Adaptive Control Method Research Articles

Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory

AbstractThis paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.

Robust data-driven model-free adaptive control to uncertainties and disturbances for NARMAX systems

This paper introduces an enhancement to the model-free adaptive control (MFAC) method based on a new control input cost function for full-form dynamic linearization (FFDL) for NARMAX systems. The new control cost function includes the one-step-ahead tracking error, OER, and output error integral. The main feature of the approach is that it has a high ability to overcome disturbances in the control input and measured noise in the output. Analytical and extensive simulations have demonstrated that the novel control law exhibits superior response and robustness in comparison to the prototype MFAC. Moreover, it guarantees stability and convergence of the tracking error by enforcing bounded-input bounded-output (BIBO) criteria. In addition, 20 Monte Carlo simulations were conducted to test the initial conditions and random control parameters, and the results demonstrated that the proposed control outperformed the prototype MFAC.

Adaptive control of airway pressure during the expectoration process in a cough assist system

Existing Mechanical Insufflation-Exsufflation (MI-E) devices often overlook the impact of cough airflow pressure on mucus clearance, particularly lacking in control over airway pressure during the expiratory phase, which can lead to airway collapse and other types of airway damage. This study optimizes the design of cough assist system and explores the effectiveness of PID and adaptive control methods in regulating airway pressure. The adaptive control method compensates for hose pressure drop by online estimation of the ventilatory hose characteristics. It achieves precise tracking of target pressure and ensures the generation of peak flow rates effective for mucus clearance, even in the absence of known patient lung physiological states and unknown hose leakage parameters. Through a series of comparative experiments, this paper confirms the significant advantages of adaptive control in reducing oscillations and overshoot, capable of more stable and precise airway pressure adjustments. This improved control strategy not only enhances clinical safety but also significantly improves therapeutic outcomes and reduces the risk of complications. The findings indicate that the revamped cough assist system, employing an adaptive control strategy, can effectively prevent airway damage during assisted coughing, offering a safer and more effective sputum clearance solution for critically ill patients with expectoration disorders.

Improved adaptive robust control of Direct-Drive Pump-Valve cooperative Brake-by-wire unit with disturbance compensation

Abstract Aiming at the needs of high-level autonomous driving, a direct-drive pump-valve coordinated brake-by-wire unit was designed. To improve the response speed, control accuracy, and robustness of the brake-by-wire unit, an improved adaptive robust control method (AIRC-AF) based on a novel adaptive law and disturbance compensation was proposed. By integrating pressure tracking error information and overall parameter estimation error information, a novel parameter adaptive law with fast convergence speed was designed. A finite-time disturbance observer was designed to observe the overall system disturbance, improving the system's anti-disturbance performance. Finally, the stability of the control method was proven using the Lyapunov stability proof method. The results show that under sinusoidal target signals and triangular wave target signals, AIRC-AF has better response speed and control accuracy. In conditions such as increased permanent magnet temperature, increased coil resistance, or reduced flow area due to hydraulic pipeline blockage, AIRC-AF has better adaptability. The unit and the method can meet the demand for precise braking force control in high-level autonomous driving.

Adaptive multimodal control of trans-media vehicle based on deep reinforcement learning

To solve the problem that the control system is prone to instability due to the sudden change of physical characteristics, strong interference, and nonlinear in the process of multimodal movement of trans-media vehicle, an adaptive control method combining the Deep Deterministic Policy Gradient (DDPG) and traditional Proportional-Integral-Derivative (PID) controller is proposed in this paper. In this approach, the upper-level DDPG controller continuously monitors the vehicle's state and environmental conditions, dynamically adjusting the PID parameters in real-time. The lower-level PID controller then utilizes these updated parameters to modulate the output thrust of the vehicle's motors, thereby achieving excellent control over the vehicle's entire movement. Firstly, according to the hydrodynamic analysis, the kinematics and dynamics mathematical model of the self-designed trans-media vehicle is constructed. This model includes the multi-stage motion modal process of aerial flight, underwater navigation, and cross-media motion, which is suitable for the simulation and verification of the control method. Then, an adaptive controller called RL-PID combining DDPG and PID is built, so that PID can adjust parameters in real-time according to the changes in the external environment. Finally, after theoretical stability proof, a comparison study is performed across three approaches, namely the novel RL-PID, Fuzzy PID, and PID. The experimental results illustrate the superiority of the proposed approach over the competing ones and the generalization of the proposed approach under different interference.

Flight Control Based on Adaptive Output Feedback for Quadrotors Using Quaternions

Quadrotors are expected to play an active role in out-of-sight areas such as equipment inspection and transportation of supplies to disaster areas. However, ensuring stable and highly accurate automatic flights has become a significant challenge. Trajectory tracking control methods that combine adaptive output feedback control based on almost strictly positive real characteristics and backstepping strategy have been proposed for a quadrotor control system with nonlinearities. These methods have simple structures and are robust. However, existing adaptive control methods have the problem of singularities, owing to the use of Euler angles in the modeling of quadrotors. In this paper, we propose an adaptive control method that enables a wider range of attitude changes using quaternions. The effectiveness of the proposed method is validated through numerical simulations.

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Adaptive control of an amplified piezoelectric-driven micropositioning stage based on notch filter structure inspiration and neural network online tuning

Abstract Piezoelectric-driven flexure micro/nanopositioning stages often exhibit a low-damping resonance mode, which can easily excite mechanical resonance during high-speed movement, and significantly impact the control system's stability, control bandwidth, and trajectory tracking accuracy. To mitigate the reliance on the precise modeling of stage dynamics inherent in current resonant controllers, an adaptive control method based on a back propagation (BP) neural network was designed to suppress resonance in real-time. First, a piezoelectric-driven flexure micro/nanopositioning stage system was constructed. Next, a feedback controller similar to a notch filter was designed, with bilinear transformation applied based on the system's inherent parameters to determine the initial values. Finally, the designed adaptive control method was tested through trajectory tracking experiments using a triangular wave signal. The experimental results showed that, when tracking the triangular wave signal, the maximum tracking error was reduced by 72.66% compared to proportional-integral (PI) control alone and by 68.66% compared to proportional integral control combined with a traditional notch filter. The tracking results demonstrate a significant improvement in the stage's stability and tracking accuracy.

Neuroadaptive Sliding Mode Tracking Control for an Uncertain TQUAV With Unknown Controllers

ABSTRACTIn this article, a neuroadaptive sliding mode control (NSMC) strategy based on recurrent neural network (RNN) for robustly and adaptively tracking the desired position and attitude of an uncertain tilting quadrotor unmanned aerial vehicle (TQUAV) with unknown controllers is presented. The main contribution of this article is the real‐time adjustment of unknown flight controllers using the approximation characteristics of RNN, in which the derived approximation errors of RNN are sufficiently estimated by adaptive control method that can reduce or eliminate the impact of error terms on the evolution of closed‐loop systems. Especially, Lyapunov stability analysis is greatly simplified compared to existing methods and does not require amplification or reduction. Finally, the superior performance of the NSMC strategy was verified by comparing simulation results.

Research on adaptive hydraulic drive optimization control of concrete mixing tank truck for open-pit mine.

The non-axisymmetric problem caused by the fluid sloshing in the tank of a mining concrete mixing tank truck during driving is affected by the excitation of complex road surfaces. The fluid sloshing is coupled with the dynamics of the vehicle body due to the excitation of the complex road surface. The traditional hydraulic drive proportional integral differential (PID) control method is not effective in dealing with such problems, which can easily lead to accidents such as overturning. To improve the accuracy and stability of the hydraulic drive control system, this paper proposes an optimized particle filter PID adaptive control method based on the elastic firefly (FA) algorithm to accelerate the convergence speed of control parameter optimization, and then analyzes its hydraulic drive control characteristics and structural applications, and discusses step steering and double lane change modes are simulated under filling rates of 1.5 and 2.0, respectively. The experimental results show that compared with traditional PID control, the proposed adaptive control method can significantly reduce the average speed error of hydraulic drive control to 0.03km/h and the maximum speed error to 0.17km/h. It also improves the control tracking performance and stability. The practicality of the adaptive hydraulic drive is verified in the filling rate experiments under step steering and double-lane shifting conditions. It has important reference value for the practical application of hydraulic drive control optimization of mining concrete mixing transport tank trucks.

Adaptive event-triggered stochastic estimator-based sampled-data fuzzy control for fractional-order permanent magnet synchronous generator-based wind energy systems

This study aims to develop an adaptive event-triggered estimation sampled-data control method for a fractional-order permanent magnet synchronous generator (PMSG)-based wind energy system (WES) using the Takagi–Sugeno (T–S) fuzzy approach. Unlike existing PMSG-based WES control schemes, we propose an aperiodic event-triggered communication scheme with an adaptive mechanism to reduce data transmission. To address the challenge of limited communication resources, we introduce an adaptive event-triggered (AET) estimation method with aperiodic sampling, significantly reducing communication overhead while maintaining stable WES performance. This method employs transmission techniques based on absolute errors, resulting in high data transmission efficiency. First, the fractional-order model for the PMSG-based WES is represented using linear sub-models through the T–S fuzzy approach. Next, a fuzzy aperiodic AET mechanism is proposed for the PMSG model with augmented estimation error systems. Then, the fractional Lyapunov function theory is employed to derive linear matrix inequalities (LMIs), ensuring bounded mean-square stability for the PMSG-based WES with the augmented estimation error systems. Additionally, the desired estimator control gains are determined through solvable LMIs. Finally, simulation studies are presented to demonstrate the superiority and feasibility of the proposed control scheme.

A Model-Free Adaptive Positioning Control Method for Underactuated Unmanned Surface Vessels in Unknown Ocean Currents

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

Fundamentals of simultaneous machining and coating (SMaC) through combination of extreme high-speed laser material deposition (EHLA) and turning

Simultaneous machining and coating (SMaC) is a novel hybrid technology developed by the Fraunhofer Institute for Laser Technology ILT that combines additive manufacturing through extreme high-speed laser material deposition (EHLA) with a simultaneously engaged turning process. In the present work, the parallelization of the two subprocesses is successfully demonstrated. For the first time, systematic investigations into the influence of residual heat from the deposition process on the machining process and the properties of the resulting coating, respectively, are conducted. The study specifically examines geometric deviations, surface roughness, and tool wear. One of the main parameters affecting the residual heat introduced into the turning process is the distance between the tool center points of the EHLA deposition head and the turning tool Δz. This parameter is identified as a primary factor influencing the dimensional accuracy of the coating geometry. The results show that SMaC not only offers potential for increasing the productivity of the process chain but also has a positive effect on the service life of the cutting tools involved, potentially improving the workability of hard coating materials. Improvements in terms of the attainable surface roughness are also observed. These investigations provide a basis for research into further aspects of the SMaC process, such as adaptive tool path control methods to enhance dimensional accuracy and the influence of induced compressive stresses in processing highly brittle coating materials.

Adaptive tracking control method for shearer cutting trajectory based on the combined strategy

Adaptive tracking control method for shearer remains challenging caused by the contradiction between coal recovery rate and equipment safety under current technological limitations, has drawn more and more attention over the past decades due to its important role in reducing personnel, increasing safety, and improving efficiency for underground mining. Therefore, a novel adaptive tracking control method for shearer cutting trajectory based on the combined strategy is proposed, whose core is the switching method of the sub-strategies based on the linear quadratic regulator (LQR) and error band control (EBC) with switching threshold determined by the Multi-strategy Marine Predator Algorithm. The performance of the proposed method is demonstrated by comparing with the existing methods reported. The experimental results reveal that the proposed method maintains a high recovery rate while improving coal mining efficiency.

Modeling and Verification of Cable-Hole Transmission Tension Ratio Considering the Cable Lateral Extrusion

Cable-hole transmission is widely applied in cable-driven mechanisms to reduce the mechanical size. However, the driving tension is attenuated with the cable threading through the hole caused by uncertain factors such as local deformation, friction, and other effects, and errors in cable-hole transmission occur. To improve the transmission accuracy of cable-driven mechanisms, a tension distribution model considering the cable lateral extrusion is established. Then, an analytical tension ratio of the cable-hole transmission is derived based on the perturbation method and tension distribution model. Parameters of the tension ratio are identified using a particle swarm optimization algorithm. An adaptive tension control method considering the cable lateral extrusion is designed and compared with the method excluding the cable lateral extrusion in the cable-hole transmission. Finally, a cable-hole transmission experimental device was constructed to verify the tension ratio, parameter identification, and servo control method of the cable-hole transmission. The results show the motion control accuracy of the cable-driven mechanism can be significantly improved with the tension ratio considering the cable lateral extrusion. Compared to the case excluding the cable lateral extrusion, the errors in cable-hole transmission considering the lateral extrusion are reduced by an order of magnitude, and the tension vibration is significantly weakened.

Stabilization of LTI systems with both uncertainties and external disturbances via DE-based control method

This paper is concerned with the stabilization of linear time-invariant (LTI) systems with unknown parameters and external disturbances. Firstly, four types of suitable filters are presented and applied to achieve the accuracy estimate of the disturbances. Based on these filters, four types of disturbance estimators are designed and used to asymptotically cancel the corresponding disturbances. Secondly, an adaptive disturbance estimator (DE)-based control method is obtained by combining the adaptive control method with the DE-based control method. It is noted that the algebraic representation of the adaptive feedback control law is given in a more concise form by the tool of the semi-tensor product of matrix. Further, the stabilization of LTI systems is achieved by the obtained adaptive DE-based control method. Finally, three numerical examples with computer simulation are provided to verify the correctness and validity of the obtained theoretical methods.

Adaptive impedance control method for manipulator based on radial basis function

Purpose In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments. Design/methodology/approach The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property. Findings Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%. Originality/value This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Post-occlusion surge parameters during ex vivo phacoemulsification with a new method of adaptive infusion control

Post-occlusion surge parameters during ex vivo phacoemulsification with a new method of adaptive infusion control

Consensus Control of Leader–Follower Multi-Agent Systems with Unknown Parameters and Its Circuit Implementation

With the development and progress of Internet and data technology, the consensus control of multi-agent systems has been an important topic in nonlinear science. How to effectively achieve the consensus of leader–follower multi-agent systems at a low cost is a difficult problem. This paper analyzes the consensus control of complex financial systems. Firstly, the dynamic characteristics of the financial system are analyzed by the equilibrium points, bifurcation diagrams, and Lyapunov exponent spectra. The behavior of the financial system is discussed by different parameter values. Secondly, according to the Lyapunov stability theorem, the consensus of master–slave systems is proposed by linear feedback control, wherein the controllers are simple and low cost. And an adaptive control method for the consensus of master–slave systems is investigated based on financial systems with unknown parameters. In theory, the consensus of the leader–follower multi-agent systems is proved by the parameter identification laws and linear feedback control method. Finally, the effectiveness and reliability of the consensus of leader–follower multi-agent systems are verified through the experimental simulation results and circuit implementation.

Predefined-time adaptive fuzzy control for nonlinear output constraint systems with unknown backlash-like hysteresis via command filtering

In this article, the predefined-time adaptive fuzzy control problem for strict-feedback nonlinear systems with asymmetric time-varying output constraint and unknown backlash-like input hysteresis is considered. An innovative predefined-time adaptive control method, in which utilises unified barrier function (UBF) to deal with the output constraint problem and adopts a differential equation to represent the unknown backlash-like hysteresis, is presented by combining a novel command filter with backstepping technology. Fuzzy logic systems (FLSs) are employed to estimate the unknown nonlinearities. Moreover, by designing appropriate parameters, the constructed control strategy ensures that all signals are predefined-time bound and the tracking error stays in a small neighbourhood adjacent zero. Ultimately, two simulation examples are given to prove the effectiveness of the presented control algorithm.