Adaptive Switching Control Research Articles

Adaptive switching control of full state constrained nonlinear systems with unknown control directions

AbstractIn this article, an adaptive switching controller is designed for the stabilization control and the tracking control of nonlinear systems suffering from full state constraints, unknown control directions, and system uncertainties. Featured with changing signs and unbounded increasing magnitudes, a switching control structure is proposed to deal with the unknown control direction problem. In order to explore and finally fix the correct control direction, an adaptive switching rule, a switching function, and a reference function are constructed. An integral Lyapunov function is adopted for the design of the control law, which ensures that full state constraints will not be violated. Using this method, the nonlinear system can be properly controlled without extra estimations or approximations. The boundedness of all the states and the stability of the system are analyzed and guaranteed. Finally, simulations are conducted to validate the proposed method, and the results illustrate its effectiveness.

A coordination control between energy storage based DVR and wind turbine for continuous fault ride‐through

AbstractContinuous fault ride‐through (CFRT) issues often arise in wind power systems. CFRT results in continuous voltage fluctuations which is characterized by “initially decreasing and then increasing” and can significantly disrupt the normal operation of wind power systems. To mitigate CFRT related problems, one approach is the utilization of energy storage (ES) based dynamic voltage restorers (DVRs). Nevertheless, the prohibitive costs and substantial ES requirements hamper practical implementation. In this article, a control scheme incorporating adaptive mode switching and coordinated control is proposed. First, the adaptive mode switching control leverages the advantages of two DVR compensation methods to reduce the injected voltage amplitude. The mode switching is activated by the DC link voltage and utilizes the PQR transformation to unlock the phase‐locked loop (PLL). Subsequently, an adaptive coordination coefficient is introduced, which considers the margin of ES regulation power and rotor inertia, to maintain power balance during CFRT. This proposed control strategy not only enables wind turbines to seamlessly ride through continuous faults without disconnecting from the grid but also leads to a reduction in the ES compensation requirement. To validate the effectiveness of the proposed approach, simulations based on Simulink and hardware‐in‐loop (HIL) experiments are conducted.

Adaptive type-2 fuzzy-neural switching control for wastewater treatment process under several operating conditions

The effluent ammonia and total nitrogen variation lead to multiple operating conditions in wastewater treatment process (WWTP), which challenges the control strategies based on a single control model. Therefore, it is essential to design suitable control strategies for WWTP to enhance the control performance under different operating conditions. In this paper, an adaptive type-2 fuzzy-neural switching control (AT2FSC) strategy is proposed to solve this problem. First, a soft-sensing model is developed based on fuzzy neural network and influent data in WWTP to estimate the effluent ammonia nitrogen and total nitrogen status. Then, the online operating conditions are determined by the critical values of effluent ammonia nitrogen and total nitrogen. Second, the corresponding fuzzy rules are devised for different operating conditions and a type 2 fuzzy switching model (AT2FSM) is constructed to capture the operating status changes of WWTP. Then, the proposed AT2FSC can design the switching control strategy according to AT2FSM to adapt to the current operating condition. Third, the Lyapunov theorem ensures the stability of the proposed AT2FSC method. Then, it facilitates the further application of AT2FSC in WWTP. Finally, the performance of AT2FSC under various operating conditions is validated on the benchmark simulation model No.1 (BSM1). Simulation results under four different operating conditions demonstrate that the proposed AT2FSC strategy can reduce the excess peak of effluent ammonia and total nitrogen while maintaining the operating performance.

Partial‐state feedback adaptive stabilization for a class of uncertain nonholonomic systems

SummaryIn this paper, we investigate the global adaptive stabilization problem via partial‐state feedback for a class of uncertain chained‐form nonholonomic systems with the dynamic uncertainty and nonlinear parameterization. The notions of Sontag's input‐to‐state stability (ISS) and ISS‐Lyapunov function, together with the changing supply rates technique are used to overcome the dynamic uncertainty. The nonlinear parameterization is well treated with the aid of the parameter separation technique. The discontinuous input‐to‐state scaling technique is employed in this procedure to derive the global stabilization controllers. Additionally, we develop a switching adaptive control strategy in order to get around the smooth stabilization burden associated with nonholonomic systems. The simulation results illustrate the efficacy of the presented algorithm.

A Multiobjective Adaptive Switching Control for Bidirectional DC/DC Converter

A Multiobjective Adaptive Switching Control for Bidirectional DC/DC Converter

Adaptive Switching Control of Active Suspension Systems: A Switched System Point of View

Adaptive Switching Control of Active Suspension Systems: A Switched System Point of View

Robust switching adaptive tracking control for uncertain high-order fully actuated systems based on fully actuated system approaches

This paper investigates the switching adaptive tracking control problem for high-order fully actuated (HOFA) systems with time-varying uncertainty. A type of special differentiable functions, named as pulse-like functions, are introduced firstly. Based on the function and the HOFA system approaches, this paper designs a new robust switching adaptive tracking (RSAT) controller, which guarantees that the state vector and its derivatives of the HOFA system are globally bounded, and the system tracking error vector converges into a preseted neighborhood of equilibrium. Most importantly, the preseted neighborhood is completely unrelated to the system initial values and the uncertainty. Furthermore, different from traditional switching adaptive control (SAC) methods, of which the controllers are often discontinuous, the RSAT controller designed in this paper is continuous (even differentiable), which is a key improvement to enhance dramatically the practicability of SAC. Finally, three simulation examples including single-link robot illustrate the effectiveness and the practicability of our proposed control method.

Vehicular Platoon Control With Gain Variations via Unreliable VANETs

This paper investigates the adaptive control for vehicular platoon with controller gain variations and packet dropouts. With leader predecessor following topology, vehicle speed tracking and distance keeping are achieved by tracking-error based control. The gain variations are compensated for by introducing an adaptive estimation law. A switching adaptive control law is presented to deal with packet dropouts, in which only distance measurements are used when packet dropouts occur. Combining the switched system approach and adaptive control, a set of stability conditions are obtained to design the adaptive controller for each follower. The results present a unified framework for determining the controller gains and the estimator adaptation law, yielding an adaptive control algorithm that guarantees both internal stability and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{2}$</tex-math></inline-formula> string stability. Numerical examples demonstrate the effectiveness of our method.

Adaptive Switching Control Based on Dynamic Zero-Moment Point for Versatile Hip Exoskeleton Under Hybrid Locomotion

Adaptive Switching Control Based on Dynamic Zero-Moment Point for Versatile Hip Exoskeleton Under Hybrid Locomotion

Simulation optimal and design of 3-kW DC-DC converter for pure electric vehicles

There are significant differences between pure electric vehicles and traditional fuel vehicles. High-power converters are required to meet the increased energy consumption requirements of loads within pure electric vehicles. A high-power integrated DC-DC converter is designed to meet the requirements of pure electric vehicles. First, the characteristics of LLC resonant converters are analyzed, and a fuzzy adaptive PID frequency conversion phase-shift soft switching control strategy is proposed. Second, the fuzzy controller is designed based on the MATLAB toolbox based on the characteristics of electric vehicle converters. Then, in order to further improve the efficiency of the converter, a planar transformer is proposed and optimized. It is found that with the increase in the switching frequency, the loss is smaller, which is more conducive to improving the efficiency. Simulation results show that zero-voltage switching and zero-current switching can be realized by using the proposed control strategy, and experimental results show that the proposed strategy is significantly more efficient under non-heavy-load conditions. The minimum and maximum efficiency values are 92% and 97.38%, respectively. The high-power converter ensures the normal operation of the load of pure electric vehicles. This work provides a method for developing high-power DC-DC converters and improving efficiency.

Dual predictive model adaptive switching control for directional control of tractor semitrailer combinations

In order to ensure that safety and reliability of tractor semitrailer combinations (TSCs) on the road, the human drivers’ steering decisions need to comprehensively consider the trajectories and states of the tractor and semitrailer. For this purpose, a dual predictive model adaptive switching control decision for directional control is proposed. Firstly, a multi-point preview algorithm and a general regression neural network are designed to percept the current local target paths for the tractor and semitrailer. Then, a kinematic predictive model control algorithm for low-speed path tracking control and a dynamic predictive model control algorithm for high-speed path following and lateral stability control are established respectively. In addition, an S-type switching function is introduced to realize smooth switching between the two control algorithms. Finally, the directional control decision in this study is validated by the numerical simulations under different conditions and compared with single-point preview driver and the MPC driver without considering semitrailer. The results show that the proposed approach can accurately track the target path and effectively improve the high-speed lateral stability.

Neural network-based adaptive controller design for robotic manipulator subject to varying loads and unknown dead-zone

In this article, aiming at handling the trajectory tracking issue of industrial manipulator system (IMS) with modeling uncertainty, varying loads (VL) and unknown dead-zone characteristic, a compensation-based adaptive switching controller synthesis is proposed. In this scheme, the dynamic model of the IMS under VL is regarded as a switched system (SS) with a specified modal set. The nonlinear term related to plant model in each subsystem is approximated by radial basis function neural network (RBFNN) so as to avoid the reliance of the controller on the accurate model, and the unknown dead-zone is estimated and compensated by NN, from which the corresponding NN robust compensation term is developed to eliminate the potential perturbations and estimated errors. The designed controller with switching mechanism effectively solves the problem of degradation of the tracking accuracy caused by VL. Finally, the uniform ultimate boundedness of error signals is analyzed by the average dwell time (ADT) approach, multi-Lyapunov function method and the synthesized adaptive control law, and the effectiveness of the developed scheme is verified by simulation.

Observer-based Asynchronous Event-triggered Robust H∞, Adaptive Switching Control for Nonlinear Industrial Cyber Physical Systems Under Data Injection Attacks

Observer-based Asynchronous Event-triggered Robust H∞, Adaptive Switching Control for Nonlinear Industrial Cyber Physical Systems Under Data Injection Attacks

Multi‐input enhanced model reference adaptive control strategies and their application to space robotic manipulators

AbstractThe Enhanced Model Reference Adaptive Control (EMRAC) algorithm, augmenting the MRAC strategy with adaptive integral and adaptive switching control actions, is an effective solution to impose reference dynamics to plants affected by parameter uncertainties, unmodeled dynamics and disturbances. However, the design of the EMRAC solutions has so far been limited to single‐input systems. To cover the gap, this paper presents two extensions of EMRAC to multi‐input systems. The adaptive mechanism of both solutions includes the ‐modification strategy to assure the boundedness of the adaptive gains also in presence of persistent disturbances. The closed‐loop system is analytically studied, and conditions for the asymptotic convergence of the tracking error are presented. Furthermore, when the plant is subjected to unmatched disturbances, the ultimate boundedness of the closed‐loop dynamics, which are made discontinuous by the adaptive switching control actions, is systematically proven by using Lyapunov theory for Filippov systems. The problem of trajectory tracking for space robotic arms in presence of unknown and noncooperative targets is used to test the effectiveness of the novel multi‐input EMRAC algorithms for taming uncertain systems. Four EMRAC solutions are designed for this engineering application, and tested within a high fidelity simulation framework based on the Robot Operating System. Finally, the tracking performance of the EMRAC implementations is quantitatively evaluated via a set of key performance indicators in the joint space and operational space, and compared with that of four benchmarking controllers.

Adaptive Tracking Control of Hybrid Switching Markovian Systems with Its Applications

This paper focuses on the model reference adaptive tracking control problem of uncertain hybrid switching Markovian systems. The stochastic multiple piecewise Lyapunov function method is set up for designing a hybrid switching signal and a piecewise dynamic switching adaptive controller. The hybrid switching signal is presented to improve the adaptive tracking capability by providing plenty of adjusting time during the stochastic switching stage. A piecewise dynamic parameter projection adaptive control technique is developed, which provides more freedom in designing a model reference adaptive law. A set of piecewise dynamic switching adaptive controllers are designed such that all the signals of the tracking error system remain within a bounded region under the proposed hybrid switching signal, and the tracking error converges to a neighborhood of zero where the radius of the neighborhood can be made arbitrarily small by choosing the regulation parameters appropriately. Finally, the developed adaptive tracking control theory of uncertain hybrid switching Markovian systems is illustrated by using a numerical example and an application example of an electro-hydraulic model.

Predictive-adaptive sliding mode control method for reluctance actuator maglev system

The control performance of the reluctance actuator maglev system is seriously affected by inherent nonlinearities (e.g. hysteresis, eddy current, flux leakage, etc.) and external disturbances. To this end, this paper proposes an enhanced unknown system dynamics estimator (USDE)-based sliding mode control (USDE-SMC) method by a novel predictive-adaptive switching (PAS) controller (USDE-SMC-PAS). First, a USDE is incorporated into SMC to compensate for the uncertainties, including the external disturbance, the parametric uncertainty, and the model mismatch of inherent nonlinearities; second, an adaptive switching (AS) controller is used to reduce chattering; finally, the switching controller is modified by PAS to enhance the dynamic response ability and uncertainty rejection ability with reduced chattering. In the PAS controller, the switching gain comprises a filtered estimation error part and a residual error part, which are obtained by a first-order filter and an adaptive law, respectively. Consequently, the strong nonlinearity and uncertainty compensation ability, high levitation accuracy, high dynamic response, and reduced chattering property can be achieved simultaneously. The stability properties of USDE-SMC, USDE-SMC with an AS controller (USDE-SMC-AS), and USDE-SMC-PAS in the closed-loop system are investigated by the Lyapunov theorem. Simulations and experiments are provided to validate the effectiveness and superior performance of the proposed method.

Time-Driven Adaptive Control of Switched Systems With Application to Electro-Hydraulic Unit.

This article focuses on the H∞ adaptive tracking problem of uncertain switched systems. A key point of the study is to set up a multiple piecewise Lyapunov function framework which provides an effective tool for designing an adaptive switching controller consisting of a state-feedback and time-driven switching signal and a time-driven adaptive law. The proposed switching signal guarantees the solvability of the H∞ adaptive tracking problem for uncertain switched systems. Significantly, it provides plenty of adjusting time for the adaptive tracking control strategy to damp the transient caused by switching and avoids frequent switching. A novel time-driven adaptive switching controller is established such that the tracking error asymptotically converges to zero and all the signals in the error dynamic system are bounded under an achieved disturbance attenuation level. The solvability criterion ensuring an H∞ adaptive tracking performance is established for the uncertain switched systems, where the solvability of the H∞ adaptive tracking problem for individual subsystems is not required. Finally, the proposed method is applied to the electro-hydraulic unit.

A New Adaptive Switching Control Method for the Complex Operating Condition of the Grid-Connected Power System

Due to the increase in wind power accessing power system in recent years, the low-frequency oscillation of power system is more complicated. Therefore, a new adaptive switching control method combined with a nonlinear system is proposed for the complex operating conditions of the grid-connected power system. Firstly, this method decomposes the low-frequency oscillation control problem of the power system into two parts, which are the linear system control and the nonlinear system control. Then, in the linear system control part, the iterative identification method is used to control this part. In the nonlinear system control part, the nonlinear term is decomposed into a combination of the previous beating measurable and the nonlinear estimation increment. Secondly, the nonlinear system is divided into three different operating conditions by the nonlinear estimation increment. Thus, four different adaptive controllers are obtained. Then, the designed controller is switched according to the controlled object with four controllers by the switching function, which not only ensures the stability of the closed-loop system, but also improves the control performance of the controller under different operating conditions. Finally, the feasibility and effectiveness of this method are verified, and the controller can fit the control efficiency of the real power system well.

Switching Adaptive Control with Applications on Robot Manipulators

This paper concentrates on the study of logic-based switching adaptive control. Two different cases will be considered. In the first case, the finite time stabilization problem for a class of nonlinear system is studied. Based on the recently developed adding a barrier power integrator technique, a new logic-based switching adaptive control method is proposed. In contrast with the existing results, finite time stability can be achieved when the considered systems contain both fully unknown nonlinearties and unknown control direction. Moreover, the proposed controller has a very simple structure and no approximation methods, e.g., neural networks/fuzzy logic, are needed. In the second case, the sampled-data control for a class of nonlinear system is investigated. New sampled-data logic-based switching mechanism is proposed. Compared with previous works, the considered nonlinear system has an uncertain linear growth rate. The control parameters and the sampling time can be adjusted adaptively to render the exponential stability of the closed loop system. Applications in robot manipulators are conducted to verify the proposed results.

An Adaptive Switching Control Strategy under Heavy–Light Load for the Bidirectional LLC Considering Parasitic Capacitance

The LLC topology is widely used to link renewable energy and inverters to provide constant voltage in the smart grid. Due to its characteristics, the voltage regulation range under light load conditions is limited, so that the output voltage cannot be maintained constant. The adaptive switching control strategy is proposed in this paper to keep the output constant. Under heavy load conditions, the voltage is kept constant by adjusting the frequency to ensure the accuracy of the control. The phase shift is adjusted to achieve constant voltage, considering the influence of parasitic capacitance on the modeling process for the changing trend of output voltage in light load conditions. The switching point is calculated from the characteristic curve to ensure that the output voltage is stable during mode switching. In addition, there is a new hysteresis control which is robust near the switching point to cope with the instability of the new energy itself and frequent disturbance under light load. Finally, a 400V–36V–1KW prototype is used to verify this control strategy.