Iterative learning control is a control design method for high performance tracking applications. In this paper a novel mechanism for accelerating the convergence of a well-known Norm Optimal Iterative Learning Control (NOILC) Algorithm is presented by modifying the reference signal each iteration using the previously measured tracking error. The change is equivalent to successive application of a gradient and NOILC iteration and can be interpreted as an augmentation of the 'feedback plus feedforward' structure of NOILC by adding further 'feedforward' data from the last iteration. The change is interpreted in terms of the spectrum of the error update operator and the annihilation of spectral components of the error signal. Convergence of the proposed algorithm is analysed rigorously and numerical examples are given to demonstrate the effectiveness of the proposed method.

The conventional tracking control of mechanical systems is generally based on the stabilization of error dynamics and most of the results can only guarantee asymptotic tracking of the desired trajectories. Since error dynamics are generally time-varying, it is very difficult to find appropriate Lyapunov functions or control Lyapunov functions to complete stability analysis and controller design. To address this limitation, the novel constructive exponential tracking control method is proposed to mechanical systems by utilizing the Hamiltonian realization and contraction analysis in this paper. Firstly, based on the Hamiltonian realization and use the structural characteristics of port-Hamiltonian systems, the exponential tracking controllers are constructed for fully actuated and under-actuated mechanical systems by combining the pre-feedback with feedback control. The proposed tracking control strategies can be used to discuss fully actuated and under-actuated mechanical systems in a unified framework. Then the exponential decay-rate of tracking controllers and procedure for selecting control parameters for fully actuated and under-actuated mechanical systems are given. Finally, comparative simulations and experiments are carried out to illustrate the effectiveness and robustness of the proposed control strategy.

Tremor is an involuntary and repetitive swinging movement of limb, which can be regarded as a periodic disturbance in tremor suppression system based on functional electrical stimulation (FES). Therefore, using repetitive controller to adjust the level and timing of FES applied to the corresponding muscles, so as to generate the muscle torque opposite to the tremor motion, is a feasible means of tremor suppression. At present, most repetitive control systems based on FES assume that tremor is a fixed single frequency signal, but in fact, tremor may be a multi-frequency signal and the tremor frequency also varies with time. In this paper, the tremor data of intention tremor patients are analyzed from the perspective of frequency, and an adaptive repetitive controller with internal model switching is proposed to suppress tremor signals with different frequencies. Simulation and experimental results show that the proposed adaptive repetitive controller based on parallel multiple internal models and series high-order internal model switching can suppress tremor by up to 84.98% on average, which is a significant improvement compared to the traditional single internal model repetitive controller and filter based feedback controller. Therefore, the adaptive repetitive control method based on FES proposed in this paper can effectively address the issue of wrist intention tremor in patients, and can offer valuable technical support for the rehabilitation of patients with subsequent motor dysfunction.

Iterative learning control (ILC) applies to systems required to track the desired trajectory of finite duration repeatedly. This article considers constrained ILC design for linear time-varying systems, a problem with limited, in relative terms, results in the literature but not uncommon in practical applications. Different design algorithms are developed, and their convergence properties are established. An extension of these designs to point-to-point tracking tasks is given. A high-speed rack feeder typically used in automated warehouses is considered to verify the designs. It represents a flexible beam structure subject to kinematic constraints, such as a maximum velocity and a maximum acceleration with a vertically moving mass causing the time-varying characteristics. Experimental results demonstrate the effectiveness of the designs.

SummaryThe finite‐time stabilization and H∞ control of stochastic nonlinear systems was investigated in this article. First, we discussed the relationship between the finite‐time stability and the dissipation property by transforming the stochastic nonlinear system to its equivalent Hamiltonian formulation. It was showed that the stochastic dissipative Hamiltonian system is finite‐time stable in probability if the system is strictly dissipative and the Hamiltonian function possessed some special forms. Then, by the energy shaping and damping injection technique, we reformulate the internal structure matrix and the Hamiltonian function to construct a finite‐time stabilization controller for stochastic nonlinear systems. Moreover, for uncertain stochastic nonlinear systems, an H∞ finite‐time robust controller was put forward by utilizing the Hamiltonian function to construct a solution for the Hamiltonian‐Jacobi Inequality. Finally, we proposed a finite‐time stabilization and a finite‐time H∞ controller for the inverted cart‐pendulum system to verify the effectiveness of the proposed method.

Iterative learning control (ILC) is a high performance control design method for systems working in a repetitive manner by learning from previous experience. Most existing ILC design considers the problem where the desired reference trajectory is defined either fully on the trial duration or on a finite number of intermediate time instants. This paper further expands the applicability of ILC by studying a more general case (named region to region tracking) where there is no desired trajectory defined at all; instead only a region where the system output should reach is given. To solve this problem, a novel ILC algorithm with an norm optimal ILC step and a projection operation is developed. Convergence properties of the algorithm are analysed rigorously. It is also shown that traditional reference tracking problems can be solved as special cases of the proposed design, resulting in a well-known norm optimal ILC algorithm being recovered and a new point to point ILC algorithm. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach.

Tremor is a very common motor disorder, mainly manifested as involuntary, periodic and rhythmic movement in any part of the body, especially in hands and upper-limbs, which seriously affects the life quality of patients. Functional electrical stimulation (FES) has been shown a promising technique to suppress tremor. Most existing FES based design methods assume tremor is a single frequency signal which however is a highly idealized simplification of the real case which contains multiple-frequency or even a frequency band, therefore limiting their practical performance. To address this problem, this paper proposes a controller design method based on multi-periodic repetitive control that is capable of suppressing tremor signal with multiple frequencies. Simulation and experimental results verify the effectiveness of the proposed method.

Tremor is a rhythmic, alternating swing motion caused by involuntary repetition of muscle contraction and relaxation. Although it does not endanger life, it will make the work and daily life of patients difficult. Functional electrical stimulation (FES) has been shown as a promising technique for tremor suppression. Wrist motion is produced by a group of muscles in a collective and coordinate way. However, existing FES-based design methods mostly aim at one pair of muscles associated with the wrist motion, thus limiting the performance of tremor suppression. Furthermore, the possible high level of stimulation required for a single muscle pair can also accelerate muscle fatigue of the patients. To address these problems, this paper uses multiple muscles FES to suppress tremor by fully considering the properties of wrist motion. This paper develops a wrist musculoskeletal model with Hammerstein structure, identifies its parameters, and proposes repetitive controllers based on frequency modified inverse algorithm to suppress tremor. Experimental results are presented to demonstrate its advantages over single muscle stimulation based tremor suppression.

SummaryThe Special Issue presents results of current research on learning‐based adaptive methods, merging together model‐based and data‐driven adaptive approaches. The special issue contains two main types of contributions. The first type of papers presents new theoretical developments for learning‐based adaptive algorithms, while the second type focuses on challenging practical applications ranging from UAVs, and autonomous vehicles, to heating and ventilation systems. These papers are compiled in a special issue of the journal. To access all of the papers please follow the following link (https://onlinelibrary.wiley.com/toc/10991115/2019/33/2).

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

This paper proposes a novel point-to-point iterative learning control (ILC) algorithm for high performance trajectory tracking applications. Based on a successive project formulation of the point-to-point ILC design problem, two point-to-point ILC design algorithms are derived: one algorithm reCovers the norm optimal point to point ILC algorithm with a desirable physical property of converging to the minimum norm (energy) solution, and the other one (interestingly) accelerates convergence speed which could lead to significant reduction in system configuration time/cost. Numerical results are provided to demonstrate the proposed algorithms' effectiveness.

Iterative learning control (ILC) is a control design method for high-performance trajectory tracking. Most existing results achieve this by learning from information collected over the past executions of the task (named trials). This brief proposes a novel ILC design framework that updates the control input by learning not only from the past trials but also from the predicted future trials using knowledge of the plant model. It is shown that by including information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance can be achieved. Analysis of the algorithm's properties reveals potentially substantial benefit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Both numerical simulations and experimental results using a nonminimum phase test facility are provided to demonstrate the effectiveness of the proposed method.

Norm optimal iterative learning control (NOILC) has recently been applied to iterative learning control (ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC's well-defined convergence properties. Design guidelines and supporting experimental results are included.