Efficient Adaptive Control Research Articles

Adaptive event‐triggered safety control for multiplayer mixed zero‐sum game with partial inputs delay

AbstractThis paper proposes an adaptive safety control method applicable to a multiplayer mixed zero‐sum (MZS) game of nonlinear systems with partial inputs delay. Firstly, a framework is introduced involving N players, where player 1 and player N form a zero‐sum (ZS) game, and player 1 and players 2 to N‐1 form nonzero‐sum (NZS) games, with some players experiencing time delays. Subsequently, the system's value function is augmented with a control barrier function (CBF) to ensure that the system's state remains within a safe region. Secondly, to approximate Nash equilibrium solutions, the study employs adaptive dynamic programming (ADP) and utilizes a critic‐only neural network (NN) to approximate optimal solutions. Diverging from traditional time‐trigger methods, computational and communication load reduction is achieved by introducing a state‐related event trigger condition. The stability of the system is then meticulously analyzed using the Lyapunov theorem. Finally, to validate the effectiveness of the proposed method, the study provides a simulation example demonstrating its performance. In summary, this research introduces an efficient adaptive safety control method for addressing multiplayer MZS games with partial inputs delay, incorporating CBFs, ADP, and state‐related event triggering.

Lateral maneuvering with a UAV mitigating lateral CG variations: modelling and an efficient adaptive backstepping control

Lateral maneuvering with a UAV mitigating lateral CG variations: modelling and an efficient adaptive backstepping control

Adaptive gain scheduling based event-triggering control design for robust performance of active magnetic bearing

This article proposes an efficient adaptive robust control for the eight-pole active magnetic bearing based on a heteropolar structure. Due to the uncertain behavior of active magnetic bearing, the mathematical model of active magnetic bearing is considered to be highly nonlinear and uncertain. As the rotor displacement and velocity are the measurable states, sliding mode control is designed to estimate state variables. Also, a matched disturbance term is used to deal with undesirable disturbances in the active magnetic bearing system. The controller is developed for an active magnetic bearing using the event-triggering-based sliding mode control technique. However, the stability of the proposed scheme has been achieved with the help of Lyapunov theory. Furthermore, the adaptive gain scheduling approach based on a neural network has been augmented to adjust the gain of the proposed controller for active magnetic bearing adaptively. The simulation studies have been performed in detail to demonstrate the use of proposed scheme for the robust control of active magnetic bearings. Finally, a comparative analysis of the proposed control design scheme with a conventional controller has been performed to achieve improved performance satisfying the plant constraints.

An Efficient Closed-Loop Adaptive Controller for a Small-Sized Quadruped Robotic Rat

Large quadruped robots have shown potential for a wide range of everyday tasks due to their superior terrain adaptation. However, small-scale quadruped robots have limited payloads and thus cannot carry sufficient sensing and computational resources, which imposes limitations on their environmental adaptability. To address this challenge, we proposed an efficient closed-loop adaptive controller by simplified pose estimation and control strategy that utilizes only inertial measurement unit sensors, which drastically reduces the control computation. Accordingly, we integrated this control system into a small-scale quadruped robot, SQuRo, and conducted a series of experiments to verify the environmental adaptation performance of SQuRo. The results demonstrated that SQuRo has achieved 6 kinds of robust motion: slope stabilization motion, linear tracking motion, autonomous fall recovery motion, uneven terrain walk, slope walk, and obstacle avoidance. This work paves the way for small-scale quadruped robots to autonomously perform tasks in challenging environments.

Retracted: Efficient Monitoring and Adaptive Control of Indoor Air Quality Based on IoT Technology and Fuzzy Inference

Retracted: Efficient Monitoring and Adaptive Control of Indoor Air Quality Based on IoT Technology and Fuzzy Inference

Online Transfer Function Estimation and Control Design Using Ambient Synchrophasor Measurements

This paper proposes an adaptive control design framework for damping inter-area oscillations in power systems. The design is entirely based on ambient synchrophasor measurements. The proposed framework consists of three components, namely, input-output transfer function estimation, channel selection, and feedback control implementation. For the first part, we propose a simple yet effective novel estimation algorithm in the frequency domain to identify low-order transfer functions in pre-specified frequency ranges between the measured input and output data using ambient synchrophasor measurements. In the second part, based on the identified transfer functions, the joint controllability-observability (JCO) of all identified channels is estimated and most suitable control candidates for damping the target inter-area mode are selected. Finally, an appropriate lead-lag controller is designed using a classical frequency-domain method to improve the damping of a dominant oscillatory inter-area mode. Since ambient data is used in the analysis, the selected channel as well as the designed controller parameters can be updated online whenever the system operating point changes resulting in an efficient adaptive controller. The effectiveness of the proposed framework is illustrated by implementing it on the two-area Kundur test system and the IEEE 39-bus test system.

WCA: A New Efficient Nonlinear Adaptive Control Allocation for Planar Hexacopters

This paper presents an efficient control allocation (CA) strategy which significantly improves the flight performance of a planar hexacopter. This allocation maps the desired control vector v = [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T, L,M,N</i> ]⟙ composed of the thrust and torques in roll, pitch, and yaw axis, respectively, to propellers’ speed. This paper shows that a CA strategy based on the classical approach of pseudo-inverse matrix only exploits a limited range of the vehicle capabilities to generate thrust and moments. A novel approach is presented, which is based on a weighted pseudo-inverse matrix method, called WCA, capable of exploiting a much larger domain in v. The three weights involved in WCA are adapted online according to nonlinear laws which are analytically derived by solving symbolically the equivalent constraint least-squares problem, thus removing the need for online-optimization calculations. This solution allows for very fast real-time operations, suitable for autopilots with limited computing resources. This paper provides 1) a detailed analysis of the limitations of the classical control allocation scheme, 2) the mathematical development of the WCA algorithm, 3) simulations and real experiments which show that this WCA strategy outperforms the classical CA approach in terms of a) capability to generate the maximum roll and pitch torques possible, without generating undesired yaw torques, b) prioritizing the generation of thrust over attitude torques, thus achieving better altitude tracking despite aggressive maneuvers or the presence of a payload, and c) better behavior in case of actuator saturation, faults or even failures.

Efficient Monitoring and Adaptive Control of Indoor Air Quality Based on IoT Technology and Fuzzy Inference

In recent years, more and more occupants have suffered from respiratory illness due to poor indoor air quality (IAQ). In order to address this issue, this paper presents a method to achieve efficient monitoring and adaptive control of IAQ. Firstly, an indoor air quality monitoring and control system (IAQMCS) is developed using IoT technology. Then, based on fuzzy inference, a novel fuzzy air quality index (FAQI) model is proposed to effectively assess IAQ. Furthermore, a simple adaptive control mechanism, called SACM, is designed to automatically control the IAQMCS according to a real-time FAQI value. Finally, extensive experiments are performed by comparing with regular control (time-based control), which show that our proposed method effectively measures various air parameters (CO2, VOC, HCHO, PM2.5, PM10, etc.) and has good performance in terms of evaluation accuracy, average FAQI value, and overall IAQ.

Neuro‐fuzzy Elman wavelet control for nonlinear uncertain systems with fuzzy input and unknown fuzzy disturbances: Application to robotics

SummaryKeeping into view the irregular existence of uncertainties and disturbances in nonlinear systems, a novel adaptive control scheme based on neuro‐fuzzy Elman wavelet neural network (NFEWNN) has been proposed in this work which is capable of accurately modeling system uncertainties. In addition to this, an improvement in an evolutionary algorithm named particle swarm optimization has also been introduced to optimize the initial parameters of the controller. Uncertain non‐linear discrete‐time systems in the non‐strict feedback form with unknown disturbances are taken into consideration. This work aims to design an efficient adaptive control scheme for the proposed systems in the case of fuzzy input and fuzzy external disturbance which makes the control of the system more complex and difficult. The proposed NFEWNN control scheme is capable of tracking the desired trajectory successfully along with accurately modeling system uncertainties with high computational power, low computational burden, and rapid convergence rate. The stability of the controller has been proved using Lyapunov theory and guaranteed convergence of the controller is proved. Finally, real‐world applications of the work have been found in robotics. The controller has been implemented on the robotic manipulator and the dynamic model of aerial robotic vehicles to prove its effectiveness and efficiency.

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski‐driver algorithm

AbstractThis study uses a Mexican hat wavelet membership function for a cerebellar model articulation controller (CMAC) to develop a more efficient adaptive controller for multiple input multiple output (MIMO) uncertain nonlinear systems. The main controller is called the adaptive Mexican hat wavelet CMAC (MWCMAC), and an auxiliary controller is used to remove the residual error. For the MWCMAC, the online learning laws are derived from the gradient descent method. In addition, the learning rate values are very important and have a great impact on the performance of the control system; however, they are difficult to choose accurately. Therefore, a modified social ski driver (SSD) algorithm is proposed to find optimal learning rates for the control parameters. Finally, a magnetic ball levitation system and a nine‐link biped robot are used to illustrate the effectiveness of the proposed SSD‐based MWCMAC control system. The comparisons with other existing control algorithms have shown the superiority of the proposed control system.

Research on shifting process control of automatic transmission

The shift quality of an automatic transmission directly affects the human-perceived comfort and the durability of the automatic transmission. In general, the inconsistency caused by manufacturing errors, life-cycle changes, or other changes in hydraulic characteristics are the main reason affecting the shift quality, which should be compensated by adaptive control in the shifting process. In this paper, we first provide an in-depth analysis of the relationship between proportional solenoid current, clutch pressure, speed and torque in the shifting process control. Then we propose two efficient adaptive control strategies for the torque phase and inertia phase, respectively. Both algorithms are tested and verified on a riot utility vehicle. The experimental results indicate that the adaptive control strategies proposed in this paper can effectively compensate the engine flare and the clutch tie-up of the torque phase, and keep the inertia phase within a proper time range.

Mechanically tunable radiative cooling for adaptive thermal control

Passive radiative cooling is currently the frontier technology in renewable-energy research. In terms of extraterrestrial applications, radiative cooling is a critical component to the thermal management system of a spacecraft, where the extreme environment of space can cause large temperature variations that can break and damage equipment. For terrestrial applications, nocturnal or daytime radiative cooling is expected to lead to cost-effective passive heat management without the need of inefficient and costly artificial refrigeration technologies. However, most currently available radiative cooling systems cannot be changed dynamically and radiate a constant static amount of thermal power. Dynamically tunable adaptive radiative cooling systems will be a critical development to prolong the lifetime of spacecraft or improve the efficiency of terrestrial cooling systems. Here we propose stretchable radiative cooling designs that can be substantially tuned by using the simple physical mechanism of mechanical strain. When their structure is stretched, the radiated power is significantly reduced. We develop a modeling method that can simulate mechanical stretching combined with electromagnetic response to compute the tunable thermal emission of these new adaptive radiative cooling systems. The presented photonically engineered structures can be used as coatings to achieve efficient adaptive thermal control of various objects in a cost-effective and environmentally friendly way. The proposed designs are much simpler to be realized than others found in the literature and the best design achieves a high thermal emission power with a tunable range on the order of 132 W/m2.

Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics

Existing data-driven and feedback traffic control strategies do not consider the heterogeneity of real-time data measurements. Besides, traditional reinforcement learning (RL) methods for traffic control usually converge slowly for lacking data efficiency. Moreover, conventional optimal perimeter control schemes require exact knowledge of the system dynamics and thus they would be fragile to endogenous uncertainties. To handle these challenges, this work proposes an integral reinforcement learning (IRL) based approach to learning the macroscopic traffic dynamics for adaptive optimal perimeter control. This work makes the following primary contributions to the transportation literature: (a) A continuous-time control is developed with discrete gain updates to adapt to the discrete-time sensor data. Different from the conventional RL approaches, the reinforcement interval of the proposed IRL method can be varying with respect to the real-time resolution of data measurements. Approximate optimization methods are carried out to address the curse of dimensionality of the optimal control problem with consideration on the resolution of data measurement. (b) To reduce the sampling complexity and use the available data more efficiently, the experience replay (ER) technique is introduced to the IRL algorithm. (c) The proposed method relaxes the requirement on model calibration in a “model-free” manner that enables robustness against modeling uncertainty and enhances the real-time performance via a data-driven RL algorithm. (d) The convergence of the IRL based algorithms and the stability of the controlled traffic dynamics are proven via the Lyapunov theory. The optimal control law is parameterized and then approximated by neural networks (NN), which moderates the computational complexity. Both state and input constraints are considered while no model linearization is required. Numerical examples and simulation experiments are presented to verify the effectiveness and efficiency of the proposed method.

Adaptive integral backstepping control of a reconfigurable quadrotor with variable parameters’ estimation

This work presents the control of a quadrotor capable of changing its configuration while flying. The designed controller should handle several arising issues since the drone geometric parameters are varying over time. This variation has a great influence on the quadrotor control, which prompts us to propose an efficient nonlinear adaptive integral backstepping controller. The control architecture includes an estimator of the inertia and other specific blocks to calculate the center of gravity and the mixing matrix. The performance of the proposed control approach has been compared with the conventional integral backstepping controller in two cases, with and without disturbances. The obtained results of the inertias and the center of gravity are also compared and confirmed with those calculated using Computer-Assisted Design–based model.

Combined effects of body posture and three-dimensional wing shape enable efficient gliding in flying lizards

Gliding animals change their body shape and posture while producing and modulating aerodynamic forces during flight. However, the combined effect of these different factors on aerodynamic force production, and ultimately the animal’s gliding ability, remains uncertain. Here, we quantified the time-varying morphology and aerodynamics of complete, voluntary glides performed by a population of wild gliding lizards (Draco dussumieri) in a seven-camera motion capture arena constructed in their natural environment. Our findings, in conjunction with previous airfoil models, highlight how three-dimensional (3D) wing shape including camber, planform, and aspect ratio enables gliding flight and effective aerodynamic performance by the lizard up to and over an angle of attack (AoA) of 55° without catastrophic loss of lift. Furthermore, the lizards maintained a near maximal lift-to-drag ratio throughout their mid-glide by changing body pitch to control AoA, while simultaneously modulating airfoil camber to alter the magnitude of aerodynamic forces. This strategy allows an optimal aerodynamic configuration for horizontal transport while ensuring adaptability to real-world flight conditions and behavioral requirements. Overall, we empirically show that the aerodynamics of biological airfoils coupled with the animal’s ability to control posture and their 3D wing shape enable efficient gliding and adaptive flight control in the natural habitat.

Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning

Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments.

Enhanced dynamic performance of steam turbine driving synchronous generator emulator via adaptive fuzzy control

This paper deals with experimental verification of the applicability of an efficient adaptive fuzzy controller (AFC) aimed for enhancing the dynamic performance of a steam turbine driving synchronous generator under different operating conditions. Two controllers are developed to control the system, namely the recursive least square optimal controller (RLSOC) and the AFC. Both controllers are designed based on the linearized and the nonlinear models of the system respectively using LabVIEW™ software and hardware in the loop (HIL). The results show that the system stability is enhanced when applying the proposed AFC. The motivation of this research is to guide the industrial manufacturers and users of steam turbines of different rating and inertia for applying different tests at severe faulty and unstable conditions. Real time emulators recently became cost-effective solutions for testing dynamic system performance using new optimized control techniques due to the relatively high cost and weight of steam turbine.

Adaptive backstepping control design for MEMS gyroscope based on function approximation techniques with input saturation and output constraints

An efficient adaptive backstepping control method is presented in the current study for controlling the micro-electro-mechanical system (MEMS) triaxial gyroscope. In this approach, practical considerations, including output constraints and input saturation, are taken into account. A Barrier Lyapunov Function (BLF) is used so that non-transgression of the constraints is ensured. In the suggested control method, for approximating the lumped uncertainties and addressing the problem of explosion of complexity, Hermite polynomials are used as a simple but effective function approximation approach as a universal approximator. Therefore, the resulting control approach is much simpler with minimal learning parameters. The Lyapunov stability theory is used so that the semiglobally uniformly ultimately boundedness of the closed-loop signals with proper tracking error convergence is guaranteed. According to the results of the comparative simulation, the superiority of this approach is confirmed.

An accelerated gradient-based optimization development for multi-reservoir hydropower systems optimization

Hydropower is one of the significant renewable energy resources. It is regularly requested at peak time steps to meet the load requirements of power systems resources allocation. Therefore, modeling the optimal operation of hydropower systems to maximize the entire energy production of reservoir systems can be a vital task for energy investment. Deriving optimal unknown decision parameters of these reservoir systems is a nonlinear, nonconvex, and complex optimization problem. Herein, a novel optimization algorithm, called an accelerated version of gradient-based optimization (AGBO), is developed to solve a complex multi-reservoir hydropower system. This advised technique uses an efficient adaptive control parameters mechanism to stabilize the global and local search; utilizing an enhanced local escaping operator (ELEO) to extend the chances of getting away from local optima; expanding the exploitation search by applying the sequential quadratic programming (SQP) technique. At first, the developed AGBO algorithm is employed to solve the optimal operation of a complex 10-reservoir hydropower system. Secondly, the possibility of the AGBO algorithm within the global optimization problems is illustrated by numerical tests of 23 mathematical benchmark functions. Optimal results show that the proposed AGBO can approach to 0.9999% of the optimal global solution. As a result, the advised method is the most superior one compared to the other advanced optimization algorithms for maximizing the load demands in hydropower system. In conclusion, this offers a productive tool to solve the complex hydropower multi-reservoir optimization systems.

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.