Adaptive Feedback Control Scheme Research Articles

Non-Rigid Registration Via Intelligent Adaptive Feedback Control.

Preserving features or local shape characteristics of a mesh using conventional non-rigid registration methods is always difficult, as the preservation and deformation are competing with each other. The challenge is to find a balance between these two terms in the process of the registration, especially in presence of artefacts in the mesh. We present a non-rigid Iterative Closest Points (ICP) algorithm which addresses the challenge as a control problem. An adaptive feedback control scheme with global asymptotic stability is derived to control the stiffness ratio for maximum feature preservation and minimum mesh quality loss during the registration process. A cost function is formulated with the distance term and the stiffness term where the initial stiffness ratio value is defined by an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based predictor regarding the source mesh and the target mesh topology, and the distance between the correspondences. During the registration process, the stiffness ratio of each vertex is continuously adjusted by the intrinsic information, represented by shape descriptors, of the surrounding surface as well as the steps in the registration process. Besides, the estimated process-dependent stiffness ratios are used as dynamic weights for establishing the correspondences in each step of the registration. Experiments on simple geometric shapes as well as 3D scanning datasets indicated that the proposed approach outperforms current methodologies, especially for the regions where features are not eminent and/or there exist interferences between/among features, due to its ability to embed the inherent properties of the surface in the process of the mesh registration.

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

Open Cycle - Ocean Thermal Energy Conversion (OC-OTEC) is one of the most important renewable energy sources that generate electricity and fresh water from seawater utilizing the temperature gradient between the warm surface seawater and the cold deep seawater. This paper aims to develop non-linear data-driven model-based adaptive Feedback Control (FBC) schemes for the OC-OTEC process to track the output power and a dynamic Feed-Forward Control (FFC) scheme to reject the effects of temperature disturbance on power caused by climate variations in OC-OTEC. The experiments are conducted on a laboratory-scale OC-OTEC experimental setup at the National Institute of Ocean Technology, Chennai. Firstly, linear data-driven models are developed using system identification techniques. Based on the developed models, gain scheduling-based adaptive control schemes are developed: Proportional Integral (PI) control and Model Predictive Control (MPC). Secondly, the closed-loop performances of the developed FBC schemes are analysed under servo and regulatory operations. Furthermore, it is observed from the sensitivity analysis results that the temperature disturbance highly influences output over the manipulated variable. Hence, a dynamic FFC scheme is implemented to reject known disturbance of 1 °C variation in temperature gradient from 18 °C to 19 °C due to Sea Surface Temperature (SST) changes in temperature gradient. The results show that the proposed MPC-based FBC-FF scheme effectively enhances the tracking and disturbance rejection performance compared to the PI-based FBC-FF control scheme with a minimum Integral Square Error (ISE) of 3.055 and Control Effort (CE) of 7.505. This experimental data-based, data-driven modelling and adaptive control research provides a new direction for automating OC-OTEC. Further, these studies will help to develop a rugged and automated upscaling OC-OTEC plant control system with proposed feasible control schemes.

Control strategy of solid oxide electrolysis cell operating temperature under real fluctuating renewable power

Power-to-liquid (PtL) technology through solid oxide electrolysis cells (SOECs) can efficiently convert renewable energy sources into synthetic fuels for long-time energy storage. However, due to its fluctuation and intermittence, renewable energy sources only produce time-varying currents, which directly impact the operating temperature of SOEC, generating high thermal stresses for SOEC and reducing its durability. To deal with this issue, a control-oriented SOEC dynamic model containing an electrochemical part, a reversible water gas shift (RWGS) part, and a thermodynamic part, was developed in this paper. Afterward, main control parameters, able to adjust SOEC operating temperature effectively, were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST). The corresponding results show that the air flow rate was the best choice to control SOEC temperature when it works in a low current density case, while the fuel flow rate becomes optimal when SOEC operates in a high current case. Given this knowledge, an adaptive feedback control strategy was proposed to stabilize the operating temperature of SOEC in practice. In the end, the effectiveness of the proposed control was demonstrated through step signals of current in both low- and high-current density cases and an actual fluctuating current from real solar cells.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Identifying partial topology of complex networks with stochastic perturbations and time delay

In various practical scenarios, the topology of a complex network is generally unknown or unavailable. Based on the adaptive feedback control strategy and the pining mechanism, some controllers and observers are designed in this manuscript to identify the partial topology of the noise-contaminated networks with coupling delay. The approach can be extended to infer the whole topology of the networks. For large-scale complex networks, our approach greatly reduces the calculation compared with the previous methods. Numerical simulations are employed to illustrate the effectiveness of the proposed theoretical results.

A digital twin-driven trajectory tracking control method of a lower-limb exoskeleton

A direct drive motor is the main component for tracking the gait rehabilitation training trajectory of a lower-limb exoskeleton (LLE). Aiming at the movement instability caused by the changes in the moment of inertia and assembly characteristics of the LLE, a trajectory tracking control method based on the digital twin model is proposed in the study. Firstly, the key characteristic parameters of LLE driven by the direct drive motor are extracted to establish a virtual twin model of LLE. Secondly, the digital twin model is driven by the physical-entity state data and the control parameters of the motor servo are optimized through the twin model based on an adaptive feedback control strategy. Finally, in order to improve the real-time feedback accuracy between the twin model and the physical entity, with the depth deterministic policy gradient and particle swarm optimization algorithm (DDPG-PSO), the parameter matching error between the twin model and the physical entity is reduced. In this way, a digital twin-driven compound control algorithm is obtained. In addition, the proposed method was verified through three sets of experiments. In Experiment 1, the virtual joint angle trajectory θfic of the twin model was compared with the actual joint angle trajectory θact and the average error was no more than 0.05, indicating that the twin model could accurately restore the motion trajectory of the physical entity. In Experiment 2, by comparing with tracking effects of Model-free adaptive control, the adaptive feedback control of digital twin-driven has better stability, and can effectively resist external interference. In Experiment 3, under the no-load condition, the algorithm converged to the optimal solution after 40 iterations. In addition, dynamic parameter changes could be detected in real time, thus proving the rapid convergence and good performance of the DDPG-PSO algorithm.

Stabilization by Adaptive Feedback Control for Positive Difference Equations with Applications in Pest Management

An adaptive feedback control scheme is proposed for stabilizing a class of forced nonlinear positive difference equations. The adaptive scheme is based on so-called high-gain adaptive controllers and contains substantial robustness with respect to model uncertainty as well as with respect to persistent forcing signals, including measurement errors. Our results take advantage of the underlying positive systems structure and ideas from input-to-state stability from nonlinear control theory. Our motivating application is to pest or weed control, and in this context the present work substantially strengthens previous work by the authors. The theory is illustrated with examples.

A switching feedback control approach for persistence of managed resources

<p style='text-indent:20px;'>An adaptive switching feedback control scheme is proposed for classes of discrete-time, positive difference equations, or systems of equations. In overview, the objective is to choose a control strategy which ensures persistence of the state, consequently avoiding zero which corresponds to absence or extinction. A robust feedback control solution is proposed as the effects of different management actions are assumed to be uncertain. Our motivating application is to the conservation of dynamic resources, such as populations, which are naturally positive quantities and where discrete and distinct courses of management actions, or control strategies, are available. The theory is illustrated with examples from population ecology.</p>

Adaptive robust feedback control of moving target tracking for all - Electrical tank with uncertainty

A moving target tracking control problem for marching tank based on adaptive robust feedback control scheme is addressed. A series of preparations is needed for tank gun before shooting a target, the purpose of this paper is to design a control system to fulfill two requirements in this process: the turret-barrel system of tank needs to be adjusted from off-target position to command position and point to the moving target stably when there are strong uncertainties (modeling error, uncertain disturbance with unknown boundaries and road excitation) in the system. Considering the characteristic of coupled interaction, the first thing we do in this paper is to build a coupled analysis model of turret - barrel system with uncertainty term in state-space form. Second, an adaptive robust feedback control scheme is proposed by adding adaptive law to overcome the uncertainty. Third, multi-body dynamics software is used to establish the mechanical mechanism of the tank, and DC-motor module is established in SIMULINK environment, thus the target information and tracking error of the control system is collected and transferred, the gear-ball screw is derived directly by the output torque of the DC-motor module. Finally, the control system and the 3D model are combined together by means of RecurDyn/SIMULINK co-simulation, the turret-barrel system of tank can approximately track the moving target in a certain range. With the adaptive robust feedback control, the target action is completely followed when the target location is constantly changing.

Improved synchronization criteria for fractional-order complex-valued neural networks via partial control

In this article, without dividing a complex-valued neural network into two real-valued subsystems, the global synchronization of fractional-order complex-valued neural networks (FOCVNNs) is investigated by the Lyapunov direct method rather than the real decomposition method. It is worth mentioning that the partial adaptive control and partial linear feedback control schemes are introduced, by constructing suitable Lyapunov functions, some improved synchronization criteria are derived with the help of fractional differential inequalities and L’Hospital rule as well as some complex analysis techniques. Finally, simulation results are given to demonstrate the validity and feasibility of our theoretical analysis.

Adaptive stochastic synchronization of delayed reaction–diffusion neural networks

In this paper, we deal with the adaptive stochastic synchronization for a class of delayed reaction–diffusion neural networks. By combing Lyapunov–Krasovskii functional, drive-response concept, the adaptive feedback control scheme, and linear matrix inequality method, we derive some sufficient conditions in terms of linear matrix inequalities ensuring the stochastic synchronization of the addressed neural networks. The output coupling with delay feedback and the update laws of parameters for adaptive feedback control are proposed, which will be of significance in the real application. The novel Lyapunov–Krasovskii functional to be constructed is more general. The derived results depend on the measure of the space, diffusion effects, and the upper bound of derivative of time-delay. Finally, an illustrated example is presented to show the effectiveness and feasibility of the proposed scheme.

Synchronization of Chaotic-Oscillation Permanent Magnet Synchronous Generators Networks via Adaptive Impulsive Control

This brief presents an adaptive impulsive control scheme for the chaotic-oscillation permanent magnet synchronous generators networks based on theories of complex networks. First, the parameter conditions of the chaotic permanent magnet synchronous generator model are presented, and the coupled dynamic networks is constructed based on Newman-Watts small-world network algorithm. After then, the adaptive impulsive control theory of complex networks is analyzed. Based on the equidistant impulsive control scheme and the adaptive feedback control scheme, a suitable impulsive control gain matrix, impulsive time and the adaptive feedback controller are designed to illustrate the synchronization of the chaotic-oscillation wind generator networks. Theoretical analysis and simulation results demonstrate that the designed adaptive impulsive controllers are efficient to improve the stability and convergence time of the network.

The synchronisation of two floating memristor-based oscillators and the circuit design

The synchronisation between two floating memristor-based Colpitts oscillators is studied in this paper. Firstly, the mathematical and circuit models of Colpitts oscillator based on a floating memristor with a diode bridge structure are built. On this basis, numerical simulations on the features of both the independent memristor and the floating memristor are conducted and compared using MATLAB software. Secondly, circuit simulation is made on the synchronisation of two floating memristor-based systems by using MULTISIM software. Finally, the physical circuit on the synchronisation of the two coupling Colpitts systems based on the diode bridge memristors is implemented by using the linear error feedback scheme and improved by the capacitor coupling scheme and the adaptive nonlinear feedback control scheme, respectively. The experimental results by the oscilloscope and simulation results show that approximate synchronisation is achieved.

Chaos in Fractional Order Cubic Chua System and Synchronization

This paper deals with fractional order Chua system with cubic nonlinearity (cubic Chua system), which is a modification of Chua system. The aim is, first, to study the chaotic behavior in fractional order cubic Chua system. We found that chaos indeed exists in the fractional version of this system. The necessary condition for exhibiting chaotic attractors similar to its integer order counterpart is presented. This condition is used to distinguish for which parameters and orders the system generates double scroll chaotic attractors. The synchronization problem between two coupled chaotic fractional order cubic Chua systems is then addressed. An adaptive feedback control scheme for the synchronization with suitable feedback nonlinear controller applied to the response system is presented. Numerical simulations are performed to verify the theoretical analysis.

Cluster synchronization of linearly coupled complex networks via linear and adaptive feedback pinning controls

Cluster synchronization is investigated for complex networks via linear and adaptive feedback control strategies. It is shown that two different controllers can be designed to achieve the cluster synchronization. Unlike most existing papers, we need not nondelayed and delayed coupling matrices to be symmetric or irreducible. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

Optimisation of the synchronisation of a class of chaotic systems: combination of sliding mode and feedback control

This paper addresses the problem of optimisation of the synchronisation for a class of uncertain chaotic systems from a control theoretic point of view. A robust adaptive feedback which accomplishes the synchronisation of chaotic systems using an optimal tuning scheme based on Riccatti equations is successfully adapted. The underlying idea is to optimise the synchronisation of chaotic systems by accounting the control effort despite the uncertainties. The approach developed considers incomplete state measurements and no detailed model of the systems to guarantee robust stability. This approach includes a high-order sliding mode estimator and leads to a robust adaptive feedback control scheme. A finite horizon can be arbitrarily established by ensuring that the chaos synchronisation is achieved at established time. An advantage is that the studied scheme accounts the energy wasted by the controller and the closed-loop performance on synchronisation. Both mathematic proof and numerical simulations are presented o show the feasibility of the optimisation strategy for establishing the synchronisation of chaotic systems even if there are some modelling mismatches and parametric variations.

Topology and parameters recognition of uncertain complex networks via nonidentical adaptive synchronization

Topology plays an essential role in chaotic behaviors and evolution performances of a complex dynamical network. In this paper, recognition issue for unknown system parameters and topology of uncertain general complex dynamical networks with nonlinear couplings and time-varying delay is investigated through generalized outer synchronization. Firstly, the unknown system parameters and topology in master network are successfully estimated by a slave network. Secondly, the unknown system parameters of both two networks and the unknown topology of the master network are effectively evaluated in view of generalized outer synchronization based on an adaptive feedback control strategy. Two situations of parameters and topologies recognition are efficiently verified by illustrative numerical simulations.

Function projective synchronization in complex dynamical networks with or without external disturbances via error feedback control

This paper investigates function projective synchronization (FPS) of complex dynamical networks (CDNs) with or without external disturbances using error feedback control scheme. The proposed error feedback control scheme contains only error terms, which is quite simple and easy to implement in practical applications. Then, an adaptive error feedback control scheme is designed to achieve FPS in CDNs with or without external disturbances, in which feedback gains can be automatically adapted to suitable constants. Moreover, there is no need to know the norm-bounded of the disturbances. Numerical examples are provided to show the effectiveness of proposed methods.

Synchronization of Unknown Uncertain Chaotic Systems Via Adaptive Control Method

In this paper, an adaptive control scheme is offered to synchronize two different uncertain chaotic systems. It is assumed that the whole dynamics of both master and slave chaotic systems and their bounds are unknown and different. The error system stabilization is achieved in two cases: with input nonlinearities and without input nonlinearities. We design an adaptive control scheme based on the state boundedness property of the chaotic systems. The proposed method does not need any information about nonlinear/linear terms of the chaotic systems. It only uses an adaptive feedback control strategy. The stability of the proposed controllers is proved by using the Lyapunov stability theory. Finally, the designed adaptive controllers are applied to synchronize two different pairs of the chaotic systems (Lorenz–Chen and electromechanical device–electrostatic transducer).

Adaptive position feedback consensus of networked robotic manipulators with uncertain parameters and communication delays

In this study, the authors address the leaderless consensus problem of multiple robotic manipulators with communication delays. Taking dynamic uncertainties of the robotic agents into consideration, they perform the distributed adaptive position feedback control scheme design for strongly connected information topology, where the communication is subject to constant and bounded time delays. The novel distributed sliding observer is first proposed such that the joint velocities can be estimated online. Then, the distributed adaptive controller with dynamic parameter updating is constructed based on the observed information. The convergence of consensus errors is proved with Lyapunov stability analysis tool. Finally, simulations are performed on networked robotic manipulators to validate the effectiveness of the theoretical approach.