC based on the mathematical model of surface-mounted permanent magnet synchronous motor (SPMSM) is replaced by the ultra-local model that does not use any motor parameters. Second, the sliding mode observer is adopted to observe the parameter of ultra-local model and compensate parameter disturbance. Finally, the stability of the sliding mode observer is proved by the Lyapunov stability criterion. The traditional MPCC method and the proposed model-free current predictive control method are comparatively analyzed, simulation and experimental results show that the proposed model-free current predictive control method can improve the parameter robustness of MPCC.

Automatic Regulation of Anesthesia via Ultra-Local Model Control

Abstract As a part of the BMS2021 Benchmark Challenge, this paper deals with the design and testing of a closed-loop anesthesia delivery regulation system by exploiting the open-source Matlab-based patient simulator. Because of system inherent complexity together with intra-and inter-patient parameters variability and partially unknown disturbances, traditional model-based approaches may suffer. To overcome these limitations, we opt for a data-driven approach using real-time ultra-local models coupled with the corresponding so-called intelligent controllers. In this way, one maintains the hemodynamic variables while regulating the levels of hypnosis, analgesia, and neuromuscular blockade in anesthesia by automatic delivery of drugs. The performance of the proposed approach has been evaluated in silico by considering a representative dataset composed of 24 patients, the presence of disturbances mimicking both surgical stimulations and actions of “anesthesiologist in the loop”, including also noise effects and time-varying system delays.

Machine Learning Approach Based on Ultra-Local Model Control for Treating Cancer Pain

Cancer illness still is one of the most common illnesses in the world, which is constantly rising. Chemotherapy plays a crucial role in treating cancer patients. In this paper, we have presented a novel intelligent sensor for controlling and adjusting chemotherapy parameters which consist of an ultra-local (ULM) controller based on a deep deterministic policy gradient (DDPG). First, the feedback signal is provided using a sensor to calculate the population of cells. Then, a controller sends the proper control commands to the actuator (chemotherapy). In the suggested scheme, the ULM is applied to the dynamic model of cancer. In order to shrink tumor cells and rising immune and normal cells at the same time. Moreover, for improving the performance of the established ULM scheme, a DDPG algorithm with the actor-critic structure is used for tuning the parameters of ULM in an adaptive manner. To demonstrate the supremacy of the DDPG based ULM controller, the conventional ULM and proportional integrator (PI) are also designed for the cancer treatment. Simulation outcomes prove the improved cancer treatment compared to the ULM and PI schemes.

Adaptive Finite Time Control for Wearable Exoskeletons Based on Ultra-local Model and Radial Basis Function Neural Network

This paper investigates an adaptive finite time control scheme for wearable exoskeletons to realize trajectory tracking control. The main feature of the proposed scheme is model-free in which no dynamic models are required except for the input and output data. Firstly, a second order ultra-local model is employed to replace the complex dynamic model of wearable exoskeleton as the controlled object, which is a novel designed model. In addition, a nonsingular fast terminal sliding surface is proposed to design the controller to guarantee the finite time convergence of tracking errors, and a radial basis function (RBF) neural network is developed to approximate the lumped disturbance in the ultra-local model. Then the stability of closed-loop system is proved by Lyapunov theory. Finally, to validate the proposed control scheme, virtual prototype is designed in SolidWorks and transferred to MATLAB, then visual simulation program is implemented based on SimMechanics. What’s more, reference trajectories are extracted from the measured data of DELSYS Electromyography (EMG) recording system. The effectiveness of proposed scheme is demonstrated by the simulation results.