Adaptive Control Scheme Research Articles

Research on Integrated Control Strategy for Wind Turbine Blade Life.

Wind turbine blades bear the maximum cyclic load and varying self-weights in turbulent wind environments, which accelerate the propagation of cracks that ultimately progress from minor faults, resulting in blade failure and significant maintenance and shutdown costs. To address this issue, this paper proposes an adaptive control strategy for the blade's useful life. The control system is divided into the inner control loop and the outer control loop. The outer loop is based on the Paris crack propagation model combined with a particle filtering algorithm and calculates the degradation of the blade life under the crack threshold conditions provided by the operation and maintenance strategy to determine the parameter settings of the inner-loop load-shedding controller. The control strategy we propose can balance the load-shedding capability of the controller with the fatigue load of the pitch actuator while considering the predefined remaining useful blade life in the operation and maintenance strategy, avoiding unplanned downtime and reducing maintenance costs.

Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy

Automatic generation control of is-landed micro-grid using integral reinforcement learning-based adaptive optimal control strategy

Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids

A SOC balancing adaptive droop control scheme for multiple energy storage modules in DC microgrids

Robust Platoon Control of Mixed Autonomous and Human-Driven Vehicles for Obstacle Collision Avoidance: A Cooperative Sensing-Based Adaptive Model Predictive Control Approach

Obstacle detection and platoon control for mixed traffic flows, comprising human-driven vehicles (HDVs) and connected and autonomous vehicles (CAVs), face challenges from uncertain disturbances, such as sensor faults, inaccurate driver operations, and mismatched model errors. Furthermore, misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’ perception and platoon safety. In this paper, we develop a two-dimensional robust control method for a mixed platoon, including a single leading CAV and multiple following HDVs that incorporate robust information sensing and platoon control. To effectively detect and locate unknown obstacles ahead of the leading CAV, we propose a cooperative vehicle–infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV. This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner. Additionally, we propose a distributed car-following control scheme with robustness to guarantee the following HDVs, considering uncertain disturbances. We also provide theoretical proof of the string stability under this control framework. Finally, extensive simulations are conducted to validate our approach. The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers, significantly reduce the mean-square deviation in obstacle sensing, and approach the theoretical error lower bound. Moreover, the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.

An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems

The Snow Ablation Optimizer (SAO) is an advanced optimization algorithm. However, it suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose an Enhanced Snow Ablation Optimization algorithm (ESAO). Initially, an adaptive T-distribution control strategy is employed to improve the algorithm's exploratory position adjustments, facilitating the identification of the global optimum. Furthermore, we introduce a Cauchy mutation strategy, endowing individuals with a robust capability to escape local extrema and steer the population towards more favorable directions. A leader-based boundary control strategy is also proposed to enhance the optimizer's search performance, significantly increasing the accuracy, speed, and stability of the algorithm in tackling complex problems. To validate the performance of ESAO, we utilize 29 CEC2017 benchmark functions for comparison against eight popular algorithms across various dimensions. Our algorithm ranked first in all comparisons, demonstrating ESAO's effectiveness. Additionally, to evaluate the practical applicability of the proposed method, we mathematically modeled the UAV swarm and solved the UAV swarm path planning problem using various competitor algorithms. Furthermore, we applied different competitor algorithms to two engineering design problems. The results demonstrate that ESAO performs the best. In general, ESAO outperforms its counterparts in terms of solution quality and stability, showcasing its superior application potential.

Decentralized Adaptive Secure Control of Uncertain Nonlinear Time-Varying Interconnected Systems Against Sensor and Actuator Attacks.

In this article, the decentralized adaptive secure control problem for cyber-physical systems (CPSs) against deception attacks is investigated. The CPSs are formed as a type of nonlinear interconnected strict-feedback systems with uncertain time-varying parameters. The attack affects the information transmission between sensor and actuator in a multiplicative manner. A novel decentralized adaptive backstepping secure control strategy is established by exploiting a particular kind of Nussbaum functions and a flat-zone Lyapunov function analysis approach. It is shown that all of closed-loop signals remain globally bounded, and each output signal eventually converges into a small neighborhood of the origin. Simulation results on an illustrative example are provided to display the effectiveness of the proposed control scheme.

Cooperative Adaptive Command Filtered Backstepping Control for EVs to UPS-Microgrid via Virtual Synchronous Generator.

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

Adaptive Control Strategies for Enhanced Integration of Solar Power in Smart Grids Using Reinforcement Learning

Integrating solar power into smart grids is challenging because of the variable nature of solar energy. This study focuses on implementing reinforcement learning (RL) using a Deep Q-Network algorithm to enhance the stability and efficiency of a grid. A custom environment was designed using OpenAI Gym, in which real-time simulation of grid operations was conducted using real-time data on solar power, weather, and other grid metrics. The trained RL agent exhibited high predictability in optimally distributing the load and managing the battery storage, with R-squared = 0.886, mean average error = 1,173,046.55 Wh, and root mean squared error = 2,075,515.10 Wh. The model effectively captured the seasonality and daily variations in solar power generation. Forecasting using the proposed model provides insights into future energy trends and uncertainties. The reward function will be further refined and scaled for more complex energy systems by incorporating additional variables and hybrid approaches. This study highlights the potential of RL-based adaptive control strategies for developing more efficient and resilient integration of renewable energy sources into smart grids.

Fault-tolerant control of underactuated unmanned surface vessels in presence of deception and injection attacks

This paper studies the trajectory tracking control problem of underactuated unmanned surface vessels (USVs) under the influence of internal and external uncertainties, actuator faults, and injection and deception attacks. In the control design, we separately designed virtual adaptive laws to compensate for the time-varying gains suffered by the kinematics channel and combined the event-triggered control (ETC) mechanism to establish an online approximator to compensate for the time-varying gains of the kinematics channels and the loss-of-effectiveness (LOE) and bias faults suffered by the system. Combining robust neural damping technology and finite-time disturbance observer (FTDO), the dynamic uncertainties and external disturbances are suppressed. Finally, a novel robust adaptive trajectory tracking control scheme is proposed. The scheme achieves active compensation for heterogeneous uncertainties including internal and external uncertainties, actuator faults, and attack signals. The Lyapunov stability theory analysis shows that all signals of the tracking system are bounded. The simulation results prove the effectiveness of this control scheme.

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Adaptive optimal control strategy of fuel economy for fuel cell battery storage system using in HEV applications

Adaptive optimal control strategy of fuel economy for fuel cell battery storage system using in HEV applications

Research on active-passive integrated vibration isolator based on metal rubber and piezoelectric actuator

Abstract The paper introduces a novel active-passive integrated vibration isolator based on metal rubber and piezoelectric actuator, along with an adaptive active vibration control strategy. The active control strategy employs the adaptive dynamic step filtered-x normalized least mean squares algorithm, allowing the step size to adaptively adjust with the error. The secondary control path of the algorithm is modeled using the enhanced rate-dependent Prandtl–Ishlinskii model and the auto-regressive with extra inputs model. The active-passive integrated vibration isolator achieves broadband vibration isolation from 10 to 200 Hz. Compared to passive isolation, the transmissibility decreases from 0.99 to 0.056 at 10 Hz, from 3.02 to 0.068 at the resonance frequency, and from 0.057 to 0.046 at 200 Hz. This study provides a theoretical and experimental foundation for the design of a novel, broadband, and efficient active-passive integrated vibration isolator structure and active control method.

Neural learning control for sampled‐data nonlinear systems based on Euler approximation and first‐order filter

AbstractThe primary focus of this research paper is to explore the realm of dynamic learning in sampled‐data strict‐feedback nonlinear systems (SFNSs) by leveraging the capabilities of radial basis function (RBF) neural networks (NNs) under the framework of adaptive control. First, the exact discrete‐time model of the continuous‐time system is expressed as an Euler strict‐feedback model with a sampling approximation error. We provide the consistency condition that establishes the relationship between the exact model and the Euler model with meticulous detail. Meanwhile, a novel lemma is derived to show the stability condition of a digital first‐order filter. To address the non‐causality issues of SFNSs with sampling approximation error and the input data dimension explosion of NNs, the auxiliary digital first‐order filter and backstepping technology are combined to propose an adaptive neural dynamic surface control (ANDSC) scheme. Such a scheme avoids the ‐step time delays associated with the existing NN updating laws derived by the common ‐step predictor technology. A rigorous recursion method is employed to provide a comprehensive verification of the stability, guaranteeing its overall performance and dependability. Following that, the NN weight error systems are systematically decomposed into a sequence of linear time‐varying subsystems, allowing for a more detailed analysis and understanding. In order to ensure the recurrent nature of the input variables, a recursive design is employed, thereby satisfying the partial persistent excitation condition specifically designed for the RBF NNs. Meanwhile, it can verify that the NN estimated weights converge to their ideal values. Compared with the common ‐step predictor technology, there is no need to redesign the learning rules due to the designed NN weight updating laws without time delays. Subsequently, after capturing and storing the convergence weights, a novel neural learning dynamic surface control (NLDSC) scheme is specifically formulated by leveraging the acquired knowledge. The introduced methodology reduces computational complexity and facilitates practical implementation. Finally, empirical evidence obtained from simulation experiments validates the efficacy and viability of the proposed methodology.

Adaptive neural optimal tracking control for uncertain unmanned surface vehicle

This article proposes a novel adaptive neural optimal control scheme for the uncertain unmanned surface vehicle (USV) system. The proposed adaptive neural optimal control scheme is composed of a neural feedforward controller and a neural optimal feedback controller. The neural feedforward controller is designed by using neural networks (NNs) and the backstepping control design technique to make the unknown nonlinear dynamics of USV system to be stabilized. The neural optimal feedback controller is designed based on adaptive dynamic programming (ADP) theory. It is proved that the formulated adaptive neural optimal control scheme can achieve all signals in USV system are uniformly ultimately bounded (UUB) and the cost function is minimized. The simulation and comparison results demonstrate the effectiveness of the proposed optimal control scheme.

A state-constrained safety adaptive trajectory tracking control scheme for a hovercraft with enhanced prescribed performance

A state-constrained safety adaptive trajectory tracking control scheme for a hovercraft with enhanced prescribed performance

Two-layer optimal scheduling method for regional integrated energy system considering flexibility characteristics of CHP system

A regional integrated energy system (RIES) guarantees the fulfillment of a diversified load demand of users by coordinating all types of energy equipment and is a two-layer optimal scheduling method that takes into consideration the flexibility of the characteristics of the combined heat and power (CHP) system is proposed in this paper. First, the thermoelectric output characteristics of hydrogen fuel cell (HFC) and CHP units are analyzed to establish an energy equipment model that takes into consideration the variability of the operating conditions. Subsequently, a two-layer optimal scheduling model of the RIES is established, where the upper layer takes into consideration the operating costs, carbon emission penalties, and renewable energy consumption capacity to determine the strategy for allocating the energy flow, and the lower layer considers the energy efficiency as the optimization objective to determine the output modes of the HFC and CHP units. Finally, an adaptive closed-loop control strategy is used to avoid the continuous startup and shutdown of the CHP unit. The simulation results show that the proposed method can effectively reduce operating costs and carbon emissions, improve energy efficiency and renewable energy consumption, and promote the efficient utilization of integrated energy resources.

Research on adaptive impedance control strategy for humanoid walking in unstructured dynamic environment

Research on adaptive impedance control strategy for humanoid walking in unstructured dynamic environment

Adaptive generic model control scheme for an optimized external heat integrated air separation column using unscented kalman filter

An External Heat-Integrated Air Separation Column (E-HIASC) process is a promising air separation technology. This study focuses on the operational stability of the optimized E-HIASC process for separating nitrogen, oxygen, and argon mixtures. The operation stability of process is achieved through an Adaptive Generic Model Control (AGMC) scheme which is designed by incorporating the identified E-HIASC state-space dynamic model into the controller algorithm. The controller synthesizes the Generic Model Control (GMC) algorithm, decoupled ARX model, and Unscented Kalman Filter (UKF) algorithm to enable the auto-regression and exogenous (ARX) for model identification and the UKF algorithm to estimate time-varying parameters and compute unmeasured E-HIASC state parameters required in the GMC algorithm. A Generic Model Control (GMC) and Multivariable PID (M-PID) control schemes were also designed for benchmarking study. Simulation results show that an AGMC scheme performs better than the GMC and M-PID schemes in tracking the product concentration set point and disturbances rejection.

A Lyapunov-based adaptive control strategy with fault-tolerant objectives for proton exchange membrane fuel cell air supply systems

Proton exchange membrane fuel cells (PEMFCs) are being extensively studied for large-scale applications. The need for reliable and safe system integration processes emphasizes the importance of health-conscious control research. Considering their high-frequency operation and load variations, the reliability of PEMFCs can be compromised by mechanical failures within air supply systems. Therefore, this paper proposes an adaptive control strategy with fault-tolerant objectives for regulating the oxygen excess ratio (OER). Specifically, it addresses two common faults within air compressors, namely compressor overheating and increased mechanical friction. While these faults have been studied in fault diagnosis research, their treatment within the context of fault-tolerant control is relatively uncommon. To ensure reliable OER regulation under multiple fault occurrences, estimator-based parameter adaptation laws are firstly derived using Lyapunov theory during the stability demonstration process. Then, the baseline controller is designed using an extended state observer and feedback linearization, with a clear presentation of the existence and stability of the state transformation. Furthermore, extensive numerical tests are conducted to demonstrate the effectiveness of the proposed control strategy. The proposed adaptive control strategy ensures consistent regulation outcomes without compromising performance under healthy conditions, highlighting its potential for large-scale application as a standard control approach in practical applications.