Adaptive Variable Structure Control Research Articles

Bearing-Constrained Leader-Follower Formation of Single-Integrators With Disturbance Rejection: Adaptive Variable-Structure Approaches.

This article studies the problem of stabilizing a leader-follower formation specified by a set of bearing constraints while disturbed by some unknown uniformly bounded disturbances. A set of leaders are positioned at their desired positions while each follower is modeled by a single integrator with an additive time-varying disturbance. Adaptive variable-structure control laws using displacements or only bearing vectors are applied to stabilize the desired formation. Thanks to the adaptive mechanisms, the proposed control laws require neither information on the bearing Laplacian nor the directions and upper bounds of the disturbances. It is further proved that when the leaders are moving with the same bounded uniformly continuous velocity, the moving target formation can be achieved under the proposed control laws. Simulation results are provided to support the stability analysis.

Adaptive Variable Structure Controller Design for Uncertain Switched Systems With Unknown Time-varying Delay

Adaptive Variable Structure Controller Design for Uncertain Switched Systems With Unknown Time-varying Delay

Adaptive backstepping human-cooperative control of a pediatric gait exoskeleton system with high- and low-level admittance

Post-neurological disorder, passive-assist training disregards human involvement during robot-aided lower-limb rehabilitation. This work presents a novel human-cooperative framework based on the admittance and trajectory control scheme to administer the subject–exoskeleton interaction for pediatric gait rehabilitation. Initially, the mechanical design and dynamic analysis of an existing exoskeleton system are briefly attended. Thereafter, an admittance model is designed in the outer loop, which recasts the desired human trajectory into a reference trajectory. As an inner-loop control scheme, a robust adaptive backstepping controller is designed to trace the reference gait trajectory under parametric uncertainties and external disturbances. Lyapunov stability analysis is solved to guarantee the uniform boundedness of the control signals. Moreover, the well-known problem of “explosion of complexity” and “overparameterization” is avoided through the design of the robust adaptive backstepping control scheme. The performance of the proposed robust adaptive backstepping-based human-cooperative control is studied under the low- and high-level admittance model. Finally, the effectiveness of the proposed control is validated with a variable structure adaptive robust-based human-cooperative control. The co-simulation results show that the proposed control with low-level admittance allows the subject to participate in the training process more frankly. The proposed robust adaptive backstepping-based human-cooperative control tracks the reference gait more promisingly than the variable structure adaptive robust-based human-cooperative control for both admittance levels.

A Barrier Function-Based Variable Structure Control for Maglev System

Magnetic levitation (Maglev) systems are widely employed in the industry especially in mechatronics systems for precise positioning and suspension. They are inherently unstable having nonlinear models with uncertain parameters and exposed to external disturbances. Therefore, high-performance robust control designs are recommended for these systems. An Adaptive Variable Structure Controller based on barrier function (AVSCbf) is designed for the first time in this work to control the displacement of the ball position of a disturbed Maglev system. This approach does not require prior knowledge of the disturbance upper bounds in the design procedure. The state space region defined by the barrier function is designed to be attractive and invariant. This feature is essential to reject disturbances and handle parametric uncertainties. The adaptive law is activated when the state trajectory is initiated outside the invariant set defined by the barrier function. The gain of the VSC is adapted according to an adaptation law, which considers the system input constraints. The control input is constrained to be a bounded positive quantity. The adaptive VSC is only applied during the reaching phase. Once the state reaches the invariant set, the barrier-function-based VSC is applied to confine the state inside it. The resulting overall controller is a chattering-free VSC since the barrier-function based VSC is continuous. The steady-state error is limited to a minimal value by only specifying the barrier function parameter. Numerical simulations are conducted to show the efficiency of the new approach. Three types of VSC controllers for the Maglev system are compared. AVSCbf is compared to the performance of adaptive only VSC without the barrier function (AVSC) and both are designed in this work. AVSCbf is also compared to the classical VSC performance from previous work in the literature. The results of the comparison showed the efficiency of the proposed controller.

Adaptive variable structure observer for system states and disturbances estimation with application to building climate control system in a smart grid

In order to reach the ambitious net-zero emission target by 2050, various technological solutions need to be developed to ensure efficient utilisation of energy. Commercial and residential buildings are a big source of greenhouse gas emissions, where efficient utilisation of energy can play a major role towards decarbonisation of the buildings sector. Heat pumps have recently emerged as an effective solution for space heating applications in buildings. Energy-efficient operation of heat pumps will make a significant contribution toward making buildings energy-efficient. In this context, heat pump control systems have a major role. Some of the existing literature on the heat pump control systems assume that various system states are available to measure. This may not always be true and/or economical to measure all the states. Moreover, the system is subject to various disturbances which cannot be directly measured. To reduce the number of sensors in heat pump control systems, an adaptive observer is developed in this paper to estimate inaccessible system states and disturbances simultaneously. An advantage of the proposed approach is that it does not require any bound on the disturbance itself, however, only assumes that the rate of change of disturbance is bounded. This is always the case in practice. In the developed method, adaptive control techniques and variable structure control techniques are combined to implement the proposed observer. In order to estimate the unknown disturbance, an augmented systems model is considered. Globally uniformly ultimately bounded property of the error dynamical systems is established by suitably designing the adaptive laws. The developed method is applied to a model of the heat dynamics of a house floor heating system connected to a ground source-based heat pump. Different disturbance signals formats and amplitudes are considered to show the effectiveness of the proposed technique. Simulation results are given to demonstrate the suitability of the proposed method.

Attenuated-chattering adaptive second order variable structure controller for mismatched uncertain systems

In this paper, an attenuated-chattering adaptive second order variable structure controller (ACASOVSC) is proposed for mismatched uncertain systems using a Moore-Penrose inverse method. The key achievements of this study include three tasks: 1) influence of the chattering in control input is diminished, 2) finite-time convergence of system states is guaranteed, and 3) external disturbance is generally assumed to be unknown in advance. Firstly, a switching manifold which comprises only output information is defined. Secondly, a reduced-order variable structure estimator (ROVSE) with lower dimension is designed to reduce the computation burden and enhance the robustness. Thirdly, an adaptive approach is used to guess the upper bound of the unknown exogenous disturbance. Next, an ACASOVSC is investigated for attenuating the chattering phenomenon and stabilizing the system. Then, a novel linear matrix inequality (LMI) constraint by the Lyapunov technique is given such that the plant is entirely invariant to matched uncertainties and asymptotically stable. Finally, a mathematical illustration is simulated, which exhibits the usefulness and the feasible application of the proposed method.

Robust control of a space robot based on an optimized adaptive variable structure control method

Space robot has been playing an increasingly important role in on-orbit service missions. Dynamic coupling exists between the space robot arm and the floating base, and an inaccurate dynamic model will deteriorate the control accuracy of space robot motion. This paper proposed an optimized adaptive variable structure control method to realize coordinate motion control of space robot base and its arm. The controller can adapt its gain to match system uncertainties and external disturbances so that the error dynamics converge to the origin following a parabola-like path which is close to the natural behavior of a second-order system. Therefore, the controller will eliminate the chattering phenomenon and reduce the settling time. Further, taking the motion error as the objective function, the gain parameter is optimized by adopting the modified Gaussian barebones differential evolution method. Numerical simulations aimed at verifying the space robot dynamic model and the effectiveness of the controller are carried out based on Simscape Multibody. The results prove that the theoretical dynamic model of the space robot is accurate. In addition, the controller is demonstrated to present reduced settling time and higher control accuracy in comparison with the boundary layer sliding mode control method.

Attitude stability control of micro-nano satellite orbit maneuver based on bias momentum

In this paper, an attitude stability control strategy based on cold air micro propulsion microsatellite orbital maneuver is designed. Firstly, the influence of environmental disturbance moment, uncertainty of rotational moment of inertia and thrust eccentricity moment on the attitude stability of the micro-nano-satellite is considered, and the attitude dynamics model of the micro-nano-satellite based on biased momentum wheel and magnetic moment device is established. Then, the disturbance moments such as environmental disturbance moments, rotational inertia uncertainty and thrust eccentricity moments are analyzed. In order to suppress the influence of various internal and external disturbance factors on the stability of the micro-nano-satellite during the deorbiting process, a robust adaptive sliding mode variable structure controller is designed. The designed robust adaptive sliding mode controller is able to compensate for various disturbances adaptively. The robust adaptive sliding-mode variable structure controller is designed with a reasonable distribution of torque considering the characteristics of bias momentum wheel control and magnetic control. Finally, numerical simulations are performed, and the simulation results show that the system has good robustness.

Neural-Fuzzy-Based Adaptive Sliding Mode Automatic Steering Control of Vision-based Unmanned Electric Vehicles

This paper presents a novel neural-fuzzy-based adaptive sliding mode automatic steering control strategy to improve the driving performance of vision-based unmanned electric vehicles with time-varying and uncertain parameters. Primarily, the kinematic and dynamic models which accurately express the steering behaviors of vehicles are constructed, and in which the relationship between the look-ahead time and vehicle velocity is revealed. Then, in order to overcome the external disturbances, parametric uncertainties and time-varying features of vehicles, a neural-fuzzy-based adaptive sliding mode automatic steering controller is proposed to supervise the lateral dynamic behavior of unmanned electric vehicles, which includes an equivalent control law and an adaptive variable structure control law. In this novel automatic steering control system of vehicles, a neural network system is utilized for approximating the switching control gain of variable structure control law, and a fuzzy inference system is presented to adjust the thickness of boundary layer in real-time. The stability of closed-loop neural-fuzzy-based adaptive sliding mode automatic steering control system is proven using the Lyapunov theory. Finally, the results illustrate that the presented control scheme has the excellent properties in term of error convergence and robustness.

Flexible spacecraft attitude maneuver via adaptive sliding mode control and mixed component synthesis vibration suppression

<p indent=0mm>A mixed component synthesis vibration suppression method (MCSVS) is proposed to solve the problem of residual vibration of flexible attachments, which is difficult to control effectively. Theorems and inferences are provided, and their proofs are performed. Aiming at the problem that the attitude controllers cannot suppress the vibration of flexible attachments in spacecraft attitude maneuver, a strategy combining the MCSVS method with an attitude control method is proposed. Considering the existence of moment of inertia variation and external disturbance of spacecraft, an adaptive sliding mode variable structure controller is proposed. By introducing a sliding mode boundary layer and the update rate of the torque parameter, the disadvantages of torque chattering and parameter dependence are eliminated. Finally, the joint application of the MCSVS method and the adaptive variable structure controller is realized with the assistance of piezoelectric intelligent materials. Simulation results show that the control strategy without the MCSVS method cannot suppress the vibration of flexible attachments. After combining the MCSVS method with the attitude control method, the residual vibration is well controlled, and the attitude accuracy is improved.

Cooperative control method of multi-missile formation under uncontrollable speed

Under the missile speed is uncontrollable, a design method of multi-missile formation flight controller based on the sliding mode variable structure control theory and adaptive dynamic surface control theory is proposed. Firstly, according to the relative position of the leader and the follower in the inertial frame, the tracking error model for the relative position and the expected relative position between the leader and the follower is obtained, and the multi-missile formation control system in the inertial coordinate system is obtained. Secondly, in order to obtain the expression of the formation control system in the ballistic coordinate system, the acceleration of the missile in the ballistic coordinate system is converted to the inertial coordinate system. Combining with the tracking of the relative position and the desired relative position of the leader and the followers, we can obtain the simplified error model for the formation control system. Then the sliding mode variable structure control theory and the adaptive dynamic surface control theory are used to design the formation controllers for the leader and follower missiles respectively, and the stability of the present controller is analysed via the Lyapunov stability theory. Finally, the designed formation controllers are used for the leader and follower missiles to simulate the parameters. The results verify the feasibility and effectiveness of the present method.

Fuzzy Adaptive Variable Structure Control of Second-Order Robotic Manipulators with Dead Zones

In this study, with respect to certain second-order robotic systems with dead zones, a fuzzy adaptive variable structure controller (VSC) is implemented. Some suitable adaptive fuzzy systems are used to estimate uncertain functions. Based on Lyapunov stability theorems, parameter adaptive laws are designed, and it is proven that all signals involved will remain bounded and the stability of the controlled system is also guaranteed. Our controller is effective for the system with or without sector nonlinearity. Finally, a simulation example is presented to illustrate the correctness of the theoretical derivation.

Variable Structure Adaptive Control of LLC Resonant converter

This paper presents the Model reference adaptive variable structure controller for second order system of LLC converter to control the output voltage and rectifier current. The small signal model of the converter is developed by using extended describing function and Jacobean Linearization methods. Seven state small signal modeling of the converter is reduced to two measurable states using the relationship between the state variables. The Lyapunov adaptive method is used to estimate the sliding mode controller parameters to solve the disturbance caused by parameter variation in the converter. The converter is designed for 4kV output voltage and 0.55A output current. The simulation result indicates that the adaptive sliding mode controller has settling time (ts) 2.5msec and rise time (tr) 4.5 μsec respectively, while the steady state error (ess) is 0.2%.

A multi-body control approach for flapping wing micro aerial vehicles

Flapping wing micro aerial vehicle (FWMAV) has a multi-body and periodic dynamics, which is influenced by unsteady aerodynamics. These features make it more difficult to control. Ignoring the wing inertia, dynamics averaging, and using a simple aerodynamic model are the simplifying assumptions for conventional model-based control, although they may result in inaccurate control. To overcome these difficulties, a multi-body control is proposed based on a model-free adaptive variable structure control (MFAVSC) approach. MFAVSC takes advantage of input/output data, while not including any explicit model information. At first, the nonlinear FWMAV dynamics is transformed into an equivalent dynamic linearization description with a concept called pseudo-partial derivative (PPD). After estimating the PPD matrix, model-free adaptive control law is designed based on the optimal criteria. Then, it is augmented by a variable structure control term to guarantee the stability, as well as speeding up its convergence. Finally, simulation results demonstrate the effectiveness of the proposed scheme to trajectory control of the FWMAV.

Adaptive variable structure controller design of uncertain nonlinear systems based on state estimation: a new scheme

Adaptive variable structure controller design of uncertain nonlinear systems based on state estimation: a new scheme

Adaptive variable structure controller design of uncertain nonlinear systems based on state estimation: a new scheme

Adaptive variable structure controller design of uncertain nonlinear systems based on state estimation: a new scheme

Design of Fast Variable Structure Adaptive Fuzzy Control for Nonlinear State-Delay Systems with Uncertainty

In this study, a new fast variable structure adaptive fuzzy controller is presented for nonlinear state-delay systems which are subjected to external disturbances and uncertainties. The undesirable chattering and singularity of the variable structure scheme are eliminated by using a novel fast robust high-precision continuous nonsingular control law which is able to accelerate the finite-time convergence both in reaching and sliding phases of the motion. A fuzzy logic system with a neural network adaptive law is used to approximate the dynamics of the nonlinear system containing the current state and the delayed state. The superiority of the proposed fuzzy neural network in online adjusting the weights of the network is the fast convergence rate of the approximation error to the optimum value in a very short time. The stability of the closed-loop system is proved by using an extended finite-time Lyapunov criterion such that the convergence of the position tracking error, velocity tracking error, and the estimation error to the bounded region is guaranteed in a very short time. Two second-order uncertain nonlinear simulation examples with external disturbances are given to evaluate the efficacy of the proposed control technique. The simulation results show that faster and high-precision tracking performance is obtained compared with the existing recent works focused on robust control of nonlinear state-delay systems with uncertainties.

Adaptive control realization for canonic Caputo fractional-order systems with actuator nonlinearity: application to mechatronic devices

Nonlinearities, such as dead-zone, backlash, hysteresis, and saturation, are common in the mechanical and mechatronic systems’ components and actuators. Hence, an effective control strategy should take into account such nonlinearities which, if unaccounted for, may cause serious response problems and might even result in system failure. Input saturation is one of the most common nonlinearities in practical control systems. So, this article introduces a novel adaptive variable structure control strategy for nonlinear Caputo fractional-order systems despite the saturating inputs. Owing to the complex nature of the fractional-order systems and lack of proper identification strategies for such systems, this research focuses on the canonic systems with complete unknown dynamics and even those with model uncertainties and external noise. Using mathematical stability theory and adaptive control strategy, a simple stable integral sliding mode control is proposed. The controller will be shown to be effective against actuator saturation as well as unknown characteristics and system uncertainties. Finally, two case studies, including a mechatronic device, are considered to illustrate the effectiveness and practicality of the proposed controller in the applications.

Modeling and Dynamics Analysis of Marine Planktonic Ecosystem Based on Adaptive Fuzzy Variable Structure Control Approach

Yao, H., 2020. Modeling and dynamics analysis of marine planktonic ecosystem based on adaptive fuzzy variable structure control approach. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 436–441. Coconut Creek (Florida), ISSN 0749-0208.Based on the logistic model, a new nonlinear dynamic model of marine plankton ecosystem is established considering the fishing behavior and the weakening effect of pollution on plankton and the self purification ability of plankton ecosystem. The dynamic characteristics of the model are analyzed by using the adaptive fuzzy variable structure control method, and the stability conditions of the marine planktonic ecosystem are obtained. On this basis, the adaptive fuzzy variable structure control of the system is designed. Finally, the feasibility and validity of this method for the study of marine planktonic ecological model are verified by numerical simulation.

An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator

A robot manipulator is a multi-degree-of-freedom and nonlinear system that is used in various applications, including the medical area and automotive industries. Uncertain conditions in which a robot manipulator operates, as well as its nonlinearities, represent challenges for fault diagnosis and fault-tolerant control (FDC) that are addressed through the proposed FDC technique. A machine-learning-based neural adaptive, high-order, variable structure observer for fault diagnosis (FD) and adaptive, modern, fuzzy, backstepping, variable structure control for use in a fault-tolerant control (FC) algorithm, are proposed in this paper. In the first stage, a variable structure observer is proposed as an FD technique for the robot manipulator. The chattering phenomenon associated with the variable structure observer(VSO) is solved using a high-order variable structure observer. Then, the dynamic behavior estimation performance in the high-order variable structure observer is improved by incorporating a neural network algorithm in the FD pipeline. This adaptive technique is also effective in improving the robustness of the fault signal estimation. Moreover, support vector machines (SVMs) that can derive adaptive threshold values are used to categorize faults. To design an effective fault-tolerant controller (FC), an adaptive modern fuzzy backstepping variable structure controller is used in this study. First, a new variable structure controller is designed. Next, to increase robustness and reduce high-frequency oscillations in uncertain conditions, a backstepping algorithm is used in parallel with the variable structure controller to design the backstepping variable structure controller. To design an effective hybrid controller, a fuzzy algorithm is integrated into the backstepping variable structure controller to create a fuzzy backstepping variable structure controller. Then, to improve the robustness and reliability of the FC, a neural adaptive. high-order. variable structure observer is applied to the fuzzy backstepping variable structure controller to design a modern fuzzy backstepping variable structure controller. An adaptive algorithm is used to fine-tune the variable structure coefficients and reduce the effect of faults on the robot manipulator. The effectiveness of the selected algorithm is validated using a PUMA robot manipulator. The neural adaptive. high-order variable structure observer improves the average performance for the identification of various faults by about 27% and 29.2%, compared with the neural high-order variable structure observer and variable structure observer, respectively.