Adaptive Control Technique Research Articles

Adaptive constrained tracking control for dynamical actuator driven linear 2 × 2 hyperbolic PDE systems with nonlinear uncertainties

The paper develops an adaptive constrained tracking control technique for a class of 2 × 2 hyperbolic partial differential equation (PDE) systems with boundary actuator dynamics, which are described by a set of ordinary differential equations (ODEs) in the presence of unknown parametric nonlinearities. Since the control input only appears in the uncertain ODE subsystem rather than directly on the boundary of PDE subsystem, the control task becomes quite difficult and the existing direct boundary control approaches are ineffective. Moreover, in this paper, a more challenging problem is considered such that the controlled output and the states of ODE actuators are constrained. To this end, by utilizing finite and infinite dimensional backstepping techniques, barrier Lyapunov functions (BLFs) and adaptive methods, a novel adaptive tracking control approach is proposed. It is the first time that such a constrained tracking control problem is addressed for the PDE-ODE coupled systems considered in this paper. On the basis of the presented method, the rigorous theoretical proof is provided to show that the PDE controlled output and all the states of the ODE actuator stay within the predefined compact sets. Finally, the results are illustrated via a comparative numerical simulation.

State-observer-based asymptotic tracking control for electro-hydraulic actuator systems with uncertainties and unmeasurable velocity

In this paper, a velocity estimation-based adaptive dynamic surface asymptotic tracking control strategy for electro-hydraulic actuator (EHA) systems subjected to uncertainties and unmeasurable velocity is proposed. Firstly, an accurate velocity signal is estimated by constructing a new state observer, reducing the influence of measurement noise, system cost, and installation complexity. Moreover, the observer's robust gain is updated by an adaptive law, reducing the conservativeness of its selection. Then, a continuous friction model based on the desired velocity signal is used for feedforward compensation to alleviate the negative effect of estimation noise. Moreover, the adaptive dynamic surface control (ADSC) technique is incorporated to avoid “complexity explosion” and eliminate filtering errors via adaptive terms. Finally, the asymptotical stability of the closed-loop system is theoretically proved via Lyapunov analysis, while the efficacy of the proposed control strategy is experimentally validated and numerically verified.

Observer‐based event‐triggered security control for nonlinear networked control systems with cyber attacks

SummaryThis paper investigates the observer‐based security control problem for interval type‐2 nonlinear networked control systems with deception attacks and replay attacks. Firstly, to alleviate the communication burden of the network, a novel adaptive event‐triggered communication (ETC) mechanism is proposed. Compared with existing observer‐based adaptive ETC security control techniques, the threshold of the proposed adaptive ETC scheme can be adjusted dynamically depending on the value of adjustable parameters and estimation error. In particular, the adjustable parameters can change the sensitivity of the threshold flexibly. Then, taking the influence of the adaptive ETC mechanism and cyber attacks into consideration, an observer‐based security controller is developed under the co‐designed method. Furthermore, by utilizing the Lyapunov stability theory, sufficient conditions for asymptotically mean‐square stable of the closed‐loop system are obtained. Ultimately, the feasibility of the designed control approach can be proved via a simulation example.

Adaptive nonlinear second-order sliding mode synchronization control of direct drive H-type platform with zero error constraint

In order to achieve better synchronization performance for H-type platform servo systems during motion, an adaptive nonlinear second-order sliding mode synchronous control method with zero error constraint is proposed in this paper. The reason and mechanism of unsynchronized movement of H-type platform are analyzed. A synchronous thrust allocation algorithm based on dynamics is designed by using the thrust balance equation under zero error constraint to suppress the rotation movement of the crossbeam theoretically. To minimize the impact of external disturbances on the system, the system employs a robust adaptive nonlinear second-order sliding mode control method. In this method, the nonlinear sliding mode function is constructed to enhance the dynamic performance of the system, the second-order sliding mode surface is designed to achieve chatter-free control input. To address the issue of limitations in sliding mode control caused by upper limits of external disturbances, an adaptive control technique is employed to estimate and compensate for uncertainties such as external disturbances. Experimental results demonstrate that the proposed synchronous control strategy can effectively adjust the operating conditions of the two motors, improve the synchronous control performance and enhance the anti-interference ability of the system.

An adaptive fuzzy control technique for a high‐speed vehicular platoon experiencing communication delays

AbstractVehicle platooning is seen as an enabler to improve driving safety, traffic congestion and stress‐free driving. A platoon is a highly non‐linear system and fuzzy control is able to deal with those systems. This paper presents a pioneering solution to the complex problem of vehicle platooning. Our adaptive controller seamlessly integrates fuzzy logic with conventional PID control technology, thereby ensuring safe and efficient travel for vehicles within a platoon. The key objectives of this controller encompass the maintenance of a consistent inter‐vehicle time gap and the consideration of time‐varying parameter uncertainties and communication delays between vehicles. It provides mathematical proof of the stability of the platoon formation using the Routh–Hurwitz theorem and a set of equations that describe the platoon's overall performance. Simulation results show that the proposed controller is effective in controlling vehicle platoons, even in the presence of random communication delays, resulting in improved driving safety and reduced traffic congestion.

Three-dimensional trajectory tracking guidance against near-space maneuvering targets with multiple constraints under wind field

This paper presents a three-dimensional disturbance observer-based adaptive tracking guidance scheme for the trajectory tracking problem of a missile intercepting a hypersonic vehicle with the wind field, autopilot lag dynamics, and input saturation. Unlike most of the existing missile trajectory tracking guidance systems without velocity controllability and external wind field model, the wind field model is added to the dynamic model, and the velocity control channel is introduced into the three-dimensional trajectory tracking guidance problem to prevent impractical scenarios and increase tracking precision. The proposed tracking guidance methodology incorporates the sliding mode and adaptive control techniques, in which the sliding mode motion is finite time stable independently of the tracking guidance system's initial conditions. Also, the super twisting switch control law is added to cope with the chattering problem. In this context, external uncertainty disturbances and unmeasured states are lumped into lump disturbance and estimated using the backpropagation neural network extended state observer. To this end, numerical simulations demonstrate the tracking to reach zero between 3 s and 5 s against various uncertainties in engineering practical, and position tracking errors are within 0.15 m, superior to the existing tracking guidance methods.

Deep reinforcement learning-based control of chemo-drug dose in cancer treatment

Background and objectiveAdvancement in the treatment of cancer, as a leading cause of death worldwide, has promoted several research activities in various related fields. The development of effective treatment regimens with optimal drug dose administration using a mathematical modeling framework has received extensive research attention during the last decades. However, most of the control techniques presented for cancer chemotherapy are mainly model-based approaches. The available model-free techniques based on Reinforcement Learning (RL), commonly discretize the problem states and variables, which other than demanding expert supervision, cannot model the real-world conditions accurately. The more recent Deep Reinforcement Learning (DRL) methods, which enable modeling the problem in its original continuous space, are rarely applied in cancer chemotherapy. MethodsIn this paper, we propose an effective and robust DRL-based, model-free method for the closed-loop control of cancer chemotherapy drug dosing. A nonlinear pharmacological cancer model is used for simulating the patient and capturing the cancer dynamics. In contrast to previous work, the state variables and control action are modeled in their original infinite spaces to avoid expert-guided discretization and provide a more realistic solution. The DRL network is trained to automatically adjust the drug dose based on the monitored states of the patient. The proposed method provides an adaptive control technique to respond to the special conditions and diagnosis measurements of different categories of patients. Results and conclusionsThe performance of the proposed DRL-based controller is evaluated by numerical analysis of different diverse simulated patients. Comparison to the state-of-the-art RL-based method, which uses discretized state and action spaces, shows the superiority of the approach in the process and duration of cancer chemotherapy treatment. In the majority of the studied cases, the proposed model decreases the medication period and the total amount of administrated drug, while increasing the rate of reduction in tumor cells.

Fuzzy adaptive resilient control against unknown false data injection attacks for high-order nonlinear systems with actuator failures

An adaptive resilient control technique for high-order nonlinear systems with actuator failures is presented in this work. The unknown false data injection (FDI) attacks make all controlled system states unavailable. To counteract this negative impact of FDI attacks, a new coordinate transformation is constructed during the procedure of recursive design. Moreover, the effect of the actuator faults is completely eliminated by skillfully designing the controller and the adaptive laws. On the basis of the backstepping technology and the Nussbaum gain technique, an adaptive resilient control strategy is presented by using compromised state variables, which ensures all the signals of the closed-loop system is bounded when the sensor is attacked. Compared with the existing results, the proposed control approach not only solves the resilient control problem of high-order nonlinear systems whose powers are positive odd numbers, but also eliminates the assumption that the attack weights are positive. In the end, a simulation result is presented to verify the feasibility of the designed scheme.

Adaptive neural dynamic surface composite control for path following of underactuated vessels with fixed-time convergence

The article discusses the path following control issue for underactuated marine surface vessels (MSVs) subject to nonlinear uncertainties. Firstly, the tracking goal of MSVs will be changed from the earth-fixed position to the line-of-sight (LOS) angle with the aid of LOS approach. To deal with the nonlinear uncertainties existing in MSVs, radial basis function neural networks (RBFNNs) are utilized. In particular, the fixed-time serial–parallel estimation models are used to boost the approximation of RBFNNs and determine whether RBFNNs really estimate the unknown nonlinearities by using the forecast and the track biases to change the weight of RBFNNs. Then, a fixed-time filter is produced to handle the ‘explosion of complex’ issue and improve the structure of the developed controller. In addition, the function tanh with smooth derivative function is utilized to overcome the singularity issue of the virtual controller derivations caused by fixed-time control. Finally, an adaptive neural fixed-time dynamic surface composite control method is developed for the path following control problem based on the adaptive backstepping control technique. The stability analysis results show the yaw angle ψ converges to the LOS angle ψs when t≥TS where Ts given later is not determined by the system initial variables. The system internal variables also reach a bounded compact set, which indicates the RBFNNs indeed estimate the uncertainties existing in MSVs by using fixed-time serial–parallel estimation models. And comparation simulation results also certificate the utilizability of the developed controller.

Fixed-Time Prescribed Performance Consensus Control for Multiagent Systems With Nonaffine Faults

This paper studies the fixed-time consensus tracking control of nonlinear multi-agent systems (NNMASs) suffering from non-affine faults. A fixed-time command filter is constructed to solve the “explosion of complexity” issue, and a novel fixed-time error compensation mechanism is designed to eliminate filtering errors. The adaptive fuzzy control technique is introduced to deal with the unknown nonlinear terms containing non-affine fault functions. A new fixed-time fault-tolerant control scheme based on the modified prescribed performance technology is proposed for the NNMASs, which ensures that the closed-loop system satisfies the practical fixed-time stability. In addition, the consensus tracking errors of the NNMASs converge to a given range within a prescribed performance bound in a fixed time. Finally, comparative simulation results show the effectiveness of the proposed method.

Performance analysis of digitally controlled nonlinear systems considering time delay issues

In this paper, a comprehensive investigation into discretization, effective sample time selection considering delays in the system, and time and frequency domain analysis of a DC-DC buck converter, which plays a vital role in photovoltaic (PV) systems, is conducted to enhance the understanding of their dynamic behavior, optimize control algorithms, improve system efficiency, and ensure reliable power conversion in photovoltaic applications. To effectively address the non-linear behavior and enhance digital control of a buck converter by selecting the best sample time, several approaches can be employed. These include accurate modeling and identification of non-linear elements, development of advanced control algorithms that account for non-linearities, implementation of adaptive control techniques, and utilization of feedback mechanisms to compensate for deviations from linearity. By considering and mitigating the non-linear behavior, digital control systems can achieve improved accuracy, stability, and transient behavior in regulating the buck converter's output waveforms (voltage or current). The results of the study demonstrated that the trapezoidal integration method which is also known as bilinear approximation, or Tustin's approach outperformed other commonly used discretization methods, such as first-order hold (FOH), zero-order hold (ZOH), impulse response matching (impulse invariant), and matched pole-zero (MPZ) technique, in dual-domain (both time and frequency) analysis. The key finding highlighting the superiority of the bilinear approximation was its ability to achieve the closest match in the frequency domain bridging the continuous-time and discrete systems. This finding emphasizes the significance of the bilinear approach in preserving the frequency characteristics of the original continuous-time system during discretization. By employing this method, the discrete system closely approximated the behavior of its continuous-time counterpart, ensuring accurate frequency-domain representation.

Synchronization of chaotic satellite systems with an unknown parameter

Spacecraft (satellite) formation has several applications in space exploration. Synchronization controllers are essential in satellite formation systems. This article proposes a novel robust adaptive synchronization control (RASC) technique for two identical three-dimensional chaotic satellite systems attitude synchronization with an unknown system parameter, unknown bounded model uncertainties, unknown external disturbances, and nonlinear terms. This controller achieves smooth error vector convergence to zero. The closed-loop has a robust performance as the controller avoids plant nonlinear terms cancellation. This controller has two parts. (i) A nonlinear controller for keeping the closed-loop stable; it flourishes robust and faster error vector convergence to zero with reduced fluctuations. (ii) An adaptive nonlinear controller that compensates exogenous disturbances and model uncertainties using online estimations of model uncertainties and bounds. The Lyapunov stability theory assures the closed-loop's global asymptotic robust stability. Computer simulation results-based analysis verifies the theoretical findings. The article includes a comparative study and discusses the control effort saturation effects.

Improved finite-time prescribed performance based adaptive neural control for nonlinear systems with sensor faults

In this article, based on a novel finite-time prescribed performance function, we consider the issue of the adaptive neural fault-tolerant tracking control problem for a type of nonlinear system with sensor faults. The control performance of the system may be affected when sensor faults occur. For the controlled system with sensor faults, an improved finite-time performance function is proposed so that its initial conditions do not need to be set in advance. Compared with existing performance functions, the performance functions in this paper can ensure that the system is always controllable, even if the sensor faults occur suddenly during the steady operation. Moreover, Radial basis function (RBF) neural networks (NNs) are employed to estimate the uncertain smooth nonlinear functions. With the help of the adaptive backstepping control technique, an improved adaptive prescribed performance control technique is developed, which can realize the boundedness of every signal in the closed-loop system, and the tracking error can be limited within the neighborhood near the origin. Simulation results demonstrate the effectiveness of the proposed control scheme.

Fault-compensation-based boundary control for hyperbolic PDEs: An adaptive iterative learning scheme

In this paper, a fault-compensation-based boundary control strategy is developed for a class of distributed parameter systems expressed by second-order hyperbolic Partial Differential Equations (PDEs), in the case of actuator faults. Considering multiplicative actuator faults and additive actuator faults simultaneously, we put forward a hierarchical fault compensation scheme, i.e., an adaptive iterative learning boundary control technique. Lyapunov-Like analysis method and a variation of Wirtinger’s inequality are used to design a boundary control scheme with an adaptive law, guaranteeing the asymptotical stability of the closed-loop hyperbolic PDE system with multiplicative faults. An iterative learning term is proposed to boost the performance of the system under both multiplicative faults and additive faults, and a composite energy function is used in analysis. With the proposed fault-compensation-based adaptive iterative learning boundary control technique, multiplicative faults are redeemed towards the time horizon and additive faults are compensated towards the iteration horizon. An active acoustic noise reduction process is presented to illustrate the merit and effectiveness of the designed adaptive iterative learning boundary control technique.

Virtual Coupling-Based H∞ Active Fault-Tolerant Cooperative Control for Multiple High-Speed Trains With Unknown Parameters and Actuator Faults

This paper investigates the cooperative control problem of multiple high-speed trains subject to unknown parameters and actuator faults. The cooperative operation of multiple high-speed trains is modeled as a coupled control system. On the basis of the virtual coupling technique, the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> active fault-tolerant cooperative control scheme is established to guarantee the desired tracking performances and cooperative performances. The adaptive control technique is adopted to estimate the unknown Davis’ coefficients and the fault parameters and linear matrix inequalities are utilized to derive the control gain of the cooperative control protocols for the trains. Rigorous analysis is provided to corroborate the closed-loop stability of the cooperative train control system and numerical simulation is conducted to verify the effectiveness and feasibility of the proposed results.

Design of Adaptive Fuzzy Fixed-Time HOSM Controller Subject to Asymmetric Output Constraints

This paper is concerned with the control design of adaptive fuzzy fixed-time high-order sliding mode (HOSM) under asymmetric output constraints. The main objective of the paper is to construct a control scheme to build the HOSM in a fixed time and fulfill the pre-set constraint condition. For this purpose, initially to conquer this obstacle regarding the output constraint, a barrier Lyapunov function is developed to keep the output variable within the predefined constraint during operation. Then, the unknown functional bounds of uncertainties in the considered system are modeled by invoking fuzzy logic systems. With the integrated use of adaptive fuzzy control and backstepping-like techniques, a novel protocol for the fixed-time HOSM control with asymmetric output constraints is developed. Through the strictly theoretical clarification, the HOSM is fixed-time established under the suggested control strategy. The effectiveness of the derived results is eventually testified by some comparative simulation results.

Optimizing the initial weights of a PID neural network controller for voltage stabilization of microgrids using a PEO-GA algorithm

This paper proposes an adaptive PID neural network (PIDNN) controller for direct and quadrature voltage control of three-phase inverters for islanded microgrids. A hybrid metaheuristic optimization algorithm is proposed for the initial weight selection of the proposed PIDNN controller using a discrete-time simulation model incorporating the nonlinearity of the insulated gate bipolar transistor (IGBT) or metal oxide semiconductor field effect transistor (MOSFET) switches of the inverter. The proposed hybrid optimization (HO) approach is a combination of population extremal optimization (PEO) and genetic algorithm (GA). It turned out that the proposed HO-PIDNN control scheme excelled the PEO, GA, and particle swarm optimization-based PIDNN controllers in terms of the objective function value, which is composed of the integral time absolute tracking error of the output voltage and a chattering penalty factor. Also, the control system outperformed the model reference adaptive PID control technique from the literature.

L1 Adaptive Control for Marine Structures

Nowadays, many maritime structures require precise dynamic positioning (DP) of the constructive elements that compose them. In addition, the use of preconstructed elements that are later moved to the final location has become widespread. These operations have not been automated with the risks involved in carrying out the complex operations required. To minimize these operational risks and to perform a correct DP of floating structures, a new approach based on the L1 adaptive control technique is proposed. As an example of application, a proposed L1 adaptive controller was implemented in the dynamic positioning of a floating caisson. Several simulations of the system with wave disturbances were carried out, and the results were compared with those obtained by applying other classical and advanced control techniques, such as linear quadratic Gaussian control (LQG) and model predictive control (MPC). It was concluded that the proposed L1 adaptive controller performs correct dynamic positioning and reduces the tension generated on the lines concerning the other advanced control techniques with which it was compared. This reduction in line tension leads to an important improvement due to the possibility of reducing the size of the actuators or reducing their number, with the important economic and safety repercussions that these actions entail.

Climatic sensorless maximum power point tracking based on adaptive neuro-extremum seeking control technique in PV generation systems

Climatic sensorless maximum power point tracking based on adaptive neuro-extremum seeking control technique in PV generation systems

Event-triggered fully distributed asymptotic consensus for leaderless multiple Euler-Lagrange systems

In this paper, the fully distributed consensus for a class of multiple Euler-Lagrange systems is investigated, where the protocol is designed under the event-triggered control framework and the dynamics of Euler-Lagrange systems are heterogeneous. Since only local information interactions at triggered instants can be used and the Euler-Lagrange systems are of relatively complex dynamics, it is challenging to achieve asymptotic consensus without using any global information (such as the Laplacian matrix information). By skillfully integrating the adaptive control, distributed control and event-triggered control techniques, a novel protocol is proposed for the investigated multiple Euler-Lagrange systems. It is proven that the asymptotic consensus can be achieved by the developed protocol. By a numerical example, the effectiveness of the developed protocol is illustrated.