Unmodeled Nonlinearities Research Articles

Adaptive Online Neural Predictive Control of Hydraulic Actuator Using FPGA Acceleration

Hydraulic actuators have recently been widely used in control systems as efficient output devices. However, high-precision control of hydraulic actuators in industrial applications remains challenging due to the conflict between controllers' complexity, especially in theoretical modeling, and their generalization abilities, including adaptability to parametric uncertainties and versatility for various working conditions. To address this issue, we propose an adaptive online neural predictive control (AONPC) scheme, which is capable of handling unmodeled nonlinearities and uncertainties with a simple structure and low demand for system modeling. Specifically, an artificial neural network (ANN) is responsible for data-driven system identification and predictive control, while upper confidence bound (UCB) algorithm is introduced for hyperparameter self-adaptation. The ANN structure and the iteration number for solving the optimization problem are determined through a simulation study. Then, to complete the whole control loop within a short and fixed tracking interval, the proposed controller is implemented on a field programmable gate array (FPGA), which can effectively improve the tracking precision. Finally, extensive experimental results on a realistic hydraulic actuator device demonstrate the superiority of the proposed controller.

Adjustments for an HVDC link fault detector: practical considerations

In this work, practical considerations regarding the use of a fault diagnosis scheme based on a distributed adaptive estimator for the high-voltage direct current (HVDC) transmission line within a simplified model of the DC link are presented. Due to transients and steady-state ripple caused by unmodeled nonlinearities in the HVDC system's conversion part, parameter adjustments of the distributed estimator, including gains and thresholds, are performed to successfully determine the fault magnitude and position within the line through simulation studies. To validate the proposed scheme, numerical simulations of the diagnostic scheme are compared with two HVDC links: a simplified one and the CIGRE (International Council on Large Electric Systems).

Composite Control Law for Nonlinear Systems With Mismatched Disturbances for a Ball-Ramp Dual-Clutch Transmission

The dual-clutch transmission (DCT) was developed to increase the transmission efficiency and the shift performance. However, in a DCT, due to uncertainty related to the actuator, the tie-up phenomenon can arise, in which two clutches engage together or the clutch torque control performance deteriorates. Especially, the actuator uncertainty increased when using a special mechanism to increase controllability and efficiency. Among them, the ball-ramp DCT (BR-DCT), which uses a self-energizing mechanism, can reduce the consumption of actuator energy while also reducing the tie-up effect. However, the nonlinearity of the actuator must be considered, such as friction between parts and change in friction coefficient. Among the methods by which this type of uncertainty is estimated, the nonlinear disturbance observer is most effective when used to estimate the unmodeled nonlinearity of an actuator and time-varying uncertainty. In order to execute disturbance rejection control to improve the performance of the shift controller using the estimated uncertainty, it is necessary to configure disturbance compensation input. However, because the BR-DCT powertrain includes mismatched disturbances and nonlinearity of the actuator, it is difficult to apply the existing methods on the composite control law to shift controller. Therefore, in this study, we propose a composite control law to guarantee integrated stability in nonlinear systems such as a BR-DCT powertrain. The proposed method was verified through a powertrain test bench equipped with a BR-DCT. Finally, the proposed composite control law was able to converge both the tracking error and the disturbance estimation error.

Hybrid gray and black-box nonlinear system identification of an elastomer joint flexible robotic manipulator

Series elastic actuators possess several properties that make them widely used in collaborative robots, which play a major role in the current paradigm of industry 4.0. However, the compliant element responsible for those desired properties can also be responsible for the addition of unmodeled nonlinearities in the system. Therefore, the aim of this paper is to propose a novel hybrid model approach and apply it in the modeling of an elastomer-based series elastic joint. The proposed hybrid model combines a phenomenological gray-box model with a black-box Nonlinear Auto-regressive model with Exogenous inputs, which is able to provide the desired physical insight while enhancing accuracy by addressing the unknown nonlinearities. The results showed that the proposed hybrid model is more than 60% more accurate than the phenomenological model, considering the mean square error, and obtained a multiple correlation coefficient up to 0.97, indicating its capacity to be used in the construction of a digital twin of the system.

Adaptive sliding mode security control of wheeled mobile manipulators with Markov switching joints against adversarial attacks

This paper deals with the security control problem of a wheeled mobile manipulator that is subjected to various challenges, such as Markov switching parameters, actuator false data injection attacks, un-modeled nonlinearities, and deficient transition rates. As actuator attacks are often not fully known or predictable, an adaptive scheme is proposed to dynamically eliminate their negative effects. To achieve an ideal sliding motion, a smooth integral hyperplane is introduced. Since the transition rates of the joints may vary, it is important to design a controller that can maintain the expected sliding motion under different mode information. Additionally, stochastic stability analysis is conducted for the sliding motion considering different types of transition rates. Finally, simulation results are provided to demonstrate the effectiveness of the proposed method.

Model-free continuation of periodic orbits in certain nonlinear systems using continuous-time adaptive control

This paper generalizes recent results by the authors on noninvasive model-reference adaptive control designs for control-based continuation of periodic orbits in periodically excited linear systems with matched uncertainties to a larger class of periodically excited nonlinear systems with matched uncertainties and known structure. A candidate adaptive feedback design is also proposed in the case of scalar problems with unmodeled nonlinearities. In the former case, rigorous analysis shows guaranteed performance bounds for the associated prediction and estimation errors. Together with an assumption of persistent excitation, there follows asymptotic convergence to periodic responses determined uniquely by an a priori unknown periodic reference input and independent of initial conditions, as required by the control-based continuation paradigm. In particular, when the reference input equals the sought periodic response, the steady-state control input vanishes. Identical conclusions follow for the case of scalar dynamics with unmodeled nonlinearities, albeit with slow rates of convergence. Numerical simulations validate the theoretical predictions for individual parameter values. Integration with the software package coco demonstrates successful continuation along families of stable and unstable periodic orbits with a minimum of parameter tuning. The results expand the envelope of known noninvasive feedback strategies for use in experimental model validation and engineering design.

Formation flying along libration point orbits using chattering attenuation sliding mode control

This paper studies a control law to achieve formation flying in cislunar space. Utilizing the eigenstructure of the linearized flow around a libration point of the Earth-Moon circular restricted three-body problem, the fuel efficient formation flying controller based on the chattering attenuation sliding mode controller is designed and analyzed. Numerical studies are conducted for the Earth-Moon L2 point and a halo orbit around it. The total velocity change required to achieve formation as well as to maintain the orbit is calculated. Simulation results show that the chattering attenuation sliding mode controller has good performance and robustness in the presence of unmodeled nonlinearity along the halo orbit with moderate fuel consumption.

Robust Nonlinear Tracking Control for Unmanned Aircraft in the Presence of Wake Vortex

The flight trajectory of unmanned aerial vehicles (UAVs) can be significantly affected by external disturbances such as turbulence, upstream wake vortices, or wind gusts. These effects present challenges for UAV flight safety. Hence, addressing these challenges is of critical importance for the integration of unmanned aerial systems (UAS) into the National Airspace System (NAS), especially in terminal zones. This work presents a robust nonlinear control method that has been designed to achieve roll/yaw regulation in the presence of unmodeled external disturbances and system nonlinearities. The data from NASA-conducted airport experimental measurements as well as high-fidelity Large Eddy Simulations of the wake vortex are used in the study. Side-by-side simulation comparisons between the robust nonlinear control law and both linear H∞ and PID control laws are provided for completeness. These simulations are focused on applications involving small UAV affected by the wake vortex disturbance in the vicinity of the ground (which models the take-off or landing phase) as well as in the out-of-ground zone. The results demonstrate the capability of the proposed nonlinear controller to asymptotically reject wake vortex disturbance in the presence of the nonlinearities in the system (i.e., parametric variations, unmodeled, time-varying disturbances). Further, the nonlinear controller is designed with a computationally efficient structure without the need for the complex calculations or function approximators in the control loop. Such a structure is motivated by UAV applications where onboard computational resources are limited.

Distributed Nash equilibrium seeking for noncooperative games in nonlinear multi‐agent systems: An event‐triggered neuro‐adaptive approach

AbstractThis paper proposes a distributed Nash equilibrium seeking strategy for noncooperative games over strongly connected topologies, where the players suffer from unmodeled nonlinearities and external disturbances. By using the neural network (NN) universal approximation theorem, a neuro‐adaptive seeking controller is proposed. Besides, the event‐triggered mechanism is introduced to reduce communication costs of the players. Furthermore, by developing a Lyapunov function, it is shown that the states of the players asymptotically converge to the Nash equilibrium. Finally, a simulation is conducted to show the effectiveness of the proposed methods.

Multi-Kernel Neural Network Sliding Mode Control for Permanent Magnet Linear Synchronous Motors

In this paper, a multi-kernel neural network sliding mode control (MNNSMC) method is proposed to enhance the position tracking performance and disturbance suppression capability of permanent magnet linear synchronous motors (PMLSMs). The designed MNNSMC strategy consists of two parts, a sliding mode control with a dynamic boundary layer and a multi-kernel neural network (MNN). In the former, the dynamic boundary layer is utilized to guarantee that the sliding mode variable converges to the sliding manifold asymptotically. In the latter, disturbances are introduced into the design of kernel functions to further approximate and compensate for the internal and external disturbances, such as parameter variations, positioning force, friction, and un-modeled nonlinearities of PMLSMs. The stability of the proposed MNNSMC strategy is analyzed and proved based on Lyapunov Theorem. Experiments are conducted on a PMLSM servo drive system to validate the effectiveness of the presented MNNSMC method, and results show that the proposed scheme has significant improvement on the position tracking performance, disturbance suppression capability, and robustness of the PMLSM system compared with an existing method.

An iterative run-to-run learning model to derive continuous brachial pressure estimates from arterial and venous lines during dialysis treatment

Objective:Non-invasive continuous blood pressure monitoring is not yet part of routine practice in renal dialysis units but could be a valuable tool in the detection and prevention of significant variations in patient blood pressure during treatment. Feasibility studies have delivered an initial validation of a method which utilises pressure sensors in the extra-corporeal dialysis circuit, without any direct contact with the person receiving treatment. Our main objective is to further develop this novel methodology from its current early development status to a continuous-time brachial artery pressure estimator. Methods:During an in vivo patient feasibility study with concurrent measurement validation by Finapres Nova experimental physiological measurement device, real-time continuous dialysis line pressures, and intermittent occluding arm cuff pressure data were collected over the entire period of (typically 4-hour) dialysis treatments. There was found to be an underlying quasi-linear relationship between arterial line and brachial pressure measurements which supported the development of a mathematical function to describe the relationship between arterial dialysis line pressure and brachial artery BP. However, unmodelled non-linearities, dynamics and time-varying parameters present challenges to the development of an accurate BP estimation system. In this paper, we start to address the problem of physiological parameter time variance by novel application of an iterative learning run-to-run modelling methodology originally developed for process control engineering applications to a parameterised BP model. Results:The iterative run-to-run learning methodology was applied to the real-time data measured during an observational study in 9 patients, supporting subsequent development of an adaptive real-time BP estimator. Tracking of patient BP is analysed for all the subjects in our patient study, supported only by intermittent updates from BP cuff measurements. Conclusion:The methodology and associated technology is shown to be capable of tracking patient BP non-invasively via arterial line pressure measurement during complete 4-hour treatment sessions. A robust and tractable method is demonstrated, and future refinements to the approach are defined.

Data-based flatness prediction and optimization in tandem cold rolling

In cold rolling process, the flatness actuator efficiency is the basis of the flatness control system. The precision of flatness is determined by the setpoints of flatness actuators. In the presence of modeling uncertainties and unmodeled nonlinearities in rolling process, it is difficult to obtain efficiency factors and setpoints of flatness actuators accurately. Based on the production data, a method to obtain the flatness actuator efficiency by using partial least square (PLS) combined with orthogonal signal correction (OSC) was adopted. Compared with the experiential method and principal component analysis method, the OSC–PLS method shows superior performance in obtaining the flatness actuator efficiency factors at the last stand. Furthermore, kernel partial least square combined with artificial neural network (KPLS–ANN) was proposed to predict the flatness values and optimize the setpoints of flatness actuators. Compared with KPLS or ANN, KPLS–ANN shows the best predictive ability. The root mean square error, mean absolute error and mean absolute percentage error are 0.51 IU, 0.34 IU and 0.09, respectively. After the setpoints of flatness actuators are optimized, KPLS–ANN shows better optimization ability. The result in an average flatness standard deviation is 2.22 IU, while the unoptimized value is 4.10 IU.

Neural-Network-Based Fully Distributed Adaptive Consensus for a Class of Uncertain Multiagent Systems

In this article, we revisit the problem of distributed neuroadaptive consensus for uncertain multiagent systems (MASs) in the presence of unmodeled nonlinearities as well as unknown disturbances. Robust consensus controllers comprising a linear feedback term, a discontinuous feedback term, and a neural network approximation term are constructed, where in each term, the weight part is endowed with some dynamical changing law. The asymptotic convergence of the consensus errors is theoretically proved based on the graph theory, nonsmooth analysis, and Barbalat's lemma. Both leaderless consensus and leader-follower tracking problems are considered before the results are further extended to containment problem in the presence of multileaders. A dramatic feature of the proposed method, in comparison with related works, is the fully distributed fashion of the information, requiring neither the underlying Laplacian eigenvalues nor the input upper bounds of the leaders (if exist). Several numerical examples are presented to testify the theoretical results.

Synchronisation of uncertain chaotic systems via fuzzy-regulated adaptive optimal control approach

ABSTRACTThis study investigates the adaptive synchronisation of uncertain chaotic systems with unmodelled nonlinearities, dynamic mismatch, parametric perturbations, and external disturbances. A fuzzy-regulated adaptive optimal control (FRAOC) scheme is derived to realise chaotic synchronisation under both structure and parameter uncertainties. In the proposed scheme, the uncertain chaotic dynamics is firstly captured and estimated using a self-organising learning algorithm in an online fuzzy rule database. Based on the complicated and uncertain chaotic information, control strategy is adaptively regulated and configured in the form of weighting matrix for the subsequent optimal controller by the use of fuzzy logic inference. An adaptive optimal controller is then developed, so that its control behaviour and performance are adaptively adjusted for the chaotic synchronisation and compound uncertainty compensation. A supervisory compensator with recursive adaptation law is also designed to attenuate the residual compensation error and guarantee the synchronisation stability. Chaotic synchronisation convergence using the proposed approach can be mathematically ensured and speeded up with satisfactory robustness. Simulation results also demonstrate the effectiveness of the proposed control method in comparison with robust quadratic optimal control based on linear matrix inequality approach.

Machine learning of electro-hydraulic motor dynamics

In this paper we propose an innovative machine learning approach to the hydraulic motor load balancing problem involving intelligent optimisation and neural networks. Two different nonlinear artificial neural network approaches are investigated, and their accuracy is compared to that of a linearised analytical model. The first neural network approach uses a multi-layer perceptron to reproduce the load simulator dynamics. The multi-layer perceptron is trained using the Rprop algorithm. The second approach uses a hybrid scheme featuring an analytical model to represent the main system behaviour, and a multi-layer perceptron to reproduce unmodelled nonlinear terms. Four techniques are tested for the optimisation of the parameters of the analytical model: random search, an evolutionary algorithm, particle swarm optimisation, and the Bees Algorithm. Experimental tests on 4500 real data samples from an electro-hydraulic load simulator rig reveal that the accuracy of the hybrid and the neural network models is comparable, and significantly superior to the accuracy of the analytical model. The results of the optimisation procedures suggest also that the inferior performance of the analytical model is likely due to the non-negligible magnitude of the unmodelled nonlinearities, rather than suboptimal setting of the parameters. Despite its limitations, the analytical linear model performs comparably to the state-of-the-art in the literature, whilst the neural and hybrid approaches compare favourably.

Adaptive neural control of vehicle yaw stability with active front steering using an improved random projection neural network

ABSTRACT Active front steering (AFS) can enhance the vehicle yaw stability. However, the control of vehicle yaw rate is very challenging due to (1) the unmodelled nonlinearity and uncertainties in vehicle dynamics; (2) timely response in control scheme. These two issues can be simultaneously alleviated through a random projection neural network (RPNN) for its high model generalisation and fast computational speed. However, typical RPNN cannot be directly applied to adaptive control applications. Therefore, a new RPNN-based adaptive neural control method is proposed, which is equipped with a newly designed adaptation law based on the theorem of Lyapunov stability. To test the performance of the proposed control method, simulations were carried out using a validated vehicle model. The simulation results show that, compared to conventional backpropagation neural network (BPNN) based controller, the proposed RPNN-based adaptive controller can reduce the response time and attenuate oscillatory steering in the case of cornering manoeuvre under fast variant vehicle speed. The results also demonstrate that the proposed RPNN-based adaptive controller outperforms the state-of-the-art fuzzy logic controller and the error feedback controller in multiple aspects including tracking nominal vehicle yaw rate, desired sideslip angle and intended path, showing its significance in vehicle yaw stability control.

A Sampled-Data Approach to Nonlinear ESO-Based Active Disturbance Rejection Control for Pneumatic Muscle Actuator Systems with Actuator Saturations

In this paper, an active disturbance rejection control scheme with a nonlinear sampled-data extended state observer (NSESO) is proposed for a pneumatic muscle actuator (PMA) system. The phenomenon of the actuator saturation is taken into account in the design procedure. The NSESO is utilized to estimate the so-called total disturbance that reflects the aggregated impacts of unmodeled nonlinearities and disturbances. Then, an NSESO-based composite control strategy is designed, where the estimates provided by the designed NSESO serve as the compensation to eliminate the total disturbance. Finally, a number of practical experiments on the PMA systems are conducted to verify the validity and applicability of the proposed design method.

A Robust Resonant Controller for High-Speed Scanning of Nanopositioners: Design and Implementation

This brief presents a novel damping control scheme for piezoactuated nanopositioning platforms with robust resonant control (RRC). The RRC is developed to attenuate the resonant-vibrational modes of the lightly damped dynamics of the stage in an inner positive feedback loop. The parameters in the proposed RRC are determined through an analytical approach. Indeed, the controller gains constrained by both the robustness and the damping ratio of the inner loop are tuned based on the small gain theory. Then, a high gain integral tracking controller is applied in the outer loop to minimize the tracking errors due to unmodeled nonlinearity and uncertainties. To validate the effectiveness of the proposed RRC, comparative experiments with conventional positive position feedback (PPF) and integral resonant control (IRC) are conducted on a piezoactuated nanopositioning stage. Results demonstrate that the proposed RRC improves the closed-loop bandwidth from 67 Hz with PPF and 135 Hz with IRC to 176 Hz. Moreover, better robustness against load variations with a range of 0–1000-g loading under 0–20-Hz input raster scanning signals are obtained by RRC compared with PPF as well as IRC.

Dynamic Decoupling Based Robust Synchronous Control for a Hydraulic Parallel Manipulator

In this paper, a novel dynamic decoupling-based robust synchronous control scheme is proposed for the high-precision motion trajectory tracking control of a hydraulic parallel manipulator. The presented controller is derived by effectively integrating joint disturbance rejection technique with a cross-coupling control (CCC) approach. The parallel manipulator is first considered as a multi-axis system with modeling uncertainties and strong nonlinear coupling dynamics. For each axis, two extended state observers are synthesized to estimate and reject both the matched uncertainties and unmatched uncertainties (e.g., external disturbances and unmodeled nonlinearities), and then the decoupled dynamic model of the system in joint space can be established. A synchronization error is developed to represent the coordination degree of multiple actuators and then coupled with the position error to form a coupling position error. Subsequently, based on the system decoupled dynamic model, a synchronous controller with the feedback of coupling position error is carried out to coordinate the motions of actuators by combining the CCC strategy with robust control via backstepping method. The proposed controller can theoretically guarantee the simultaneous convergence of both position tracking errors and synchronization errors. The stability of the system is verified via the Lyapunov method. The comparative experiment results are obtained to verify the high tracking performance nature of the proposed control strategy.

In-move aligned SINS/GNSS system using recurrent wavelet neural network (RWNN)-based integration scheme

Advances in micro-electro mechanical system (MEMS) technology bring about revolutionary changes in autonomous vehicle navigation. As a new development, strap-down inertial navigation system (SINS) is effectively combined with global navigation satellite system (GNSS) to construct an integrated SINS/GNSS system. However, time-growing navigation error is the main challenge of using MEMS-grade inertial measurement unit (IMU) in the SINS/GNSS system. Failure of un-accounted inertial sensor error causes a rapid degradation in the overall performance of low-cost SINSs. This paper aims to enhance the long-term performance of low-cost MEMS-grade SINS/GNSS navigation system. A new integration scheme is presented for in-move aligned SINS/GNSS system. Un-modeled nonlinearities in the SINS dynamics as well as error uncertainties in the measurements of MEMS-grade IMU motivate using a robust data fusion algorithm for the proposed integration scheme. Considering these facts, a new recurrent wavelet neural network (RWNN)-based algorithm is designed for data fusion in the proposed in-move aligned SINS/GNSS system. Several vehicular field tests have been carried out to assess the long-term performance and accuracy of the proposed navigation algorithm.