Iterative Control Research Articles

Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

In this article, an iterative-learning-based robotic controller is developed, which aims at providing a prescribed assistance or resistance force to the human user. In the proposed controller, the characteristic parameter of the human upper limb movement is first learned by the robot using the measurable interaction force, a recursive least square (RLS)-based estimator, and the Adam optimization method. Then, the desired trajectory of the robot can be obtained, tracking which the robot can supply the human’s upper limb with a prescribed interaction force. Using this controller, the robot automatically adjusts its reference trajectory to embrace the differences between different human users with diverse degrees of upper limb movement characteristics. By designing a performance index in the form of interaction force integral, potential adverse effects caused by the time-related uncertainty during the learning process can be addressed. The experimental results demonstrate the effectiveness of the proposed method in supplying the prescribed interaction force to the human user. Note to Practitioners—This article concentrates on developing a novel control technique to make the robot supply a prescribed interaction force to the human user in the presence of time-related uncertainties. The proposed control method is applicable to various scenarios of the human–robot interaction, e.g., it can be used for rehabilitation robots to provide assistive or resistive force to stroke patients or for exoskeleton robots to provide assistive force to human users for completing heavy-load tasks. Moreover, the desired interaction force can be tailored for different human users according to their needs and different task objectives. Consequently, the proposed controller can serve diverse users and has a promising perspective in automation.

Data-Driven Feedforward Parameter Tuning Optimization Method Under Actuator Constraints

Feedforward control includes iterative force injection-based feedforward control and model-based feedforward control. Model-based feedforward control enables high performance for nonrepetitive motions in industrial motion systems. In particular, model-based feedforward control with a parameterized input shaping filter and a feedforward controller can effectively implement trajectory tracking tasks without relying on mathematical models of systems. However, the pre-existing model-based feedforward control does not consider the saturation boundary of the system actuator, which may lead to saturation of the feedforward force for actual systems with actuator constraints. Therefore, this article aims to develop a feedforward control method that obtains high tracking performance for nonrepetitive trajectory under actuator constraints. In particular, a parameterized input shaping and feedforward control method based on basis functions is pursued, which is combined with a data-driven iterative optimization approach to cope with nonrepetitive trajectory tracking tasks and actuator constraints. Simulations and experiments on a motion system confirm the optimal trajectory tracking performance under actuator constraints and robustness to nonrepetitive trajectory.

PAM: A Piecewise-Linearly-Approximated Floating-Point Multiplier With Unbiasedness and Configurability

Approximate computing is a promising alternative to improve energy efficiency for IoT devices on the edge. This work proposes a piecewise-linearly-approximated and unbiased floating-point approximate multiplier with run-time configurability. We provide a theoretically sound formulation that turns multiplication approximation to an optimization problem. With the formulation and findings, a multi-level architecture is proposed to easily incorporate run-time configurability and module execution parallelism. Finally, the proposed multiplier is further optimized to reduce the circuit implementation complexity, making the multiplier linearly dependent on the precision requirement, instead of quadratically or exponentially as in prior work. When compared to the prior state-of-the-art approximate floating-point multiplier, ApproxLP M. Imani <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> , “ApproxLP: Approximate multiplication with linearization and iterative error control,” in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Proc. ACM/IEEE Des. Autom. Conf.</i> , 2019, pp. 1–6., the proposed multiplier outperforms in all the aspects including accuracy, area, and delay. By replacing a full-precision floating-point multiplier in GPU, the proposed design can improve the energy efficiency for various edge computing tasks. Even with Level 1 approximation, the proposed multiplier improves energy efficiency up to 20× for machine learning on CIFAR-10, with almost negligible accuracy loss.

A two-stage robust iterative learning model predictive control for batch processes

Iterative learning model predictive control (ILMPC) has been considered as potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along batches and ensure system stability within batches. However, as a model-based control method, the control performance of the ILMPC algorithm deteriorates when exists model parameter uncertainty. Therefore, guaranteeing system tracking performance in the case of model parameter uncertainty is a challenging task in the framework designing of ILMPC method. To this end, we develop a two-stage robust ILMPC strategy for batch processes, which integrates the robust iterative learning control (ILC) in the domain of batch-axis and robust model predictive control (MPC) in the domain of time-axis into one comprehensive control scheme. The integrated control law of the developed two-stage robust ILMPC algorithm is obtained by solving two convex optimization problems. As a result, the developed control method obtains faster convergence speed and better tracking performance in the case of model parameter uncertainty. Moreover, the convergence analysis of the system is presented. Finally, comparative simulations are provided to verify the superiority of the developed control algorithm.

Iterative learning model predictive control for multivariable nonlinear batch processes based on dynamic fuzzy PLS model

This paper proposes a latent variable nonlinear iterative learning model predictive control method (LV-NILMPC) based on the dynamic fuzzy partial least squares (DFPLS) model to achieve trajectory tracking and process disturbance suppression in multivariable nonlinear batch processes. The dynamic and nonlinear characteristics of the physical system are constructed by integrating the T-S fuzzy model into the regression framework of the dynamic partial least squares (PLS) inner model. The decoupling and dimensionality reduction characteristics of the DFPLS model automatically decompose a multivariable nonlinear system into multiple univariate subsystems operating independently in the latent variable space. Based on the DFPLS model, we design LV-NILMPC controllers corresponding to each latent variable subspace to track the projection of the reference trajectories. Compared with the previous control method, the method proposed in this paper has a faster convergence rate and smaller tracking error. The method is suitable for nonlinear, multivariable and strong coupling batch processes. Finally, the application of two cases shows that the method is effective.

Recursive algorithms for identification of dual Youla-Kucera parametrized plant models in closed loop

The growing interest in using dual Youla Kucera plant parametrization for modeling plant uncertainties raises the need for recursive identification algorithms dedicated to the identification of these structures in closed loop in view of developing appropriate iterative tuning and adaptive control strategies. The paper presents recursive algorithms for identification in closed loop of dual Youla-Kucera parametrized plant models. These algorithms assure global asymptotic stability in the deterministic environment and allow to obtain unbiased parameter estimation in the presence of measurement noise when the plant model is in the model set. The paper also re-visit the Hansen scheme which allows to associate open loop type recursive identification algorithms for the identification of these structures in closed loop. When the plant model is not in the model set, comparison of the various algorithms is done in terms of the bias distribution. Further comparisons and performance evaluation is provided by simulations on some relevant examples and experimental identification in closed loop of a test bench for active noise control.

Iterative Learning Control for Actuator Fault Uncertain Systems

An iterative learning fault-tolerant control method is designed for an actuator fault intermittent process with simultaneous uncertainties for the system parameters. First, an intermittent fault tolerance controller is designed using 2D system theory, and the iterative learning control (ILC) intermittent process is transformed into a 2D Roesser model. Secondly, sufficient conditions for the controller’s existence are analyzed using the linear matrix inequality (LMI) technique, and the control gain matrices are obtained by convex optimization with LMI constraints. Under these conditions for all additive uncertainties for the system parameters and admissible failures, the controller can ensure closed-loop fault-tolerant performance in both the time and batch directions, and it can also meet the H∞ robust performance level against outside disturbances. Eventually, the algorithm’s computational complexity is analyzed, and the effectiveness of the algorithm is verified by simulation with respect to an injection molding machine model. Compared with traditional ILC laws, which do not consider actuator faults, the proposed algorithm has a better convergence speed and stability when the time-invariant and time-variant actuator faults occur during implementation.

Model-Free Adaptive Iterative Learning Bipartite Containment Control for Multi-Agent Systems.

This paper studies the bipartite containment tracking problem for a class of nonlinear multi-agent systems (MASs), where the interactions among agents can be both cooperative or antagonistic. Firstly, by the dynamic linearization method, we propose a novel model-free adaptive iterative learning control (MFAILC) to solve the bipartite containment problem of MASs. The designed controller only relies on the input and output data of the agent without requiring the model information of MASs. Secondly, we give the convergence condition that the containment error asymptotically converges to zero. The result shows that the output states of all followers will converge to the convex hull formed by the output states of leaders and the symmetric output states of leaders. Finally, the simulation verifies the effectiveness of the proposed method.

Terminal constrained robust hybrid iterative learning model predictive control for complex time-delayed batch processes

This work mainly addresses terminal constrained robust hybrid iterative learning model predictive control against time delay and uncertainties in a class of complex batch processes with input and output constraints. In this work, an equivalently novel extended two-dimensional switched system is first constructed to represent the process model by introducing state difference, output error and new relaxation variable information. Then, a hybrid predictive updating controller is proposed and an optimal performance index function including terminal constraints is designed. Under the condition that the switching signal meets certain conditions, the solvable problem of model predictive control is realized by Lyapunov stability theory. Meanwhile, the design scheme of controller parameters is also given. In addition, the robust constraint set is adopted to overcome the disadvantage that the traditional asymptotic stability cannot converge to the origin when it involves disturbances, such that the system state converges to the constraint set and meets its expected value. Finally, the effectiveness of the proposed algorithm is verified by controlling the speed and pressure parameters of the injection molding process.

Impedance Iterative Learning Backstepping Control for Output-Constrained Multisection Continuum Arms Based on PMA

Background: Pneumatic muscle actuator (PMA) actuated multisection continuum arms are widely applied in various fields with high flexibility and bionic properties. Nonetheless, their kinematic modeling and control strategy proves to be extremely challenging tasks. Methods: The relationship expression between the deformation parameters and the length of PMA with the geometric method is obtained under the assumption of piecewise constant curvature. Then, the kinematic model is established based on the improved D-H method. Considering the limitation of PMA telescopic length, an impedance iterative learning backstepping control strategy is investigated. For one thing, the impedance control is utilized to ensure that the ideal static balance force is maintained constant in the Cartesian space. For another, the iterative learning backstepping control is applied to guarantee that the desired trajectory of each PMA can be accurately tracked with the output-constrained requirement. Moreover, iterative learning control (ILC) is implemented to dynamically estimate the unknown model parameters and the precondition of zero initial error in ILC is released by the trajectory reconstruction. To further ensure the constraint requirement of the PMA tracking error, a log-type barrier Lyapunov function is employed in the backstepping control, whose convergence is demonstrated by the composite energy function. Results: The tracking error of PMA converges to 0.004 m and does not exceed the time-varying constraint function through cosimulation. Conclusion: From the cosimulation results, the superiority and validity of the proposed theory are verified.

Hybrid Encryption Algorithm for Sensitive Information of College Physical Fitness in Cloud Storage Environment

In order to improve the security of college sports fitness sensitive information, this paper proposes a hybrid encryption algorithm for college sports fitness sensitive information in cloud storage environment. Build an analysis model of influencing factors of cloud storage environment to identify the risk value of sensitive information; Using Bloom filter data structure to eliminate redundant data of sensitive information; The transmission channel model of sensitive information and the security coding model of sensitive information are constructed. Combined with the fuzzy differential information fusion method, the complete key of sensitive information under the symmetric encryption protocol is obtained to realize the key optimization design. Through AES encryption and decryption algorithm, the anti encryption control and structural reorganization of college sports fitness sensitive information, and the iterative convergence control of hybrid encryption, so as to realize the hybrid encryption of college sports fitness sensitive information. The encryption time of the design algorithm under different attribute numbers is always kept below 0.2S, the maximum encryption time under different number of software packages is only 0.5 s, and the encryption accuracy can reach 1, which proves that the design algorithm has certain application value.

Robust Backstepping Control Applied to UAVs for Pest Recognition in Maize Crops

In this paper, a robust control technique is developed to achieve the quadrotor stabilization against unmodeled matching vanishing dynamics. The synthesis of the proposed robust control is based on the Lyapunov approach and the backstepping method allowing to construct an iterative control algorithm. To compare the performance of the proposed controller, a Proportional Derivative (PD) controller is used to obtain experimental results in an outdoor environment. To compare the closed-loop system responses with both controllers, the Integral Absolute Error is computed and several tests are conducted to calculate the error standard deviation. Ultimately, employing the robust backstepping control approach in pest recognition in maize crops, which is a specific task of precision agriculture, demonstrates its effectiveness in improving the trajectory tracking of the vehicle while it captures images of the crops.

Online Value Iteration for Intelligent Discounted Tracking Design of Constrained Systems

In this brief, in order to solve the discounted optimal tracking control problem for discrete-time systems with control constraints, an advanced online value iteration (VI) algorithm is developed. First, we revisit discounted general value iteration (GVI) for optimal tracking control design. Second, the stability condition of GVI is established, which can be applied to evaluate the current iterative tracking control policy. Third, based on the concept of attraction domain, the evolving tracking control policies generated by online VI can be obtained through judging the location of the current tracking error. Finally, we prove system stability based on online VI for the present tracking control problem. A simulation example is shown to illustrate the effectiveness of the developed algorithm.

Energy Efficiency Maximization in Large-Scale Cell-Free Massive MIMO: A Projected Gradient Approach

This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions for this optimization problem are based on solving a sequence of second-order cone programs (SOCPs), whose computational complexity scales dramatically with the network size. Therefore, they are not implementable for practical large-scale cell-free massive MIMO systems. To tackle this issue, we propose an iterative power control algorithm based on the frame work of an accelerated projected gradient (APG) method. In particular, each iteration of the proposed method is done by simple closed-form expressions, where a penalty method is applied to bring constraints into the objective in the form of penalty functions. Finally, the convergence of the proposed algorithm is analytically proved and numerically compared to the known solution based on SOCP. Simulations results demonstrate that our proposed power control algorithm can achieve the same EE as the existing SOCPs-based method, but more importantly, its run time is much lower (one to two orders of magnitude reduction in run time, compared to the SOCPs-based approaches).

Neural network state observer-based robust adaptive iterative learning output feedback control for the rigid-flexible coupled robotic systems with unknown delays and backlash-like hysteresis

Neural network state observer-based robust adaptive iterative learning output feedback control for the rigid-flexible coupled robotic systems with unknown delays and backlash-like hysteresis

Impact of Divergence in BBO on Efficient Energy Strategy of Demand Side Management

Demand Response (DR) keeps up an important character in the growth of electricity generation with transmission and distribution in the phase of advanced grid automation. For future smart grid, basically two types of Demand Side Management strategies have been used with a number of shiftable or controllable devices. In this research article, two new variants of Biogeography Based Optimization (BBO) algorithm named as Iteration Control BBO and Pandemic BBO are proposed for solving the minimization problem and definitions of these variants have been made by change in migration control $$\overrightarrow{a}$$ vector. These two variants are indirectly support the principle of No Free Lunch theorem. The beauty of these variants is that they are reported the impact of divergence in BBO on efficient energy strategy of DSM. The simulation results reported in this paper claims that the used DSM techniques acquire financial savings, while lowering and shifting peak load.

Distributed iterative learning temperature control for large‐scale buildings

AbstractThis article investigates the distributed iterative learning control for heating ventilation and air condition (HVAC) systems. Large‐scale building HVAC systems consisting of several subsystems are generally disturbed by the external environment and random human activities. The control objective is to achieve all units of the large‐scale building consistently maintaining the desired temperature. A distributed learning control (DLC) scheme with a decreasing gain is proposed and analyzed for the first time. To accelerate convergence speed, an adaptation mechanism is introduced to generate an adaptive learning gain sequence. Then, an accelerated distributed learning control (ADLC) scheme is designed to improve the transient convergence performance. For the DLC scheme, the input error is proved convergent to zero in the mean‐square sense, whereas the convergence with probability 1 is proved for the ADLC scheme. Numerical results are presented to verify the effectiveness of the proposed schemes.

Quasi-linear toroidal simulations of resonant magnetic perturbations in eight ITER H-mode scenarios

Both linear and quasi-linear aspects of the plasma response to the resonant magnetic perturbation (RMP) field are numerically investigated for various H-mode scenarios in ITER, covering the pre-fusion power operation and the fusion power operation phases. Linear response computations for eight ITER scenarios, with varying plasma current and toroidal magnetic field, reveal that the best coil current phasing for controlling the type-I edge localized modes (ELMs) scales roughly linearly with the edge safety factor. The coil phasing is defined as the relative toroidal phase of the coil currents between different rows, for a given toroidal harmonic. Quasi-linear initial value simulation, which is the focus of the present study, shows that application of the n = 3 (n is the toroidal mode number) RMP field has a minimum side effect on the plasma core momentum confinement but potentially a large effect on the global particle transport. Generally, the RMP field with the best (worst) coil phasing for ELM control produces the strongest (weakest) effect on the plasma edge flow and the overall density. This robustly holds for all eight ITER scenarios. Consequently, in order to minimize the RMP induced side effects while achieving ELM control (suppression) in ITER, a compromise is necessary in choosing the coil current configuration.

Constrained Decoupling Adaptive Dynamic Programming for A Partially Uncontrollable Time-Delayed Model of Energy Systems

The lack of feedback-loop design and uncontrollable state reconstruction, such as load demand and renewable generation, are key factors to hinder the development of energy control. Adaptive dynamic programming is an efficient method to break through the bottleneck. However, most current works assume that the evolution of uncontrollable state satisfies Markovian property. This paper proposes a time-delayed adaptive dynamic programming framework for optimal energy control, utilizing more comprehensive historical data for state reconstruction. The uncontrollable state is reconstructed by an attention mechanism-based Transformer network based on online measured data. Then, a self-learning iterative algorithm is developed to obtain the optimal state feedback control policy under a discounted performance index function. The problem of solving iterative control policy, which is a mixed-integer nonlinear programming problem with an enormous computational burden, is decomposed into three sub-problems, i.e. nonlinear programming with no discrete variable, which is solved by the Projected Newton method, mixed-integer quadratic programming, and quadratic programming. Numerical results illustrate that the developed control algorithm minimizes the electricity cost in the long term and avoids peak load.

A Hybrid 3D-2D Image Registration Framework for Pedicle Screw Trajectory Registration between Intraoperative X-ray Image and Preoperative CT Image.

Pedicle screw insertion is considered a complex surgery among Orthopaedics surgeons. Exclusively to prevent postoperative complications associated with pedicle screw insertion, various types of image intensity registration-based navigation systems have been developed. These systems are computation-intensive, have a small capture range and have local maxima issues. On the other hand, deep learning-based techniques lack registration generalizability and have data dependency. To overcome these limitations, a patient-specific hybrid 3D-2D registration principled framework was designed to map a pedicle screw trajectory between intraoperative X-ray image and preoperative CT image. An anatomical landmark-based 3D-2D Iterative Control Point (ICP) registration was performed to register a pedicular marker pose between the X-ray images and axial preoperative CT images. The registration framework was clinically validated by generating projection images possessing an optimal match with intraoperative X-ray images at the corresponding control point registration. The effectiveness of the registered trajectory was evaluated in terms of displacement and directional errors after reprojecting its position on 2D radiographic planes. The mean Euclidean distances for the Head and Tail end of the reprojected trajectory from the actual trajectory in the AP and lateral planes were shown to be 0.6–0.8 mm and 0.5–1.6 mm, respectively. Similarly, the corresponding mean directional errors were found to be and . The mean trajectory length difference between the actual and registered trajectory was shown to be 2.67 mm. The approximate time required in the intraoperative environment to axially map the marker position for a single vertebra was found to be 3 min. Utilizing the markerless registration techniques, the designed framework functions like a screw navigation tool, and assures the quality of surgery being performed by limiting the need of postoperative CT.