Adaptive Control Design Research Articles

Fixed-time adaptive fuzzy SOSM controller design with output constraint

Fixed-time adaptive fuzzy SOSM controller design with output constraint

Distributed synchronization control for multiple tailless flying‐wing UAVs with predefined tracking accuracy and input nonlinearities

SummaryThis article addresses the distributed adaptive synchronization tracking control problem for multiple flying‐wing UAVs with predefined accuracy and unknown input nonlinearities. Considering that the multiple flying‐wing unmanned aerial vehicles (UAVs) system is a large‐scale nonlinear and strongly coupled system, the control schemes in existing literature which only consider longitudinal, lateral or attitude control may not be applicable in practical engineering. Thus, a full‐state flying‐wing UAV model containing both translational and rotational motions is established, whose dynamics are six‐degree‐of‐freedom with twelve‐state‐variables. Furthermore, to handle the problem that the prior knowledge of the actuator nonlinearities of multi‐UAV system are difficult to obtain, a more general input nonlinear model is established and the adaptive boundary estimation technique is developed. Finally, a performance‐oriented controller is proposed by incorporating a series of smooth functions into adaptive neural control design and Lyapunov analysis. Theoretical analysis and simulation results show that the presented control scheme can guarantee the satisfactory transient tracking error performance, and the tracking error converges to a user‐defined interval ultimately.

Spatiotemporal adaptive state feedback control of a linear parabolic partial differential equation

AbstractThis article deals with the issue of asymptotic stabilization for a linear parabolic partial differential equation (PDE) with an unknown space‐varying reaction coefficient and multiple local piecewise uniform control. Clearly, the unknown reaction coefficient belongs to a function space. Hence, the fundamental difficulty for such issue lies in the lack of a conceptually simple but effective parameter identification technique in a function space. By the Lyapunov technique combined with a variant of Poincaré‐Wirtinger inequality, an update law is derived for estimate of the unknown reaction coefficient in a function space. Then a spatiotemporal adaptive state feedback control law is constructed such that the estimate of the unknown coefficient is bounded and the closed‐loop PDE is asymptotically stable in the sense of spatial norm if a sufficient condition given in terms of space‐time varying linear matrix inequalities (LMIs) is fulfilled for the estimated coefficient and the control gains. Both analytical and numerical approaches are proposed to construct a feasible solution to the space‐time varying LMI problem. With the aid of the semigroup theory, the well‐posedness and regularity of the closed‐loop PDE is also analyzed. Moreover, two extensions of the proposed adaptive control scheme are discussed: the PDE in ‐D space and the PDE with unknown diffusion and reaction coefficients. Finally, numerical simulation results are presented to support the proposed spatiotemporal adaptive control design.

Nonlinear Adaptive Fuzzy Control Design for Wheeled Mobile Robots with Using the Skew Symmetrical Property

This research presents a nonlinear adaptive fuzzy control method as an analytical design and a simple control structure for the trajectory tracking problem in wheeled mobile robots with skew symmetrical property. For this trajectory tracking problem in wheeled mobile robots, it is not easy to find an analytical adaptive fuzzy control solution due to the complicated error dynamics between the controlled wheeled mobile robots and desired trajectories. For deriving the analytical adaptive fuzzy control law of this trajectory tracking problem, a filter link is firstly adopted to find the solvable error dynamics, then the research is based on the skew symmetrical property of the transformed error dynamics. This proposed nonlinear adaptive fuzzy control solution has the advantages of low computational resource consumption and elimination of modeling uncertainties. From the results for tracking two simulation scenarios (an S type trajectory and a square trajectory), the proposed nonlinear adaptive fuzzy control method demonstrates a satisfactory trajectory tracking performance for the trajectory tracking problem in wheeled mobile robots with huge modeling uncertainties and outperforms the existing H2 control method.

Lyapunov-based adaptive PID controller design for buck converter

Lyapunov-based adaptive PID controller design for buck converter

Design and implementation of a new adaptive MPPT controller for solar PV systems

This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver the maximum available power to the load under solar radiation and atmospheric temperature changes. This article presents a new adaptive control framework to enhance the performance of MPPT, which will minimize the complexity in system control and efficiently manage uncertainties and disruptions in the environment and PV system. Here, the MPPT algorithm is decoupled with model reference adaptive control (MRAC) techniques, and the system gains MPPT with overall system stability. The simulation and design of the new MRAC for MPPT based on a boost converter are addressed here. Moreover, a mathematical model is formulated and an efficient MRAC is designed for MPPT. To validate the robustness of the controller, MATLAB/Simulink is utilized to compare with the state-of-the-art approach, which is incremental conductance (INC) and perturb & observe (P&O) under various operating conditions based on the convergence time, tracking efficiency, PV current & voltage ripple, overall efficiency, and error rates. The proposed controller’s average tracking efficiency is 99.77% and 99.69% under diverse temperature and radiation conditions, respectively. In addition, it takes only 3.6 msec to capture MPP, which is around ten times faster than INC and twelve times faster than the P&O approach. When compared to INC and P&O, the MPP error rates in the MRAC-MPPT scheme are significantly lower. The simulation outcomes indicate that the presented controller exhibits excellent tracking under varying circumstances like solar radiation and temperature.

Projection and Barrier Lyapunov-Based Controller Update Laws in MRAC Structure for Flexible Satellite Launch Vehicles

This paper investigates adaptive flight control design strategies which are robust to a wide range of both parametric and non-parametric uncertainties for a highly non-linear, time-varying and under-actuated launch vehicle system. In general, scheduled PID controllers are designed for the rigid body dynamics of the launch vehicle and structural filters/compensators are used to stabilize underactuated dynamics such as slosh and flexibility. In this work, two stable Lyapunov-based adaptive controllers are designed with full-state feedback. In the first controller, adapted gains are constrained to be within the limits defined by the user using a rectangular projection operator. In the second controller, tracking error is also constrained using a barrier Lyapunov function. States of the under-actuated dynamics and rigid body dynamics are estimated from the noisy attitude and attitude rate measurements using an extended Kalman filter. Tracking and regulation control studies are conducted to show the efficiency of the proposed controller update laws using different command signals.

Fractional order adaptive MRAC controller design for high-accuracy position control of an industrial robot arm

Most of conventional feedback controllers become inefficient in hard industrial environments like in steel industry, because of uncertainties in the plant model, or process dynamics variation due to nonlinear actuators, and changes in the character of the disturbances. This paper proposes an adaptive control design based on fractional order model reference adaptive control (FOMRAC) strategy in order to deal with an uncertain horizontal positioning control of an unloading machine in a rotary hearth furnace for hot rolling operation. The proposed FOMRAC scheme uses the MIT rule as an adaptive mechanism with two main modifications comparatively to the conventional MRAC: the reference model is an adequate fractional order system and the parameter adjustment rule contains a fractional order integrator. Stability analysis of the proposed control scheme is performed using the Lyapunov stability theorem. Numerical simulations are presented to show the effectiveness of the proposed fractional adaptive schemes applied to an industrial robot arm loading round steel blocks from inside a rotary hearth furnace. After comparison with the conventional MRAC, it is shown that the performances of FOMRAC are superior to classical control schemes.

Composite model-reference adaptive control with least-squares estimator

This work presents a new design of a composite model-reference adaptive control (MRAC) with a least-squares parameter estimator. The objective of the design is to preserve the remarkable transient adaptation characteristic obtained by a modified MRAC algorithm recently introduced and, at the same time, enjoy the superior parameter convergence performance of a least-squares estimator. The algorithm employs two different estimators for the same controller parameter, one updated by a gradient law driven by the tracking error and the other updated by a least-squares algorithm driven by a prediction error. In this way, the performance of each one is kept unchanged. The existence of a Lyapunov function assures the global uniform stability of the proposed composite adaptive scheme and simulation results confirm and illustrate its properties.

A Study on the Design of Error-Based Adaptive Robust RBF Neural Network Back-Stepping Controller for 2-DOF Snake Robot’s Head

In this paper, we will propose the controller of a 2-DOF head in a snake robot for effective image data reading when the snake robot is driving, and present an error-based adaptive robust radial base function neural network back-stepping controller. The snake robot head system is a nonlinear system, and there are unmeasurable disturbances that occur while driving. To solve this problem, we use back-stepping controller and radial basis function neural network to compensate for unmeasurable disturbances to improve the steady state. In order to further compensate for the network approximation error that occurs during training or large signal changes, we use adaptive coefficients and error functions to approximate the network approximation error and design adaptive robust terms. Compared to the previous controller, the proposed controller can actively compensate for large signal changes and has the advantage of not generating residual input in a steady state. The proposed controller is based on Lyapunov function candidate to design an adaptive law and prove the system stability. The stability of the control system is proven through Lyapunov analysis and bounded. The proposed controller compared and verified the controller performance for two inputs through simulation and presented the efficiency of the controller.

Analysis and Design of Adaptive Cruise Control for Smart Electric Vehicle With Domain-Based Poly-Service Loop Delay

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches.

Adaptive control of LTV systems with uncertain periodic coefficients

The paper considers the control problem for a class of uncertain linear time-varying systems. The time-varying (TV) parameters are represented by multisinusoidal functions with a priori unknown amplitudes, phases, and frequencies of harmonics. The maximum numbers of the harmonics are known. To compensate the influence of the TV parameters on the closed-loop system, based on internal model principle, TV parameters observers are designed, and the plant model is represented in the parameterized form with constant unknown parameters. This form is used for design of adaptive backstepping controller with modular identifiers with improved parameters tuning.

A Novel Approach for an Outdoor Oyster Mushroom Cultivation using a Smart IoT-based Adaptive Neuro Fuzzy Controller

An automatic environment control systems for greenhouses are turning to be very significant because of food demand, and rise in temperature and population of the world. This article proposes to design and implement a low cost, robust and water efficient autonomous smart internet of things (IoT) system to monitor and control the temperature, and humidity of an outdoor oyster mushroom growing unit. The IoT-based control system involves DHT22 sensors, ESP32 controller and actuators (water pump and cooling fan) to facilitate the adequate amount of air for circulation to maintain temperature and water to maintain humidity inside an outdoor oyster mushroom growing unit as per its requirement. A real working prototype is developed and implemented on integrating fuzzy inference system (FIS) in ESP32 controller using Arduino C with the help of its integrated design environment. The FIS is designed to calculate the switching on/off time of water pump and cooling fan on sensing current temperature, and humidity inside oyster mushroom unit with respect to ambient temperature, and humidity respectively. The prototype provides inside temperature, humidity, ambient temperature, ambient humidity, water pump time and fan time on Thing-speak platform in real time. Furthermore, the data is used for design and simulation of Adaptive Neuro Fuzzy Inference Controller for an outdoor oyster mushroom growing unit in MATLAB/Simulink to improve the performance of the system. The practical applicability of the proposed ANFIS controller over FIS Controller and industrial PID Controller is shown by simulation findings with use of experimental data. The system reduces water use as well as an extremely extraordinary administration required for monitoring the mushroom unit. In addition, it increases robustness of the system.

Design of Robust Adaptive Controller for Industrial Robot Based on Sliding Mode Control and Neural Network

Design of Robust Adaptive Controller for Industrial Robot Based on Sliding Mode Control and Neural Network

MIMO fuzzy adaptive control systems based on fuzzy semi-tensor product

<abstract><p>Based on fuzzy semi-tensor product (STP) algorithms and fuzzy relation matrix (FRM) models, the design of an adaptive fuzzy controller was proposed in this paper for the multivariable nonlinear systems with uncertainty. The controlled multi-input-and-multi-output (MIMO) plants were expressed and processed first by FRM models and fuzzy STP operations, and then the indirect adaptive fuzzy control laws were designed. The tracking property of the FRM models was proved for the control objective of MIMO systems. The effectiveness of the novel matrix expression was verified by simulations of the tracking control on a two-link rigid robot manipulator. Results in simulation tests show that the proposed design of adaptive FRM control is efficient for nonlinear multivariables. Therefore, the proposed indirect fuzzy adaptive controllers can be extended to general matrix expression for MIMO nonlinear systems with fuzzy STP algorithms and FRM models and online approximate unknown parameters, according to required accuracy.</p></abstract>

Use of Artificial Neural Networks in the Design of Adaptive Fuzzy Logic Controllers in the manufacturing of Prosthetic Knees

This paper presents real-time parameter updates that help to make adaptive machining process robust to changes in exogenous operating conditions. This work supports the commercial imperative for a customizable ‘batch size of one’ implant that does not unreasonably affect operating costs like tool life. A Neural Network (NN) is presented for use within a Computer Numerical Control (CNC) manufacturing cell to determine real-time tool offset adjustments in knee prostheses. This study integrates additional sensor inputs from the CNC cell, including improved force monitoring data for critical tools used in the machining of a Tibial component. The resulting time series force model is used to compare classification performance on Random Forest (RF) and Bi-directional Long Short Term Memory (BiLSTM) Neural Networks. In relation to network training performance, pre-processing improvements are reported for the RF algorithm using a time series data conversion process step. For the case of the BiLSTM model, 2D time series data is converted to a 3D array using a novel projection technique. The accuracy of both of methods is assessed in real-time using tool offset control applied using an adaptive fuzzy logic law. In this use case it has been observed that a RF approach exhibits less overfitting than its BiLSTM counterpart and therefore is a better choice for the dynamic computation of the tool offset height, the actuator output.

A Barrier Neuroadaptive Dynamic Surface Control Approach for Tank Gun Control Systems With Input Saturation

In harsh battlefield environments, tanks have to encounter some nonlinear characteristics including frictional moment, gear backlash and parameter drifts, etc. The existence of such nonlinear characteristics makes the controller design of tank gun control systems (TGCSs) challenging. In this paper, a barrier neuroadaptive control approach is proposed to handle the uncertainties and nonlinearities, so as to achieve satisfactory tracking performance for TGCSs. With a time-varying barrier Lyapunov function employed in controller design, the output error of TGCS is restricted within the preset bound during the control process. A radial basis function (RBF) neural network is built to approximate the uncertainties in tank gun control systems. An anti-windup control strategy is developed to deal with the input saturation nonlinearity, with a Nussbaum function used to compensate for the nonlinear term arising from input saturation. By reasonably applying filtering error into output-constrained adaptive backstepping control design, the three steps in the traditional backstepping control design are reduced to two steps. The asymptotic stability of the closed-loop TGCSs is proven by Lyapunov theory. Finally, a simulation example is presented to verify the effectiveness of the proposed control scheme.

A Novel Neural Network-Based Robust Adaptive Formation Control for Cooperative Transport of a Payload Using Two Underactuated Quadcopters

Designing a controller for the cooperative transport of a payload using quadcopter-type unmanned aerial vehicles (UAVs) is a very challenging task in control theory because these vehicles are underactuated mechanical systems. This paper presents a novel robust adaptive formation control design for the cooperative transport of a suspended payload by ropes using two underactuated quadcopters in the presence of external disturbances and parametric uncertainties. The structure of the proposed controller is divided into two subsystems: fully actuated and underactuated. An integral sliding mode adaptive control strategy is proposed for the fully actuated subsystem, and for the underactuated subsystem, an adaptive control strategy based on the combination of Backstepping and sliding mode is proposed. Then, the control parameters of the sliding surfaces of both control subsystems are adaptively tuned by a neural network. In addition, to improve the robustness of the proposed controller, a disturbance observer is incorporated to estimate and compensate the lumped disturbances. The asymptotic stability of the cooperative transport system is verified with the Lyapunov theorem. Finally, numerical simulations are performed in MATLAB/Simulink environment, and the results show that the proposed controller successfully transports the payload safely and without oscillations. Moreover, the desired formation pattern is maintained throughout the flight task, even with external disturbances and parametric uncertainties.

Adaptive Dead-Time Control Design with Low Dead-Time Error in 20 MHz 90 V GaN Gate Driver

This paper presents an adaptive dead-time control circuit for a maximum work frequency 20 MHz, maximum voltage level 90 V GaN gate driver. The dead-time is set to prevent straight-through of the upper and lower power transistors of the bridge arm structure and ensure the reliability of the motor driver. For GaN drivers on the market today, fixed or configurable dead-time is widely used in more-than-10 MHz application scenarios. However, the switching loss caused by insufficient dead-time and reverse turn-on loss caused by excessive dead-time of GaN devices have a hazardous influence on the efficiency of drivers, which fixed or configurable dead-time cannot avoid. The gate driver with the proposed adaptive dead-time control circuit has been implemented in a 0.18 um BCD process. The proposed adaptive control circuit dissipates 56.3 uA quiescent current in a simulation situation and is able to provide maximum 5 ns dead-time error for a GaN gate driver.

Adaptive Fuzzy Q-Learning Control Design and Application to Grid-Tied Nine-Level Packed E-Cell (PEC9) Inverter

This letter applies a multiagent fuzzy Q-learning (FQL) algorithm, incorporated with a model-free nonlinear controller (MFNC), entitled FQL-MFNC for stabilized controlling of a recently introduced grid-connected nine-level Packed E-Cell (PEC9) inverter under dynamical operation. Unlike previous tuning schemes, which concentrate on extracting mathematical formulation of a controlled plant, this letter investigates a fuzzy Q-learning agent for optimal design of PEC9. In first step, the fuzzy reinforcement learning is adopted to tune the MFNC controller in the simulation environment. In fact, the FQL algorithm finds the optimal policy based on a reward function for adjustment of the MFNC control coefficients to guarantee the grid-connectivity requirements under PEC9 dynamical operation are met. The experimental tests are conducted to assure efficiency and practicability of the designed multi-agent FQL-MFNC scheme on the single-phase grid-connected PEC9 inverter.