Hybrid Adaptive Control Research Articles

A hybrid control combining an advanced sliding mode control law with a proportional-derivative control by applying on vibration suppression

A hybrid controller is proposed to control the vibration of a cantilever structure. The hybrid adaptive controller combines sliding mode controller (SMC) and proportional-derivative (PD) controller. Meanwhile, a type-2 fuzzy neural network (T2FNN) with a zero-order consequent part and certain center is applied to SMC due to the unknown of the controlled object. The initial parameters of T2FNN are obtained through the identification by an SMC-theory based T2FNN. The adaption laws of the controller and T2FNN are given through Lyapunov stability. The state space equation of the beam is established for simulation. The proposed controller is applied to vibration control of a cantilever beam bonded with piezoelectric actuator to verify its effectiveness. The simulation and experiment results show excellent control performance of disturbance rejection and vibration suppression of the proposed controller.

Hybrid adaptive bionic fuzzy control method for MIMO systems with dead-zone input

In this research, a hybrid adaptive bionic fuzzy control strategy is developed for a class of complicated nonlinear multiple-input-multiple-output (MIMO) systems with dead-zone input. The first component of the bionic adaptive controller is a general phrase for tunning system parameters depending on the present state, and the second component is a trend-based compensation for adjusting the system parameters. This technique makes the system more intelligent and boosts its anti-interference capabilities. The stability and convergence are analyzed using the Lyapunov synthetic method, and thus the parameter restrictions of the MIMO system are provided. Finally, the strong anti-interference of the system is verified by the simulations.

Coordinate Control for an SMIB Power System with an SVC

To improve power quality in power systems vulnerable to current disturbances and unbalanced loads, a hybrid control scheme is proposed in the present paper. A hybrid adaptive robust control strategy is devised for an SMIB power system equipped with a static VAR compensator to ensure robust transient stability and voltage regulation (SVC). High-order sliding mode control is combined with a dynamic adaptive backstepping algorithm to form the basis of this technique. To create controllers amenable to practical implementation, this method uses a high-order SMIB-SVC model and introduces dynamic constraints, in contrast to prior approaches. Improved transient and steady-state performances of the turbine steam-valve system are the goals of the dynamic backstepping controller. A Lyapunov-based adaptation law is developed to address the ubiquitous occurrence of parametric and nonparametric uncertainty in electrical power transmission systems due to the damping coefficient, unmodeled dynamics, and external disturbance. High-order sliding mode (HOSM) control is used for generator excitation and SVC devices to construct finite-time controllers. The necessary derivatives for HOSM control are calculated using high-order numerical differentiators to prevent simulation instability and convergence issues. Simulations demonstrate that the suggested method outperforms conventionally coordinated and hybrid adaptive control schemes regarding actuation efficiency and stability.

Learning generalized Nash equilibria in monotone games: A hybrid adaptive extremum seeking control approach

In this paper, we solve the problem of learning a generalized Nash equilibrium (GNE) in merely monotone games. First, we propose a novel continuous semi-decentralized solution algorithm without projections that uses first-order information to compute a GNE with a central coordinator. As the second main contribution, we design a gain adaptation scheme for the previous algorithm in order to alleviate the problem of improper scaling of the cost functions versus the constraints. Third, we propose a data-driven variant of the former algorithm, where each agent estimates their individual pseudogradient via zeroth-order information, namely, measurements of their individual cost function values. Finally, we apply our method to a perturbation amplitude optimization problem in oil extraction engineering.

Spatial hybrid adaptive impedance learning control for robots in repetitive interactive tasks

The existing model-based impedance learning control methods can provide variable impedance regulation for physical human–robot interaction (PHRI) in repetitive tasks without interactive force sensing, however, these methods require the completion of the repetitive tasks with constant time, which restricts their applications. For PHRI in repetitive tasks with different completion time, this paper proposes a spatial hybrid adaptive impedance learning control (SHAILC) strategy by using the spatial periodic characteristics of the tasks. In the spatial hybrid adaptation, spatial periodic adaptation is used for estimating time-varying human impedance and differential adaptation is designed for estimating robotic constant unknown parameters. The use of deadzone modifications in hybrid adaptation maintains the accuracy of the parameter estimation when the tracking error is small relative to the modeling error. The control stability is analyzed by a Lyapunov-based analysis in the spatial domain, and the control effectiveness and superiority is illustrated on a parallel robot in repetitive tasks with different task completion time.

Dual Observer Based Adaptive Controller for Hybrid Drones

A biplane quadrotor (hybrid vehicle) benefits from rotary-wing and fixed-wing structures. We design a dual observer-based autonomous trajectory tracking controller for the biplane quadrotor. Extended state observer (ESO) is designed for the state estimation, and based on this estimation, a Backstepping controller (BSC), Integral Terminal Sliding Mode Controller (ITSMC), and Hybrid Controller (HC) that is a combination of ITSMC + BSC are designed for the trajectory tracking. Further, a Nonlinear disturbance observer (DO) is designed and combined with ESO based controller to estimate external disturbances. In this simulation study, These ESO-based controllers with and without DO are applied for trajectory tracking, and results are evaluated. An ESO-based Adaptive Backstepping Controller (ABSC) and Adaptive Hybrid controller (AHC) with DO are designed, and performance is evaluated to handle the mass change during the flight despite wind gusts. Simulation results reveal the effectiveness of ESO-based HC with DO compared to ESO-based BSC and ITSMC with DO. Furthermore, an ESO-based AHC with DO is more efficient than an ESO-based ABSC with DO.

An Adaptive Hybrid Control of Reduced Switch Multilevel Grid Connected Inverter for Weak Grid Applications

Grid-connected inverters have a very significant role in the integration of renewable energy resources with utility grids. However, in recent studies, it is revealed that grid-connected inverters are vulnerable to instability when the nature of the grid changes from strong to weak, which produces uncertainty and performance degradation. An increase in grid impedance decreases stability margins, tremendously increases total harmonic distortion after a certain limit, and amplifies the voltage harmonics in the grid. A cascaded reduced switch symmetrical multilevel inverter along with an adaptive hybrid control technique is proposed for injecting power generated from distributed energy resources efficiently and stably to the utility grid. This research contributes twofold: a multilevel inverter topology and the other is its control method. The multilevel inverter reduces total harmonic distortion and size of the filter while increasing power handling capability. The control unit of the proposed system further consists of two parts: one is the synchronous frame current controller, and the other is stationary frame adaptive harmonic compensators. The grid current controller which is working in a synchronous reference frame ensures regulated current injection to the grid. It is not favorable to implement a harmonic compensator in a synchronous reference frame due to computation complexities. Therefore, the stationary reference frame controllers are used for harmonic compensations. But the resultant harmonic compensators have narrow bandwidth. Thus, these are not robust against variation in grid frequency. In this research, this problem is resolved by adding the adaptive features within the harmonic compensators which shift its passing band according to the frequency of the grid while remaining with the same bandwidth. The proposed design of the hybrid frame controller is validated by considering a nine-level inverter connected with a weak grid.

Global Asymptotic Position and Heading Tracking for Multirotors Using Tuning Function-Based Adaptive Hybrid Feedback

This letter considers the problem of global asymptotic position and heading tracking for multirotors. We propose a hybrid adaptive feedback control law that globally asymptotically tracks a position and heading reference in the presence of unknown constant disturbances in both the translational and rotational dynamics. By employing a tuning function-based backstepping approach, the number of parameter estimates are minimized. Moreover, we propose a novel control law for the translational subsystem, which leads to a simpler virtual control law when backstepping. Global asymptotic heading tracking is achieved through a novel construction of the desired rotation matrix. The theory is verified through experiments on a quadrotor.

Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage

Piezo-actuated stage (P-AS) has become the topic of considerable interest in the realm of micro/nanopositioning technology in the recent years owing to its advantages, such as high positioning accuracy, high response speed, and large output force. However, rate-dependent (RD) hysteresis, which is an inherent nonlinear property of piezoelectric materials, considerably restricts the application of P-AS. In this research paper, we develop a Hammerstein model to depict the RD hysteresis of P-AS. An improved differential evolution algorithm and a least-squares algorithm are used to identify the static hysteresis sub-model and the dynamic linear sub-model for the Hammerstein model, respectively. Then, a hysteresis compensator based on the inverse Bouc–Wen model is designed to compensate for the static hysteresis of the P-AS. However, the inevitable modeling error presents a hurdle to the hysteresis compensation. In addition, external factors, such as environmental noise and measurement errors, affect the control accuracy. To overcome these complications, a hybrid adaptive control approach based on the hysteresis compensator is adopted to increase the control accuracy. The closed-loop system stability is analyzed with the help of the Lyapunov stability theory. Finally, experimental results indicate that the raised control approach is effective for the accurate positioning of P-AS.

An improved adaptive hybrid controller for battery energy storage system to enhance frequency stability of a low inertia grid

Despite the promising dynamic characteristics of battery energy storage system (BESS) for efficient and reliable use in stability enhancement of a low inertia grid due to the large-scale integration of renewable energy sources (RESs), existing BESS controllers are found to be complex, inefficient and less responsive to adapt any changes in frequency of the system. This paper presents a simple improved adaptive Fuzzy-ANFIS hybrid algorithm-based BESS controller. The proposed controller can emulate virtual inertia by controlling the active power flow to improve frequency stability i.e. frequency nadir and rate of change of frequency (ROCOF). Necessary modeling of the controller was carried out and its performance has been analyzed to evaluate the performance of the controller. With different case studies and comparative analysis, the controller has also demonstrated its potentials to improve the frequency nadir by 0.43 % for sudden load variation and 0.61 % for transient short circuit fault. The promising performance of the controller suggests its utility for better grid inertial support to a low inertia grid. Additionally, due to the improvement in ROCOF and settling time, the proposed controller thus can increase the overall inertia in addition to the power balance at point of common coupling (PCC).

Formation Control of Multiple Mobile Robots Based on Fuzzy Adaptive Sliding Mode Control

In order to solve the problems of slow convergence and chattering in formation control of multiple mobile robots, and to optimize the dynamic characteristics of formation system, a hybrid adaptive controller is proposed by analyzing the characteristics of different sliding mode reaching laws, which can accelerate the convergence speed and effectively suppress the chattering of the system. Firstly, the saturation function is introduced into the double power reaching law to form the saturation double power reaching law; Secondly, the hyperbolic tangent reaching law is used to improve the stability of the system when it approaches the sliding surface; Finally, fuzzy logic rules are used to adjust the coefficients of saturated double-power reaching law to optimize the overall dynamic parameters. Simulation results show that the designed controller can improve the convergence speed of the system, effectively suppress the chattering, and realize the fast and stable following of the follower robot to the pilot robot, which verifies the correctness and availability of the proposed controller.

An adaptive hybrid control strategy for a class of nonlinear systems

This study provides an adaptive fuzzy control scheme for the class of nonlinear uncertain strict-feedback systems with external disturbances. The backstepping and synergetic control theories are used to develop a robust control approach for this class of systems. The unknown behaviors of the system are estimated through fuzzy systems. The stability is analyzed using the Lyapunov method, which shows that the proposed control method ensures that all the signals of the closed-loop system are bounded, and the tracking error converges approximately to zero. Two simulation examples are given to demonstrate the efficiency of the developed control approach.

Simultaneous micropositioning and microvibration control of a magnetostrictive Stewart platform with synthesized strategy

In this paper, we present a hybrid adaptive feedforward and feedback controller with hysteresis compensator on a magnetostrictive-actuated multi degree of freedom (DOF) positioning and vibration isolation integrated system (PVIIS). The PVIIS is custom-developed and based on “cubic” Stewart configuration. It is challenging to develop an effective controller to systematically address the issues of cross-coupling, strut hysteresis and dynamics for the PVIIS. Magnetostrictive asymmetric hysteresis leads to the loss of closed-loop output performance with conventional linear controller. To this end, a novel comprehensive controller is proposed in this paper. With the inverse kinematics analysis, the spatial motion of the mobile plate of the PVIIS is decoupled into the independent motions of six actuator struts. As for controlling the single strut of the PVIIS, the hybrid adaptive feedforward (AFF) and proportional–integral–derivative (PID) feedback controller with asymmetric hysteresis compensator (HC) is constructed. Among them, the AFF sub-controller is characterized by an adaptive finite impulse response (FIR) filter whose coefficients are updated by normalized least-mean squares (NLMS) algorithm. The PID feedback sub-controller is introduced to enhance the ability of positioning and vibration isolation. The arctangent-polynomial modified Prandtl–Ishlinskii (APMPI) model is used to build the inversion-based asymmetric hysteresis compensator. The experimental set-up is built. The positioning and vibration isolation experiments are conducted. Experimental studies show that the output performances are best with the proposed controller compared against sole AFF, hybrid AFF and PID controllers. With the proposed controller, the relative positioning error can maintain around 2.83% when the PVIIS performs 2-DOF rotation in the presence of 0.5 Hz disturbance.

Hybrid Approach Control of Micro-Positioning Stage with a Piezoelectric Actuator

For a class of system with nonlinear hysteresis, this paper presents an adaptive hybrid controller based on the hybrid backstepping-sliding mode, and describes the controller analytically by the LuGre model. Both backstepping and the sliding mode techniques are based on the Lyapunov theory. Drawing on this common point, the authors developed a new controller combining the two control techniques with a recursive design. The design aims to achieve two effects: assuring the stability of the closed loop system, and improving the continuous performance of the tracking position trajectory. The performance of the proposed hybrid controller was verified by implementing the identified Piezo model. The results show that our controller can track the system output desirably with the reference trajectory.

Hybrid Adaptive Event-Triggered Platoon Control with Package Dropout

Hybrid Adaptive Event-Triggered Platoon Control with Package Dropout

Hybrid Adaptive Output Feedback Tracking for Stable Systems With Unknown Input Constraints

The problem of global output tracking is addressed for stable minimum phase systems, with constant uncertain parameters and constant disturbances belonging to a known compact set, in the presence of input constraints such as amplitude saturation. The relative degree, the system order, and the sign of the high frequency gain are assumed to be known. A hybrid adaptive output feedback control is designed to achieve global asymptotic output tracking, provided that there exists an input within the input constraints capable of tracking exactly the output reference signal. By virtue of the hybrid control approach, the closed-loop system is linear within each time interval between parameter updating. Moreover, no information on input saturation is required by the controller. In addition to the standard assumptions in adaptive control design when inputs are unconstrained, further requirements on the reference signals, on system stability and on parameter bounds, are needed when inputs are constrained.

Adaptive Hybrid Pitch Angle Control and MPPT for PMSG-Based Wind Power Generator Systems

This paper presents the hybrid adaptive control approach of a Permanent Magnet Synchronous Generator (PMSG) based on a variable wind speed turbine. The suggested control technique involves controlling both the pitch angle (β) and the hybrid MPPT algorithm, whose proper operation depends on the accuracy of the (β) control. The Fractional Proportional-Integral (FPI) controller-based (PA) control technique was simulated and contrasted with the Proportional-Integral (PI) controller. With further refinements, the hybrid Maximum Power Point Tracking (MPPT) algorithm was employed for optimal power/torque. The effectiveness of the proposed scheme has been verified through simulation work using MATLAB-SIMULINK. The results demonstrated that the adaptive control strategy has good performance and reliability in terms of generation efficiency. The MPPT strategy achieves maximum energy tracking at low speeds while maintaining system stability and constant DC voltages. The FPI controller enhances the performance of the controller by reducing the rise time by 0.3%, the settling time by 2%, and the accuracy of choosing the optimal angle by an average of 3%.

Setpoint Decision Support Strategy and Adaptive Hybrid Control of Greenhouse Climate: A Simulation Study

Setpoint Decision Support Strategy and Adaptive Hybrid Control of Greenhouse Climate: A Simulation Study

Finite-time synchronization of different dimensional chaotic systems with uncertain parameters and external disturbances

This paper proposes a new control scheme using two scaling matrices that realizes the finite-time synchronization of different-dimensional chaotic systems with parameter uncertainties and external disturbances. Firstly, based on Lyapunov stability theorem and finite-time stability theorem, the definition of finite-time synchronization of chaotic systems with different dimensions is introduced. Secondly, in the case of external disturbance and parameter uncertainty, an adaptive feedback hybrid controller and parameter adaptive laws are designed to synchronize different dimensional uncertain chaotic systems in finite-time. Then, according to the characteristics of the unknown parameters of the system, a transformation matrix is constructed to meet the needs of chaotic systems with different dimensions, and a simplified synchronization control scheme is designed. Finally, two numerical experiments are carried out to verify the effectiveness of the proposed methods.

Adaptive Neural-PID Visual Servoing Tracking Control via Extreme Learning Machine

The vision-guided robot is intensively embedded in modern industry, but it is still a challenge to track moving objects in real time accurately. In this paper, a hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) is proposed for dynamic visual tracking of the manipulator. The scheme extracts line features on the image plane based on a laser-camera system and determines an optimal control input to guide the robot, so that the image features are aligned with their desired positions. The observation and state–space equations are first determined by analyzing the motion features of the camera and the object. The system is then represented as an autoregressive moving average with extra input (ARMAX) and a valid estimation model. The adaptive predictor estimates online the relevant 3D parameters between the camera and the object, which are subsequently used to calculate the system sensitivity of the neural network. The ELM–PID controller is designed for adaptive adjustment of control parameters, and the scheme was validated on a physical robot platform. The experimental results showed that the proposed method’s vision-tracking control displayed superior performance to pure P and PID controllers.