Model-free Adaptive Control Research Articles

Hybrid modeling-based temperature and humidity adaptive control for a multi-zone HVAC system

In this paper, we study multi-zone air environment comfort and building energy conversation for the heating, ventilation, and air conditioning (HVAC) system. Each zone is equipped with the air-handling unit (AHU) and fan coil unit to regulate the supply air state for comfortable zone climate. It is known that the multi-zone air environment appears spatial and temporal variation due to the hygro-thermal interaction which is an extremely complex nonlinear dynamic process. In this study, hybrid models incorporating the first-principles model with full form dynamic linearization (FFDL)-based data-driven model have been proposed to precisely describe the multi-zone climate dynamics including unknown nonlinearity and uncertainty. Then model-free adaptive control (MFAC) scheme is designed for multi-zone climate control, which contains controller design, parameter estimation and reset mechanism to ensure system control performance. Finally, different case studies are conducted to test the performance of model prediction and system control for the multi-zone climate environment. Model validation shows that the zone climate prediction of the proposed hybrid model shows better agreement with actual measured data. The step response results display that the hybrid model-based MFAC can realize multi-zone climate control performance improvement in faster convergence without stable bias. By comparing with prior work, the hybrid model-based MFAC has the potential to get closer to actual situation in cost-effective multi-zone climate control.

High-order model-free adaptive iterative learning control

In this paper, a high-order model-free adaptive iterative learning control (HOMFAILC) scheme is proposed for nonlinear non-affine discrete-time systems. The main feature of the proposed HOMFAILC is that not only the control input iterative learning law is designed with higher-order algorithms but also the parameter iterative adaptive updating law has the similar forms. Using additional control knowledge in both the iterative learning control law and the parameter estimation law, the control performance of the proposed HOMFAILC can be improved consequently. Furthermore, the similar structure in the control law and parameter updating law can also help to achieve a better control performance. The convergence is proved with rigorous mathematical analysis. Simulation study shows the effectiveness of the proposed HOMFAILC.

Date-Driven Tracking Control via Fuzzy-State Observer for AUV under Uncertain Disturbance and Time-Delay

This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a fuzzy state observer (FSO) was designed to achieve high precision. Concretely, the mathematical model of AUV motion was analyzed, and simplified via model decoupling, thus providing the model basis with an explicit physical explanation for the controller. Second, the MFAPC scheme for a multiple-inputs and multiple-outputs (MIMO) discrete time system was derived, that estimates system external disturbance. The controller can online estimate and predictive time-varying parameter pseudo-Jacobian matrix (PJM) to establish equivalent state space data-model for AUV motion system. Third, the Takagi–Sugeno (T–S) fuzzy model based state observer was designed to combine with the MFAPC scheme for the first time, which was used to online decline the state error generated by system uncertain time-delay. In addition, the stability of the proposed control scheme was analyzed. Finally, two trajectory tracking scenarios were designed to verify the effectiveness and robustness of the proposed FMFAPC scheme, and the simulations are implemented using the realistic parameters of T-SEA AUV.

A Novel Multi-Agent Model-Free Adaptive Control Algorithm for a Class of Multivehicle Systems with Constraints

To solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a model-free adaptive control algorithm with constraints (cMFAC) is proposed in this paper. In the cMFAC algorithm, a dynamic linearization technique with a time-varying parameter pseudo-gradient (PG) is used to linearize the multivehicle collaborative system. Then, a cMFAC controller is designed. The algorithm sets the input and output constraints at the same time to prevent the vehicle speed and other parameters from exceeding the specified range. The main advantage of the cMFAC algorithm is that the entire control process only needs the input and output data of each vehicle and can effectively handle the input and output constraints. In addition, the stability of the cMFAC method is verified through strict mathematical analysis, and its effectiveness is verified with semi-physical experiments based on a MATLAB/Simulink module and CarSim platform connection environment. It is worth noting that the proposed cMFAC controller is symmetric because the input cost function and PG cost function have symmetric and similar structures, and the forms of the two cost functions are the same.

Design of Real-Time Extremum-Seeking Controller-Based Modelling for Optimizing MRR in Low Power EDM

Electric discharge machining (EDM) is one of the non-conventional machining processes that supports machining for high-strength and wear-resistant materials. It is a challenging task to select the process parameters in real-time to maximize the material removal rate since real-time process trials are expensive and the EDM process is stochastic. For the ease of finding process parameters, a modelling of the EDM process is proposed. Due to the non-linear relationship between the material removal rate (MRR) and discharge time, a model-free adaptive extremum-seeking controller (ESC) is proposed in the feedback path of the EDM process for finding an optimal value of the discharge time at which the maximum material removal rate can be achieved. The results of the model show a performance that is closer to the actual process by choosing steel workpieces and copper electrodes. The proposed model offers a lower error rate when compared with actual experimental process data. When compared to manual searching for an optimal point, extreme seeking online searching performed better as per the experimental results. It was observed that the experimental validation also proved that the ESC can produce a large MRR by tracking the extremum control. The present study has been limited to only the MRR, but it is also possible to implement such algorithms for more than one response parameter optimization in future studies. In such cases the performance measures of the process could be further enhanced, which could be used for a real-time complex die- and mold-making process using EDM.

Improved Model-Free Adaptive Control for Integral Plants

In this paper, an application of a model-free adaptive control (MFAC) to integral plants, which are linear plants with poles at the origin, is discussed. It is well known that an MFAC gives excellent performance for nonlinear plants and stable linear plants. However, for integral plants that are widely used in industrial robots, an MFAC fails in control. Therefore, we firstly investigate the stability of an MFAC for an integral plant, and it is shown that a system of an MFAC with an integral plant becomes unstable. Then, we propose a structure to improve an MFAC by adding a PID controller. The stability of the proposed method with an integral plant is confirmed by the Jury stability criterion and a root locus method. The validity of our method is confirmed by simulations and actual experiments.

An Enhanced Model Free Adaptive Control Approach for Functional Electrical Stimulation Assisted Knee Joint Regulation and Control.

Functional electrical stimulation has been widely used in the neurologically disabled population as a rehabilitation method because of its intrinsic and higher ability to activate paralyzed muscles. However, the nonlinear and time-varying nature of the muscle against exogenous electrical stimulus makes it very challenging to achieve optimal control solutions in real-time, that results in difficulty in achieving functional electrical stimulus-assisted limb movement control in the real-time rehabilitation process. Model-based control methods have been suggested in many functional electrical stimulations elicited limb movement applications. However, in the presence of uncertainties and dynamic variations during the process the model-based control methods are unable to give a robust performance. In this work, a model-free adaptable control approach is designed to regulate knee joint movement with electrical stimulus assistance without prior knowledge of the dynamics of the subjects. The model free adaptive control with a data-driven approach is provided with recursive feasibility, compliance with input constraints, and exponential stability. The experimental results obtained from both able-bodied participants and a participant with spinal cord injury validate the ability of the proposed controller to allocate electrical stimulus for regulating seated knee joint movement in the pre-defined trajectory.

Event-Triggered Data-Driven Control for Nonlinear Systems Under Frequency-Duration-Constrained DoS Attacks

This paper addresses the event-triggered model free adaptive control (MFAC) problem for unknown nonlinear systems under denial-of-service (DoS) attacks, where the design and analysis are discussed under the data-driven framework. Firstly, by using the novel pseudo partial derivative, the nonlinear systems are converted into an equivalent data-relationship model. Then, the DoS attacks are described as limited by their frequency and duration, and without any more specific assumptions about the attack structure or strategy. Next, a novel event-triggered MFAC scheme is proposed. By employing the Lyapunov stability theory, the stability performance is analyzed. Furthermore, a compensation algorithm is designed to against the adverse impact brought by the DoS attacks. Finally, simulations including a numerical example and a load frequency control (LFC) example for multi-area power systems are given to demonstrate the validity and applicability of the proposed schemes.

Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence

In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This method adds the differential of tracking error in the criteria function to compensate for the effect of the random disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative (PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the maximum tracking error is not limited by the initial value of PPD. The convergence of the maximum tracking error is deduced in detail. This method can track the desired output with enhanced convergence and improved tracking performance. Two examples are used to verify the convergence and effectiveness of the proposed method.

Data-Driven Model-Free Adaptive Positioning and Anti-Swing Control for Bridge Cranes

In the positioning and anti-swing control of bridge crane, a model-free adaptive control (MFAC) based on data-driven is proposed in order to eliminate the dependence of controller design on the model and the influence of unmodeled dynamics and uncertain disturbances on the controller performance. Only using the input and output data of the bridge crane system, the virtual full format dynamic linearized data model of the bridge crane nonlinear system is obtained through the data-driven modeling method. On the basis of this virtual data model, a model-free adaptive control law and a pseudo-Jacobian matrix estimation algorithm are designed according to the optimization theory under the constraint conditions. The stability of the closed loop system and the convergence of the system error are analyzed and proved by Lipschitz condition and inequality theory. The effectiveness of the control strategy for positioning and anti-swing control of bridge cranes is verified on simulated simulation and experimental platform of bridge crane. The results show that the proposed method is feasible and has good anti-disturbance performance and robustness.

Mitigating Subsynchronous Oscillation Using Model-Free Adaptive Control of DFIGs

Incorporating subsynchronous damping controllers (SSDCs) into the control loops of doubly-fed induction generators (DFIGs) is one of the most cost-effective methods to suppress subsynchronous oscillation (SSO). However, it is difficult to achieve satisfactory control performance of SSDCs due to the time-varying structure and operating conditions of wind integrated power systems. In this paper, an improved model-free adaptive control (MFAC) method is developed for DFIGs to overcome the limitations of conventional methods for SSO mitigation. First, the structure of the MFAC-based SSDC (M-SSDC) for DFIGs is designed. Then, an improved MFAC predictive control algorithm is proposed to achieve SSO mitigation. Moreover, the stability of the closed-loop system with the proposed M-SSDC is analyzed, which provides some guidelines for determining controller parameters. Additionally, the input-output signal pair of M-SSDC is selected by using geometric measure for the optimum damping performance. Case studies under various operating conditions of the test power system validate the effectiveness of the proposed M-SSDC as well as its superior damping performance over conventional approaches.

Compensation-Based Distributed Model-Free Adaptive Control for Cyber-Attacks

In this work, three different types of cyber-attacks are considered together to develop a unified data-driven control method for a nonlinear networked multi-agent system bypassing any modeling processes. To this end, a distributed output is defined for every agent to show its relationship with the adjacent agents. Then, the nonlinear dynamics of the distributed output is transformed into a linear data model by using a dynamic linearization method, which is further used to predict the distributed output of the agent if the unconfined cyber-attack occurs. By introducing a stochastic variable, the compensated distributed output is reformulated to build a relationship between the actually measured one and the virtually predicted one. In the sequence, a compensation-based distribute model-free adaptive control (cDMFAC) is proposed to resist the unconfined cyberattacks. The convergence is proved rigorously in the sense of mathematical expectation. The simulation study further confirms the effectiveness of the proposed cDMFAC method.

Safe navigation of a quadrotor UAV with uncertain dynamics and guaranteed collision avoidance using barrier Lyapunov function

In this paper, the safe autonomous motion control of a quadrotor Unmanned Aerial Vehicle (UAV) is considered, in the presence of disturbance, stationary and moving obstacles. In this regard, we directly combine an analytical control design approach, within the backstepping framework, with obstacle avoidance to solve the navigation problem. A Barrier Lyapunov Function (BLF) is incorporated into the translational control to keep the vehicle out of a safety sphere, constructed around the obstacles, while steering it toward a desired position. BLF allows the direct inclusion of the obstacle position in the control design. This is achieved for both cases of known and unknown obstacle velocities. Furthermore, the issue of arbitrary initial conditions is analytically addressed, with a preassigned exit time from the safety sphere. We also consider the case of chance-constrained collision avoidance. The proposed approach leads to a computationally efficient design, since a closed-form of the control is obtained with no need for real-time optimization. More importantly, the analytical stability of closed-loop system is guaranteed. A hierarchical control structure is designed with an adaptive model-free control for unknown attitude dynamics in the presence of disturbances. A number of numerical simulations are performed to evaluate the effectiveness of the proposed approach.

Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault.

A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input-output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.

Compensation-Based Cooperative MFAILC for Multiple Subway Trains Under Asynchronous Data Dropouts

This paper researches the cooperative control problem for multiple subway trains under the asynchronous data dropouts in both measurement channel and downlink channel. The compensation-based cooperative model free adaptive iterative learning control (cCMFAILC) for the multiple city subway trains (MCSTs) is proposed to avoid deterioration of the control performance due to data dropouts. First, the nonlinear subway train system is transformed into an equivalent dynamic linearization data model to describe the input-output dynamics of the subway train system. Next, the lost data is replaced by the corresponding data of the same time instant in the latest available iteration. And the cCMFAILC is designed to guarantee that the speed tracking errors of MCSTs are bounded along the iteration axis and the headway of neighboring subway trains is stabilized in a safe range. Finally, theoretical analysis and MCSTs simulations verify the validity of the proposed cCMFAILC scheme.

Data-Driven Iterative Learning Predictive Control for Power Converters

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

Event-triggered model-free adaptive control for unknown multiple input and multiple output nonlinear system under denial of service attacks

The event-triggered model-free adaptive control problem for a class of multiple input and multiple output (MIMO) nonlinear system under denial of service (DoS) attacks is investigated in this paper. To economize the limited bandwidth resources, an event-triggered mechanism is designed between the sensor and controller. First, intermittent denial of service attack is modeled, which is described as limited by frequency and duration, without more specific assumptions about the structure of attacks strategies. Then, with the help of the dynamic linearization method, a novel event-triggered model-free adaptive control algorithm is designed. The predictive output increments in the algorithm are used to compensate for the influence of DoS attacks on the system. Subsequently, the stability of the system is analyzed by using the Lyapunov method to determine the convergence of the tracking error. Finally, two simulation examples illustrate the validity of the design.

Path tracking of autonomous tractor based on model-free adaptive control

Autonomous tractors, with path tracking as one of the key technologies, have become a research hotspot in recent years. However, the nonlinearity of and uncertainties in the autonomous tractor path tracking process can lead to challenges in terms of control. Although the model-based control method has been widely used in path tracking and achieved good results, only a limited number of model-free adaptive control (MFAC) methods have been developed for path tracking. This paper proposes an MFAC path tracking controller for autonomous tractors based on the use of a preview heading deviation method in which the preview heading angle is used as an input to control the steering, enabling the controller to achieve a small lateral error and stable control. In the simulation, the tracking effects of the designed controller, proportional integral differential (PID) and pure pursuit controllers at different speeds were compared. The results show that the proposed controller has good tracking ability and adaptability and can achieve high tracking accuracy. Finally, the tracking errors of the designed and PID controllers were compared in a real vehicle test, with the results verifying the effectiveness and practicality of the designed controller.

A data-driven model-free adaptive controller with application to wind turbines

A data-driven controller is presented in this paper, which stems from the well known model-free adaptive control approach based on an equivalent linearized dynamical model of the plant. Inspired by the recent paper (Liu and Yang, 2019), the output tracking problem is here solved by a data-driven adaptive sliding-mode controller simultaneously ensuring prescribed performance constraints. To allow a rigorous stability analysis, the sliding variable, and the consequently derived controller, have been redesigned with respect to the inspiring paper. A proper setting of the gain of the discontinuous term is shown necessary to ensure closed loop stability. Validation of the technique has been extensively performed on the well assessed high-fidelity tool FAST (NREL) to solve the efficiency maximization problem using the proposed approach for a 5 MW wind turbine operating in the medium wind speed region.

Consensus of multi-agent systems with heterogeneous unknown nonlinear switching dynamics: A dwelling time approach

This paper investigates the consensus problem of multi-agent systems with unknown heterogeneous nonlinear switching dynamics. First, an extended model-free adaptive controller is proposed to mitigate the effect of unknown switching dynamics. Then, an average dwelling time is provided to guarantee the stability of the multi-agent system and ensure consensus with both directed and undirected communication topologies. Finally, numerical examples are provided to show the impact of switching dynamics on the standard model-free adaptive controller. Then, the average dwelling time is calculated and added to ensure consensus in the presence of switching dynamics.