Model-free Adaptive Control Algorithm Research Articles

Data-Driven Model-Free Adaptive Containment Control for Uncertain Rehabilitation Exoskeleton Robots with Input Constraints

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model.

Research on Energy-Saving Control Strategies for Single-Effect Absorption Refrigeration Systems

The automatic control device is a critical component of absorption refrigeration systems. Its functional enhancement can reduce operating costs, improve energy efficiency, and ensure long-term stable unit operation. Given that absorption refrigeration systems operate under various dynamic conditions, the rational design of control strategies is particularly important. This study analyzes the influence of changes in the cooling water and heat source water flow rates on the outlet temperature of chilled water in the unit based on the open-loop response characteristics of absorption refrigeration systems. It proposes a dual-loop energy-saving control strategy for single-effect hot water lithium bromide absorption refrigeration systems based on the setpoint comprehensive optimization algorithm. Considering the multiple variables, strong coupling, large inertia, long time delay, and nonlinear characteristics of absorption refrigeration systems, as well as the difficulties in modeling these systems, this study applies a model-free adaptive control algorithm to the system’s control. It derives both SISO and MIMO model-free control algorithms with time-delay components. Through simulations comparing MFAC, improved MFAC, and traditional PID control, the dual-loop energy-saving control strategy is demonstrated to effectively reduce system heat consumption by approximately 20%, decrease power consumption by about 10%, and enhance the system’s SCOP by approximately 19.3%.

Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm

Path tracking control of autonomous vehicle under the measurement disturbance via a novel robust model free adaptive control algorithm

Data-Driven-Based Cooperative Resilient Learning Method for Nonlinear MASs Under DoS Attacks.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Dual-channel event-triggered model-free adaptive iterative learning control under DoS attacks

This paper investigates a dual-channel event-triggered model-free adaptive iterative learning control problem for unknown nonlinear systems under periodic denial-of-service (DoS) attacks. Firstly, periodic DoS attacks on the measured output of the system are modelled using the Bernoulli distribution. Then, to minimise the utilisation of system bandwidth resources, a dual-channel event-triggered mechanism is designed for the sensor-to-controller and controller-to-actuator channels. Subsequently, a dual-channel event-triggered model-free adaptive iterative learning control algorithm is introduced. The convergence of the tracking error is proven in terms of mathematical expectation using Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is verified through two simulation cases.

Attitude Adjustment of a Two-Wheel Balance Vehicle Based on an Enhanced Model-Free Adaptive Control Algorithm

Attitude Adjustment of a Two-Wheel Balance Vehicle Based on an Enhanced Model-Free Adaptive Control Algorithm

Research on autonomous berthing control of MASS based on real time kinematic

Technologies for autonomous navigation of MASS (Maritime Autonomous Surface Ship) in open waters are advancing rapidly. However, the autonomous berthing technology, which represents the final phase of MASS achieving fully autonomous navigation, is still in the process of maturing. Consequently, the study of autonomous berthing control technology holds significant practical importance for the realization of fully autonomous navigation in MASS. This paper proposes an autonomous berthing control program for MASS, based on a MFAC (Model Free Adaptive Control) algorithm that integrates control technology and perception technology. The program encompasses the design of both the berthing logic algorithm and control algorithm. For the controllers, the Preview Deviation Yaw Control System and Speed Control System are designed using the CFDL-MFAC (Compact Form Dynamic Linearization based MFAC) algorithm. The RTK (Real Time Kinematic) data are utilized as inputs for the controller. The experiments are conducted in Lingshui Bay, Dalian, China. The results demonstrate that the lateral distance between the vessel's midship and the berth is less than 1.5 times the vessel's breadth, and the approaching angle is within the range of 0° to 10°, which shows that the proposed program could effectively help vessels achieve desirable berthing states.

Quantized Iterative Learning Bipartite Containment Tracking Control for Unknown Nonlinear Multi-agent Systems

This paper proposes a quantized model-free adaptive iterative learning control (MFAILC) algorithm to solve the bipartite containment tracking problem of unknown nonlinear multi-agent systems, where the interactions between agents include cooperation and antagonistic interactions. To design the controller, the agent’s dynamics is transformed into the linear data model based on the dynamic linearization method, and then a quantized MFAILC algorithm is established based on the quantized values of the relative output measurements. The designed controller only depends on the input and output data of the agent. We prove that under the quantized MFAILC algorithm, the multi-agent systems can achieve the bipartite containment, that is, the output trajectories of followers converge to the convex hull formed by the leaders’ trajectories and the leaders’ symmetric trajectories. Finally, we provide simulations to illustrate the effectiveness of our theoretical results.

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system.

A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

Lateral Control of Autonomous Vehicles with Data Dropout via an Enhanced Data-driven Model-free Adaptive Control Algorithm

Lateral Control of Autonomous Vehicles with Data Dropout via an Enhanced Data-driven Model-free Adaptive Control Algorithm

Moisture content control strategy of magnetorheological fluid based on model-free adaptive control algorithm

Moisture content control strategy of magnetorheological fluid based on model-free adaptive control algorithm

Applications and Challenges of Fieldbus Technology in Intelligent Manufacturing Systems

Abstract This study advances the field of intelligent manufacturing by integrating automation, information technology, and digitalization into a novel “five horizontal and three vertical” system architecture. We introduce an enhanced model-free adaptive control (MFAC) algorithm that utilizes a dynamic linearization method alongside pseudo-partial derivatives (PPD) for optimizing data transfer within intelligent manufacturing systems. Using CANoe software for simulation, our research demonstrates the improved MFAC algorithm’s superior bus message transmission efficiency by 0.0224 to 0.026 and a significant reduction in the network load factor by 0.2068. These advancements highlight the efficacy of fieldbus technology in reducing network congestion and enhancing real-time performance in intelligent manufacturing environments.

Event-triggered data-driven load frequency control for multi-area power system with wind penetration under deregulation environment

In order to solve the problem of power exchange between areas of multi-area power systems under the power market, this paper designs a power trading contract based on the generation participation matrix, so as to simulate the specific process of power change in each area. With the integration of wind power into the large-scale power system, it is difficult to model the multi-area power system. For this situation, this paper designs a data-driven model-free adaptive load frequency control algorithm based on collecting input and output data, which gets rid of the dependence of the power system on the model. Along with the frequent transmission of input and output data in each area, the communication load of the power system also increases. Aiming at saving communication resources, this paper designs an event-triggered mechanism to reduce the communication bandwidth. The stability of the control algorithm is demonstrated theoretically. Finally, a three-area power system with wind penetration is used as an example to simulate and verify the effectiveness of the proposed algorithm in this paper.

Pseudo-partial-derivative information-driven adaptive fault-tolerant tracking control for discrete-time systems

The fault-tolerant tracking control problem is studied for the discrete-time systems with actuator faults. To lessen adverse impacts of actuator fault, a PPD information-driven fault estimation algorithm is established to adaptively estimate actuator fault information online, which avoids the additional construction and training process of neural network. With the aid of the adaptive fault compensation, a model-free adaptive fault-tolerant tracking control algorithm is constructed to ensure that the expected output reference trajectory can be tracked by system output. Moreover, only input and output data are employed throughout the design process, system dynamics are not demanded. Ultimately, the availability of developed strategy is proved through a simulation.

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays.

To solve the time-delay problem and actuator saturation problem of nonlinear plants in industrial processes, an improved compact-form antisaturation model-free adaptive control (ICF-AS-MFAC) method is proposed in this work. The ICF-AS-MFAC scheme is based on the concept of the pseudo partial derivative (PPD) and adopts equivalent dynamic linearization technology. Then, a tracking differentiator is used to predict the future output of a time-delay system to effectively control the system. Additionally, the concept of the saturation parameter is proposed, and the ICF-AS-MFAC controller is designed to ensure that the control system will not exhibit actuator saturation. The proposed algorithm is more flexible, has faster output responses for time-delay systems, and solves the problem of actuator saturation. The convergence and stability of the proposed method are rigorously proven mathematically. The effectiveness of the proposed method is verified by numerical simulations, and the applicability of the proposed method is verified by a series of experimental results based on double tanks.

Layered and subregional control strategy based on model-free adaptive iterative learning for laser additive manufacturing process

Improving stability in process and the quality of manufactured parts is critical to laser additive manufacturing (LAM). Developing appropriate control methods is essential for this purpose. Layered control approaches are commonly utilized in current research on LAM process control, with a primary focus on the pattern of layer-by-layer deposition. However, these strategies might neglect the dynamic and evolving characteristics of the process along the scanning direction. Due to the complex nature of the LAM process, such layered control approaches could be inadequate in addressing intra-layer instability, potentially resulting in metallurgical defects. To overcome these limitations, we propose a layered-and-subregional model-free adaptive iterative learning control (LS-MFAILC) method. Considering the cyclic and the layer-by-layer processing principle of LAM, we develop a subregional control strategy and integrate it with a layered control framework. In addition, the model-free adaptive iterative learning control (MFAILC) algorithm is utilized as the controller due to its ability to control complex and time-varying systems with repetitive running characteristics. In the proposed method, each target layer to be printed in process is divided into multiple non-overlapping regions along the scanning direction, and the process parameters for each region are adaptively controlled based on historical information acquired from previous layers during the process. The temperature information of the melt pool is captured and extracted using a thermal imager integrated with the printing system. Experimental results and comparison with the layered control approach demonstrate the effectiveness and superiority of our method.

An intelligent control system for building aquaculture based on model-free adaptive control

Aiming at the booming building breeding mode, basis on the traditional one-story pig breeding control system, a set of building breeding intelligent control systems based on Model-Free Adaptive Control (MFAC) algorithm was designed. The system adopts the control mode of primary control plus sub-control, which has higher control precision and faster response speed. This paper first designs the hardware system based on the operating principle of the system and then creates the controller software of the system based on the principle of MFAC. Through simulation verification, the results show that the proposed control system based on MFAC can realize the precise control of the environmental parameters of each building unit and has good robustness and adaptability.

Test-based model-free adaptive iterative learning control with strong robustness

ABSTRACT A test-based model-free adaptive iterative learning control algorithm (TB-MFAILC) with strong robustness is proposed in this paper. The algorithm improves the situation where existing model-free adaptive iterative learning control algorithms fail to converge or converge relatively slowly in noisy environments. Also, this work demonstrates the convergence and robustness of the proposed algorithm in different environments. Subsequently, the effectiveness of the proposed algorithm is illustrated by numerical comparison simulations with the existing model-free adaptive iterative learning control algorithm and the PD-based adaptive switching learning control algorithm in noisy environments. Finally, the advantages of the proposed algorithm are further illustrated through the analysis of relevant parameters.

Model-Free Adaptive Control of Hydrometallurgy Cascade Gold Leaching Process with Input Constraints.

Hydrometallurgy technology can directly deal with low grade and complex materials, improve the comprehensive utilization rate of resources, and effectively adapt to the demand of low carbon and cleaner production. A series of cascade continuous stirred tank reactors are usually applied in the gold leaching industrial process. The equations of leaching process mechanism model are mainly composed of gold conservation, cyanide ion conservation, and kinetic reaction rate equations. The derivation of the theoretical model involves many unknown parameters and some ideal assumptions, which leads to difficulty and imprecision in establishing the accurate mechanism model of the leaching process. Imprecise mechanism models limit the application of model-based control algorithms in the leaching process. Due to the constraints and limitations of the input variables in the cascade leaching process, a novel model-free adaptive control algorithm based on compact form dynamic linearization with integration (ICFDL-MFAC) control factor is first constructed. The constraints between input variables is realized by setting the initial value of the input to the pseudo-gradient and the weight of the integral coefficient. The proposed pure data-driven ICFDL-MFAC algorithm has anti-integral saturation ability and can achieve faster control rate and higher control precision. This control strategy can effectively improve the utilization efficiency of sodium cyanide and reduce environmental pollution. The consistent stability of the proposed control algorithm is also analyzed and proved. Compared with the existing model-free control algorithms, the merit and practicability of the control algorithm are verified by the practical leaching industrial process test. The proposed model-free control strategy has advantages of strong adaptive ability, robustness, and practicability. The MFAC algorithm can also be easily applied to control the multi-input multi-output of other industrial processes.

Improved Data-Driven Model-Free Adaptive Damping Controller Design for Interconnected Power Systems With Stochastic Communication Delays

The original model-based wide-area damping control (WADC) is difficult to obtain good damping performance due to the complex structure of interconnected power systems. In this paper, an improved data-driven model-free adaptive control (ID-MFAC) algorithm is proposed to design WADC for interconnected power systems with stochastic communication delays. First, the multiple-input multiple-output power system can be decoupled as multiple single-input single-output subsystems by considering controller interactions and system disturbances. Second, a step-size vector is designed to adjust the pseudo-partial derivative and make the algorithm more flexible by considering both input and output data adequately. Furthermore, an adaptive delay compensator is designed to compensate stochastic communication delays. Last, simulations are conducted to verify that the ID-MFAC algorithm can efficiently suppress multiple inter-area oscillations modes under various stochastic communication delays.