Extended state observer‑based pressure control for pneumatic actuator servo systems

A pneumatic actuator is a fast and economical tool that converts compressed air into mechanical motion. In this paper, an extended state observer (ESO)-based sliding mode controller (SMC) is developed to adjust the air pressure of the actuator for accurate position control. Specifcally, an impedance control module is established to produce desired air pressure based on the relationship between forces and desired positions. Then, the ESO-based SMC is implemented to adjust the air pressure to the required level despite the presence of system uncertainties and disturbances. As a result, the position of the actuator is controlled to a setpoint through the regulation of pressure. The performance of ESO-based SMC is compared with that of a classic active disturbance rejection controller (ADRC) and a SMC. Simulation results demonstrate that the ESO-based SMC shows comparable performance to ADRC in terms of precise pressure control. In addition, it requires the least control efort necessary to excite valves among the three controllers. The stability of ESO-based SMC is theoretically justifed through Lyapunov approach.     

Adaptive output consensus for heterogeneous nonlinear multi‑agent systems with multi‑type input constraints under switching‑directed topologies

This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies, in which all followers are subjected to multi-type input constraints such as unknown asymmetric saturation, unknown dead-zone and their integration. A unified representation is presented to overcome the difficulties originating from multi-agent input constraints. Moreover, the uncertain system functions in a non-lower triangular form and the interaction terms among agents are dealt with by exploiting the fuzzy logic systems and their special property. Furthermore, by introducing a nonlinear filter to alleviate the problem of “explosion of complexity” during the backstepping design, a distributed common adaptive control protocol is proposed to ensure that the synchronization errors converge to a small neighborhood of the origin despite the existence of multiple input constraints and arbitrary switching communication topologies. Both stability analysis and simulation results are conducted to show the effectiveness and performance of the proposed control methodology.     

Precision attitude regulation of uncertain flexible spacecraft

Consider the precision attitude regulation with vibration suppression for an uncertain and disturbed flexible spacecraft. The disturbance at issue is typically any finite superposition of sinusoidal signals with unknown frequencies and step signals of unknown amplitudes. First we show that the conventional mathematical model for flexible spacecrafts is transformable to a multi-input multi-output (MIMO) strict-feedback nonlinear normal form. Particularly it is strongly minimum-phase and has a well-defined uniform vector relative degree. Then it enables us to develop an adaptive internal model-based controller in the framework of adaptive output regulation to solve the problem. It is proved that asymptotic stability can be guaranteed for the attitude regulation task and the vibration of flexible appendages vanishes asymptotically. Hence, the present study explores a new idea for control of flexible spacecraft in virtue of its system structures.     

A neuro‑observer‑based optimal control for nonaffine nonlinear systems with control input saturations

In this study, an adaptive neuro-observer-based optimal control (ANOPC) policy is introduced for unknown nonaffine nonlinear systems with control input constraints. Hamilton–Jacobi–Bellman (HJB) framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input. ANOPC consists of both analytical and algebraic parts. In the analytical part, first, an observer-based neural network (NN) approximates uncertain system dynamics, and then another NN structure solves the HJB equation. In the algebraic part, the optimal control input that does not exceed the saturation bounds is generated. The weights of two NNs associated with observer and controller are simultaneously updated in an online manner. The ultimately uniformly boundedness (UUB) of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method. Finally, two numerical examples are provided to confirm the effectiveness of the proposed control strategy.     

Constrained nonlinear MPC for accelerated tracking piece-wise references and its applications to thermal systems

In this paper, we propose a model predictive control (MPC) strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints. Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation. Then, recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions. Moreover, the local optimality of the tracking MPC is achieved for unreachable output reference signals. By comparing to traditional tracking MPC, the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.     

On a novel tracking differentiator design based on iterative learning in a moving window

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative (PID). The practical challenge, however, is to extract such signals from noisy measurements and this difficulty is addressed first by J. Han in the form of linear and nonlinear tracking differentiator (TD). While improvements were made, TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the differentiation. The two approaches proposed in this paper start with the basic linear TD, but apply iterative learning mechanism to the historical data in a moving window (MW), to form two new iterative learning tracking differentiators (IL-TD): one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits; the other a serial IL-TD which is implementable digitally on any computer platform. Both algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.     

Unknown system dynamics estimator-based impedance control for lower limb exoskeleton with enhanced performance

In this article, an unknown system dynamics estimator-based impedance control method is proposed for the lower limb exoskeleton to stimulate the tracking flexibility with the terminal target position when suffering parametric inaccuracies and unexpected disturbances. To reinforce the robust performance, via constructing the filtering operation-based dynamic relation, i.e., invariant manifold, the unknown system dynamics estimators are employed to maintain the accurate perturbation identifi- cation in both the hip and knee subsystem. Besides, a funnel control technique is designed to govern the convergence process within a minor overshoot and a higher steady-state precision. Meanwhile, an interactive complaint result can be obtained with the aid of the impedance control, where the prescribed terminal trajectory can be adjusted into the interaction variable-based target position by the force–position mapping, revealing the dynamic influence between the impedance coefficient (stiffness and damping) and the adjusted position magnitude. A sufficient stability analysis verifies the ultimately uniformly bounded results of all the error signals, and even the angle errors can be regulated within the predefined funnel boundary in the whole convergence. Finally, some simulations are provided to demonstrate the validity and superiority including the enhanced interaction flexibility and robustness.     

Asymptotic stability of port‑hamiltonian systems with constant inputs

In this paper, the asymptotic stability of Port-Hamiltonian (PH) systems with constant inputs is studied. Constant inputs are useful for stabilizing systems at their nonzero equilibria and can be realized by step signals. To achieve this goal, two methods based on integral action and comparison principle are presented in this paper. These methods change the convex Hamiltonian function and the restricted damping matrix of the previous results into a Hamiltonian function with a local minimum and a positive semidefinite matrix, respectively. Due to common conditions of Hamiltonian function and damping matrix, the proposed method asymptotically stabilizes more classes of PH systems with constant inputs than the existing methods. Finally, the validity and advantages of the presented methods are shown in an example.     

Data‑driven disturbance observer‑based control: an active disturbance rejection approach

In this paper, a data-driven method for disturbance estimation and rejection is presented. The proposed approach is divided into two stages: an inner stabilization loop, to set the desired reference model, together with an outer loop for disturbance estimation and compensation. Inspired by the active disturbance rejection control framework, the exogenous and endogenous disturbances are lumped into a total disturbance signal. This signal is estimated using an on-line algorithm based on a datadriven predictor scheme, whose parameters are chosen to satisfy high robustness-performance criteria. The above process is presented as a novel enhancement to design a disturbance observer, which constitutes the main contribution of the paper. In addition, the control strategy is completely presented in discrete time, avoiding the use of discretization methods for its digital implementation. As a case study, the voltage control of a DC-DC synchronous buck converter afected by disturbances in the input voltage and the load is considered. Finally, experimental results that validate the proposed strategy and some comparisons with the classical disturbance observer-based control are presented.     

Cursor caging: enhancing focus targeting in interactive fisheye views

Fisheye view is an effective approach to visualizing and navigating large data sets by offering both local details and global context in the same view.However,by using different magnification factors for detail and context information,fisheye view also leads to new usability issues,one of which is the focus targeting difficulty.This challenge happens when a user tries to select a target to either shift the focus to a new place or chooses the target.Because of the magnification factors applied to the fisheye view,the target moves when the cursor moves,and consequently,the moving distance the cursor actually needs to travel to reach the target does not match the distance between the cursor and target shown on the screen.Task accuracy and efficiency can be affected.This paper analyzes the mechanism of this difficulty and proposes a new technique,cursor caging,as a method to alleviate the focus targeting difficulty in interactive fisheye views.This technique extends the fisheye magnification approach from one element to a focal region,which can contain several elements,and allows the mouse cursor to move freely inside this region without affecting the magnification of objects inside the focal region.In addition to the mathematical representation of this technique,we also develop two designs that incorporate the cursor caging concept in fisheye views,and describe a usability study on the technique.Our results showed that cursor caging can significantly improve the task completion time and reduce the error rate in focusing targeting tasks.     

Optimal energy consuming planning for a home‑based microgrid with mobility constraint of electric vehicles and tractors

This research deals with the energy management problem to minimize the cost of non-renewable energy for a small-scale microgrid with electric vehicles (EV) and electric tractors (ET). The EVs and ETs function as batteries in the power system, while they often have to leave it for their mobility and agricultural work. Each State of Charge (SoC), which is the charge rate of the battery from 0 to 1, and the operating time of ETs are optimized under the assumption that the required electrical energy, the arrival and departure time of EVs, and the working time of ETs are given by users, but they include uncertainties. In this paper, we deal with these uncertainties by constraints for robust energy planning and expected optimization based on scenarios, and show that the scheduling of the SoC assuming the worst case and the optimal home-based power consumption planning that considers the cost of each scenario corresponding to each variation can be obtained. Our proposed method is formulated as a mixed integer linear programming (MILP), and numerical simulations show that the optimal cooperative operation among multiple houses can be obtained and its global optimal or sub-optimal solution can be quickly obtained by using CPLEX.     

Output digitization of simple measure‑preserving linear systems

We examine three simple linear systems from the viewpoint of ergodic theory. We digitize the output and record only the sign of the output at integer times. We show that even with this minimal output we can recover important information about the systems. In particular, for a two-dimensional system viewed as a flow on the circle, we can determine the rate of rotation. We then use these results to determine the slope of the trajectories for constant irrational flow on the two-dimensional torus. To achieve this, we randomize the system by partitioning the state space and only recording which partition the state is in at each integer time. We show directly that these systems have entropy zero. Finally, we examine two four-dimensional systems and reduce them to the study of linear flows on the two-dimensional torus.     

Communication Between Speech Production and Perception Within the Brain——-Observation and Simulation

Realization of an intelligent human-machine interface requires us to investigate human mechanisms and learn from them. This study focuses on communication between speech production and perception within human brain and realizing it in an artificial system. A physiological research study based on electromyographic signals （Honda, 1996） suggested that speech communication in human brain might be based on a topological mapping between speech production and perception, according to an analogous topology between motor and sensory representations. Following this hypothesis, this study first investigated the topologies of the vowel system across the motor, kinematic, and acoustic spaces by means of a model simulation, and then examined the linkage between vowel production and perception in terms of a transformed auditory feedback （TAF） experiment. The model simulation indicated that there exists an invariant mapping from muscle activations （motor space） to articulations （kinematic space） via a coordinate consisting of force-dependent equilibrium positions, and the mapping from the motor space to kinematic space is unique. The motor-kinematic-acoustic deduction in the model simulation showed that the topologies were compatible from one space to another. In the TAF experiment, vowel production exhibited a compensatory response for a perturbation in the feedback sound. This implied that vowel production is controlled in reference to perception monitoring.     

Quantum‑enhanced reinforcement learning for control: a preliminary study

Reinforcement learning is one of the fastest growing areas in machine learning, and has obtained great achievements in biomedicine, Internet of Things (IoT), logistics, robotic control, etc. However, there are still many challenges for engineering applications, such as how to speed up the learning process, how to balance the trade-off between exploration and exploitation. Quantum technology, which can solve complex problems faster than classical methods, especially in supercomputers, provides us a new paradigm to overcome these challenges in reinforcement learning. In this paper, a quantum-enhanced reinforcement learning is pictured for optimal control. In this algorithm, the states and actions of reinforcement learning are quantized by quantum technology. And then, a probability amplification method, which can effectively avoid the trade-off between exploration and exploitation via quantized technology, is presented. Finally, the optimal control policy is learnt during the process of reinforcement learning. The performance of this quantized algorithm is demonstrated in both MountainCar reinforcement learning environment and CartPole reinforcement learning environment—one kind of classical control reinforcement learning environment in the OpenAI Gym. The preliminary study results validate that, compared with Q-learning, this quantized reinforcement learning method has better control performance without considering the trade-off between exploration and exploitation. The learning performance of this new algorithm is stable with different learning rates from 0.01 to 0.10, which means it is promising to be employed in unknown dynamics systems.     

Img FS: a transparent cryptography for stored images using a filesystem in userspace

Real-time encryption and decryption of digital images stored on end-user devices is a challenging task due to the inherent features of the images. Traditional software encryption applications generally suffered from the expense of user convenience, performance efficiency, and the level of security provided. To overcome these limitations, the concept of transparent encryption has been proposed. This type of encryption mechanism can be implemented most efficiently with kernel file systems. However, this approach has some disadvantages since developing a new file system and attaching it in the kernel level requires a deep understanding of the kernel internal data structure. A filesystem in userspace(FUSE) can be used to bridge the gap. Nevertheless, current implementations of cryptographic FUSE-based file systems suffered from several weaknesses that make them less than ideal for deployment. This paper describes the design and implementation of Img FS, a fully transparent cryptographic file system that resides on user space. Img FS can provide a sophisticated way to access, manage, and monitor all encryption and key management operations for image files stored on the local disk without any interaction from the user. The development of Img FS has managed to solve weaknesses that have been identified on cryptographic FUSE-based implementations. Experiments were carried out to measure the performance of Img FS over image files’ read and write against the cryptographic service, and the results indicated that while Img FS has managed to provide higher level of security and transparency, its performance was competitive with other established cryptographic FUSE-based schemes of high performance.     

Dynamic output feedback stabilization of deterministic finite automata via the semi‑tensor product of matrices approach

This paper deals with the dynamic output feedback stabilization problem of deterministic finite automata (DFA). The static form of this problem is defined and solved in previous studies via a set of equivalent conditions. In this paper, the dynamic output feedback (DOF) stabilization of DFAs is defined in which the controller is supposed to be another DFA. The DFA controller will be designed to stabilize the equilibrium point of the main DFA through a set of proposed equivalent conditions. It has been proven that the design problem of DOF stabilization is more feasible than the static output feedback (SOF) stabilization. Three simulation examples are provided to illustrate the results of this paper in more details. The first example considers an instance DFA and develops SOF and DOF controllers for it. The example explains the concepts of the DOF controller and how it will be implemented in the closed-loop DFA. In the second example, a special DFA is provided in which the DOF stabilization is feasible, whereas the SOF stabilization is not. The final example compares the feasibility performance of the SOF and DOF stabilizations through applying them to one hundred random-generated DFAs. The results reveal the superiority of the DOF stabilization.     

A solution strategy for distributed uncertain economic dispatch problems via scenario theory

In this paper, an uncertain economic dispatch problem (EDP) is considered for a group of coopertive agents. First, let each agent extract a set of samples (scenarios) from the uncertain set, and then a scenario EDP is obtained using these scenarios. Based on the scenario theory, a prior certification is provided to evaluate the probabilistic feasibility of the scenario solution for uncertain EDP. To facilitate the computational task, a distributed solution strategy is proposed by the alternating direction method of multipliers (ADMM) and a finite-time consensus strategy. Moreover, the distributed strategy can solve the scenario problem over a weight-balanced directed graph. Finally, the proposed solution strategy is applied to an EDP for a power system involving wind power plants.     

Robust adaptive control for mobile manipulators

This paper addresses the trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm adopts a robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. A kinematic controller is first designed to make the robot follow a desired end-effector and platform trajectories in task space coordinates simultaneously. Then, an adaptive control scheme is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. The system stability and the convergence of tracking errors to zero are rigorously proven using Lyapunov theory. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive control law in comparison with a sliding mode controller.     

Fast adaptive regression-basedmodel predictive control

Model predictive control (MPC) is an optimal control method that predicts the future states of the system being controlled and estimates the optimal control inputs that drive the predicted states to the required reference. The computations of the MPC are performed at pre-determined sample instances over a finite time horizon. The number of sample instances and the horizon length determine the performance of the MPC and its computational cost. A long horizon with a large sample count allows the MPC to better estimate the inputs when the states have rapid changes over time, which results in better performance but at the expense of high computational cost. However, this long horizon is not always necessary, especially for slowly-varying states. In this case, a short horizon with less sample count is preferable as the same MPC performance can be obtained but at a fraction of the computational cost. In this paper,we propose an adaptive regression-based MPC that predicts the bestminimum horizon length and the sample count from several features extracted from the time-varying changes of the states. The proposed technique builds a synthetic dataset using the system model and utilizes the dataset to train a support vector regressor that performs the prediction. The proposed technique is experimentally compared with several state-of-the-art techniques on both linear and non-linear models. The proposed technique shows a superior reduction in computational time with a reduction of about 35–65% compared with the other techniques without introducing a noticeable loss in performance.     

Sequencing of multi‑robot behaviors using reinforcement learning

Given a collection of parameterized multi-robot controllers associated with individual behaviors designed for particular tasks, this paper considers the problem of how to sequence and instantiate the behaviors for the purpose of completing a more complex, overarching mission. In addition, uncertainties about the environment or even the mission specifications may require the robots to learn, in a cooperative manner, how best to sequence the behaviors. In this paper, we approach this problem by using reinforcement learning to approximate the solution to the computationally intractable sequencing problem, combined with an online gradient descent approach to selecting the individual behavior parameters, while the transitions among behaviors are triggered automatically when the behaviors have reached a desired performance level relative to a task performance cost. To illustrate the effectiveness of the proposed method, it is implemented on a team of differential-drive robots for solving two different missions, namely, convoy protection and object manipulation.