Self-tuning Control Scheme Research Articles

Adaptive backstepping controller based on a novel framework for dynamic solution of an ankle rehabilitation spherical parallel robot

AbstractThis research offers an adaptive model-based methodology for autonomous control of 3-RRR spherical parallel manipulator (RSPM) based on a novel modeling framework. RSPM is an overconstrained parallel mechanism that has a variety of applications in medical procedures such as ankle rehabilitation because of its precision and accuracy. However, obtaining a complete explicit dynamic model of these mechanisms for tracking purposes has been a problematic challenge due to their inherent singularities, coupling effects of the limbs, and redundant constraints imposed by the intermediate joints. This paper presents a novel algorithm to obtain the analytical kinematic solutions of RSPMs based on the closed-loop vector method, which includes constraint analysis. By incorporating constrained kinematics into the dynamic model, a comprehensive explicit dynamic solution of the non-overconstrained version 3-RCC of RSPM is developed in task space, based on screw theory and the linear homogeneous property of algebraic equations on the manipulator twist. Based on the proposed computational framework, a robust self-tuning backstepping control (STBC) strategy is applied to the robot to overcome the effect of external disturbances and time-varying uncertainties. Furthermore, an observer-based compensation (OBC) method is presented for dealing with the nonlinear hysteresis loops of the ankle during trajectory tracking purposes. The closed-loop stability of the whole system including STBC and OBC is theoretically performed by Lyapunov methods. The proposed methodologies are validated by realistic co-simulations in different scenarios. For instant, in the presence of external disturbances, the maximum tracking error norm of STBC is 37.5% less than the sliding mode approach.

Adaptive and intelligent control of a dual-arm space robot for target manipulation during the post-capture phase

Dual-arm space robots provide a promising solution for efficiently and flexibly completing complex on-orbit servicing tasks. However, their effectiveness is often hindered by strong dynamic coupling between the robotic arms and the satellite base in the microgravity environment. In this paper, both robotic arms are used as task arms, and a system model that considers the closed-loop constraint formed after a dual-arm space robot captures a target is established. On this basis, two types of control strategies, namely self-tuning control strategy (STC) and radial basis function neural network control strategy (RBF) are proposed for trajectory tracking of the target transferred or detumbled by the robot arms in the presence of system uncertainty or external disturbances. The control methods are verified and compared against the traditional computed torque control strategy (CTC) in terms of control accuracy and system robustness through simulation. The results demonstrate the excellent robustness of both RBF and STC strategies. RBF outperforms the other strategies in terms of control accuracy, while STC proves to be the most energy-efficient.

An ADRC Parameters Self-Tuning Control Strategy of Tension System Based on RBF Neural Network

High precision control of substrate tension is the premise and guarantee for producing high-quality products in roll-to-roll precision coating machine. However, the complex relationships in tension system make the problems of decoupling control difficult to be solved, which has limited the improvement of tension control accuracy for the coating machine. Therefore, an ADRC parameters self-tuning decoupling strategy based on RBF neural network is proposed to improve the control accuracy of tension system in this paper. Firstly, a global coupling nonlinear model of the tension system is established according to the composition of the coating machine, and the global coupling model is linearized based on the first-order Taylor formula. Secondly, according to the linear model of the tension system, a parameters self-tuning decoupling algorithm of the tension system is proposed by integrating feedforward control, ADRC and RBF. Finally, the simulation results show that the proposed tension control strategy has good decoupling control performance and effectively improves the tension control accuracy for the coating machine.

Study on torsional vibration characteristics and suppression of electric vehicles with dual-motor drive system

The battery electric vehicles with dual-motor coupling drive system have serious torsional vibration problems when the driving torque changes rapidly. This paper targets at reducing the negative effect of the torsional vibration with an active control strategy. The torsional vibration model of the dual-motor coupling drive system is established through the lumped mass method. A four-inertia modeling method which can reflect the powertrain configuration characteristics is proposed, by simplifying the traditional lumped mass model, and the feasibility is verified based on the error comparing of the torsional vibration characteristics between the two models. Combined Kalman filter theory, PID control theory and fuzzy control theory, a real-time self-tuning active control strategy is proposed to compensate motor torque by considering vehicle acceleration as the control input. Finally, the four-inertia model with the control strategy is simulated under such conditions as the torque abrupt change and the C-WTVC driving cycle. The simulation results indicated that the proposed active control strategy performs well in the torsional vibration suppression and the control effect is robust to the different structural parameters.

A Self-Tuning Adaptive Control Scheme for a Grid-Connected Three-Phase PV System

This article presents a new self-tuning sinusoidal recursive controller (STSRC) for handling the uncertainties and disturbances in a three-phase grid-connected photovoltaic system (GCPVS). The performance of a GCPVS is greatly influenced by the uncertainties, such as intermittency in solar irradiation, change in capacitance and equivalent series resistance (ESR) of dc-link capacitor, and variation of impedance between the inverter and the grid. This STSRC comprises improved linear sinusoidal tracer-based recursive least-square estimation (IRLS) to achieve robust performances, despite the parametric uncertainties through real-time estimation of its parameters, and updation of its gains. The efficacy of the proposed STSRC is verified through simulation using MATLAB/Simulink and experimental studies pursued on a 2.5-kW prototype. It is observed that STSRC exhibits superior tracking of PV voltage with reduced ripple and improved grid current with reduced total harmonic distortion (THD) of 1.66%. The estimation accuracy is found above 92%.

부하토크의 변동이 있는 PMSM을 위한 MRAS 기반 셀프튜닝 PI 속도제어

In this paper, we present the model reference adaptive system (MRAS)-based self-tuning speed control method for permanent magnet synchronous motor (PMSM) with a variety of load torque. To do this, the dynamic model of the PMSM is firstly formulated in a d-q reference frame linked with the rotor. Next, the self-tuning propositional-integral (PI) speed control is proposed for regulating the rotor speed of PMSM. While self-tuning PI speed control strategy enhances the performance of PMSM with a complete model-free approach. The electrical angle and rotor speed are effectively estimated by the MRAS-based control strategy. Then, the stable self-tuning method is used to update the control gains to enhance the ability of speed control performance in PMSM. Finally, the effectiveness and robustness of the proposed control techniques is demonstrated through some simulations using mahematical PMSM model.

An intelligent control system for robot massaging with uncertain skin characteristics

PurposeTo ensure accurate position and force control of massage robot working on human body with unknown skin characteristics, this study aims to propose a novel intelligent impedance control system.Design/methodology/approachFirst, a skin dynamic model (SDM) is introduced to describe force-deformation on the human body as feed-forward for force control. Then a particle swarm optimization (PSO) method combined with graph-based knowledge transfer learning (GKT) is studied, which will effectively identify personalized skin parameters. Finally, a self-tuning impedance control strategy is designed to accommodate uncertainty of skin dynamics, system delay and signal noise exist in practical applications.FindingsCompared with traditional least square method, genetic algorithm and other kinds of PSO methods, combination of PSO and GKT is validated using experimental data to improve the accuracy and convergence of identification results. The force control is effective, although there are contour errors, control delay and noise problems when the robot does massage on human body.Originality/valueIntegrating GKT into PSO identification algorithm, and designing an adaptive impedance control algorithm. As a result, the robot can understand textural and biological attributes of its surroundings and adapt its planning activities to carry out a stable and accurate force tracking control during dynamic contacts between a robot and a human.

Deep reinforcement learning based parameter self-tuning control strategy for VSG

With the development of new energy technology, the distributed generation has attracted more and more attention. In order to enhance the inertia of distributed generator system to improve its stability, the control technology of virtual synchronous generator (VSG) is proposed. However, the traditional VSG control technology often has poor flexibility and long dynamic adjustment time. In this context, a deep deterministic policy gradient (DDPG) algorithm based adaptive controller is designed to realize the adaptive control of inertia and damping coefficient in the system, so that the parameters can be adjusted adaptively under different operating conditions. Next, a simulation model of VSG isolated island single machine operation model is built in MATLAB Simulink, and the implementation of DDPG algorithm is given and verified by simulation. The results show that the VSG parameter adaptive system controlled by DDPG has stronger ability to resist disturbance and achieves better performance than the traditional VSG adaptive system. The DRL model only takes 0.448 s to return to stable, but the other two models need 0.632 s and 0.818 s respectively.

Modelling and analysis of inertia self‐tuning phase control strategy for a floating multi‐body wave energy converter

Wave energy converters (WECs) exhibit higher energy capture capacity when the wave frequency is close to the nature frequency of the device. A possible way to enhance energy capture efficiency is to modify the nature frequency of a WEC to match the incoming wave frequency. This paper presents an inertia self-tuning phase control strategy which can optimize the wave energy capture efficiency of a floating multi-body (FMB) WEC by regulating automatically the total mass of the capture energy part according to the incoming wave conditions. A variable-frequency inertia self-tuning control system is designed to conduct the proposed control strategy. The mathematical model of rotational motion of the FMB WEC is built and the dynamic performance of the control system is analysed in detail. Simulation results illustrate the proposed control strategy can achieve a phase optimization and hence significantly improve wave energy extraction of the FMB WEC in both regular and irregular waves. For regular waves, the maximum capture width ratio)CWR( under the control strategy is 7.3 times higher than that no control strategy is used. For irregular waves, the maximum CWR under the control strategy is 4.4 times that without the control strategy at the same wave frequency.

Performance Comparison of Various controllers on Semi-Active Vehicle Suspension System

The value of a self-tuning adaptive semi-active control scheme for automotive suspension systems is discussed in this paper. The current vehicle suspension system uses fixed-coeffcient springs and dampers. The ability of vehicle suspension systems to provide good road handling and improve passenger comfort is usually valued. Passive suspension allows you to choose between these two options. Semi-Active suspension(SAS), on the other hand, can provide both road handling and comfort by manipulating the suspension force actuators directly. The semi-active suspension system for a quarter car model is compared to passive and various controllers such as Proportional-Integral, Proportional-Integral-Derivative, Internal model control (IMC)-PID, IMC-PID with filter, FUZZY, and Adaptive-network-based fuzzy inference system(ANFIS) in this analysis. This research could be relevant in the future for designing better car suspension adjustments to eliminate vertical jerks and rolling motion experienced by the vehicle body on bumps and humps.

Switched-Capacitor Tuning for Three-Phase Wireless Power Receiver Based on Perturbation and Observation Method

To realize the tuning of three-phase dynamic wireless power receivers for rail vehicles, a non-real-time static self-tuning scheme based on the switched capacitor and the perturbation and observation (P&O) method is studied. First, the reactance regulation characteristics of the parallel and the series switched capacitor are discussed and compared. Then, to avoid the sophisticated mistuning detection, in the case of only observing the output voltage, the feasibility of using the P&O method to tune three phases of the receiver sequentially is analyzed. Furthermore, theoretical analysis shows that the switched capacitor can also be used to control the current phase difference between the three phases. Thereby, by introducing the current phase difference control, a fast three-phase simultaneous tuning strategy is proposed without additional hardware. Afterward, the self-tuning control scheme for the star-connected series-compensated receiver with the parallel and series switched capacitor is design in detail and be applied to a scaled-down prototype of a three-phase dynamic wireless power transfer system for verification. The result shows that the receiver can be tuned within the ±5% deviation of the main compensation capacitance, and the tuning speed of the proposed simultaneous tuning of three phases is about three times that of the sequence tuning.

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed.

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

Investigation of Advanced Control Schemes for High-speed Small-Hole EDM Drilling

High-speed small-hole electrical discharge machining (EDM) drilling is extensively applied to machine cooling film holes of turbine blades and diesel nozzle spray holes due to its non-contact characteristics and dedicated features, e.g. high-speed rotation and high-frequency pulse power generator. However, with increasing drilling depth, the process stability could be easily deteriorated by frequent debris due to restricted flushing conditions. Consequently, the machining efficiency and surface finish of high-aspect-ratio holes cannot be guaranteed. To address this problem, this paper proposes a sliding mode and self-tuning control scheme to regulate the discharging process. An integral function is presented as the switch mode to avoid chattering, and a direct algorithm that reparameterizes the model in terms of controller parameters is considered to reduce computation. The presented comparative drilling experiments with high aspect ratio (nearly 100) show the benefits of the adaptive controller.

Self-tuning NMPC of an Engine Air Path

Many automotive systems such as engines have manufacturing tolerances or change over time. This limits the performance of controllers tuned for the nominal case. A robust controller can not always overcome this performance gap. Against this background, in this work, we propose a self-tuning control strategy for an engine air path model obtained from data of a real engine and show its benefits setting. The self-tuning control consists of an online parameter estimation algorithm for polynomial non-linear autoregressive with exogenous input (PNARX) models and a nonlinear model predictive controller (NMPC) implemented by the continuation/generalized minimum residual (C/GMRES) algorithm. In a first step design of experiments (DOE) is utilized to identify a PNARX model offline from measurements performed on an engine test bed. A tracking NMPC is designed for this model and applied in simulation on the identified model. The control performance is assessed for the case of a wrong initial guess. It is shown that the resulting performance gap can be overcome by the online parameter estimation of a k-step prediction model with directional forgetting. An improved closed loop control performance of the air path model confirms the approach.

Robust stabilisation of rotary inverted pendulum using intelligently optimised nonlinear self-adaptive dual fractional-order PD controllers

ABSTRACTThis article presents an intelligently optimised self-tuning fractional-order control scheme to improve the attitude-stabilisation of an inverted pendulum. Primarily, the scheme employs two Fractional-order Proportional-Derivative (FPD) controllers acting concurrently on the system to minimise the deviations in its state-trajectories. Wherein, one FPD controller compensates the variations in pendulum-angle and its fractional-order derivative to vertically balance the pendulum, where as the other FPD controller acts as a position controller and regulates the variations in arm-angle and its fractional-order derivative. The integration of fractional calculus with conventional PD controllers optimises the reference-tracking performance of the control scheme by increasing its degrees-of-freedom and design flexibility. In order to further improve the system’s immunity against exogenous disturbances, the PD gains of each controller are dynamically adjusted after each sampling interval using piecewise nonlinear functions of their respective state-variations. The hyper-parameters of the nonlinear gain-adjustment functions as well as the fractional-number power of the derivative-operator of each controller are selected via Particle-Swarm-optimisation (PSO) algorithm. The proposed adaptive control scheme is tested on the QNET Rotary Inverted Pendulum setup via ‘hardware-in-the-loop’ experiments. The optimality and robustness of the proposed control scheme are validated by comparing its performance with PSO-based fixed-gain dual-PD and dual-FPD control schemes.

Self-Tuning Neural Predictive Control Scheme for Ultrabattery to Emulate a Virtual Synchronous Machine in Autonomous Power Systems.

An adaptive neural predictive controller (ANPC) is proposed for an ultrabattery energy storage system (UBESS) to enable its operation as a virtual synchronous machine (VSM) in an autonomous wind-diesel power system. The proposed VSM emulates the inertial response and oscillation damping capability of a typical synchronous machine (employed in conventional power plants) by adaptively controlling the power electronic interface of the UBESS. The control objective is to support the network frequency while ensuring efficient/economic use of the UBESS energy. During the load-generation mismatch, ANPC continuously searches for optimal VSM parameters to minimize the actual frequency variations, their rate of change of frequency (ROCOF), and the power flow through the UBESS while maintaining the state of the charge (voltage) of the ultrabattery bank to tackle subsequent disturbances. Simulations confirm that the proposed self-tuning VSM achieves similar performance as that of other VSM control schemes while substantially reducing the power flow through the UBESS and, hence, uses significantly less energy per hertz improvement (in frequency). An index is used to evaluate the performance of the proposed scheme. In addition, the self-tuning VSM has a better dynamic response (quantified as a reduction in ROCOF and settling times) while attenuating the frequency excursions for all simulated cases.

Application of an Additive Self-tuning Controller for Static Synchronous Series Compensator for Damping of Sub-synchronous Resonance Oscillations

In this paper, an additive self-tuning (ST) control scheme is presented for a static synchronous series compensator (SSSC) to improve performance of conventional PI control system for damping sub-synchronous resonance (SSR) oscillations. The active and reactve series compensation are provided by a three-level 24-pulse SSSC and fixed capacitor. The proposed ST controller consists of a pole shift (PS) controller and a recursive least squares (RLS). The RLS identifier algorithm is used to estimate parameters of auto regressive moving average exogenous (ARMAX) model. With this scheme, there is no any necessity for retuning the PI controller parameters under different operational circumstances of the system. The SSR analysis are performed using eigenvalue analysis, transient simulation and FFT analysis. The considered test system is adapted from IEEE First Benchmark Model. As shown in the results, the proposed additive ST controller has an effective and acceptable performance for damping of SSR under different disturbances and operating conditions. It should be noted here that all digital simulations have been done by using MATLAB.

Design of an Implicit Self-tuning PID Controller Based on the Generalized Output

PID controllers have been widely employed in real processes. Since PID parameters strongly affect the control performance, lots of schemes for tuning PID parameters have been proposed. Fixed PID controllers are applied mainly. However, it is impossible to obtain good control performance for time-variant systems by the fixed PID controllers. Therefore, it is important to tune PID parameters in an on-line manner. In addition, especially in process systems, it is difficult to disturb the systems to identify. Since, it is important to determine PID parameters directly from the closed-loop operating data. In this paper, in order to overcome these problems, an implicit self-tuning PID control scheme is proposed. In the proposed method, a new parameter tuning law using obtained closed-loop data is introduced, and recursive least squares method is applied to the scheme. This scheme has a reference model which can be designed by users. As a result, the system output can track the desired reference model output. The new parameter tuning law can calculate PID gains directly from closed-loop data, so the proposed method belongs to implicit schemes. Therefore, a system identification can be avoided. In addition, procedures of the scheme are very simple. Hence, the computation cost becomes low. The effectiveness of the control scheme is evaluated by an experimental example.

Self-Tuning Fuzzy Control for Seismic Protection of Smart Base-Isolated Buildings Subjected to Pulse-Type Near-Fault Earthquakes

Pulse-type near-fault earthquakes have obvious long-duration pulses, so they can cause large deformation in a base-isolated system in contrast to non-pulse-type near-fault and far-field earthquakes. This paper proposes a novel self-tuning fuzzy logic control strategy for seismic protection of a base-isolated system, which can operate the control force of the piezoelectric friction damper against different types of earthquakes. This control strategy employs a hierarchic control algorithm, in which a higher-level supervisory fuzzy controller is implemented to adjust the input normalization factors and output scaling factor, while a sub-level fuzzy controller effectively determines the command voltage of the piezoelectric friction damper according to current level of earthquakes. The efficiency of the proposed control strategy is also compared with uncontrolled and maximum passive cases. Numerical results reveal that the novel fuzzy logic control strategy can effectively reduce the isolation system deformations without the loss of potential advantages of base-isolated system.

Robust adaptive speed regulator with self-tuning law for surfaced-mounted permanent magnet synchronous motor

This study exhibits a self-tuning speed control scheme for the surface-mounted permanent magnet synchronous motor (SPMSM) against the parameter variations through the multivariable approach. The proposed method has two novelties. The first one is to combine the adaptive controller with the self-tuning algorithm so as to make the decay ratio of the tracking errors higher in the transient period. The second one is to provide a systematical way to find a robust stabilizing control gain corresponding the speed and current tracking errors by solving an optimization problem. The efficacy of the proposed method was experimentally investigated using a 3-kW SPMSM.