Practical issues in data-driven model-free adaptive control for an omnidirectional mobile manipulator

This article focuses on addressing three practical issues encountered when applying a data-driven model-free adaptive control (MFAC) approach to mobile robots. The first practical issue lies in a common assumption in MFAC schemes that the sign of all elements in pseudo-partial derivative (PPD) should be constant, while it cannot be satisfied if omnidirectional mobile manipulators (OMMs) move with platform rotation. To solve this problem, a new coordinate frame is introduced, which is crucial for applying MFAC to any mobile robots with rotation. The second one is that the initial value setting method for estimation of PPD is unclear. Improper settings may easily lead to control system instability. An initial value setting method for estimation of PPD is proposed with explicit physical interpretation. Lastly, applying the typical MFAC scheme directly to OMM fails to converge well to the desired trajectory. To tackle this, a new data-driven MFAC controller is proposed by incorporating a sliding mode control. Finally, experimental tests on an OMM are carried out to verify the effectiveness of the proposed control scheme. To the best of our knowledge, this is the first MFAC scheme that has been experimentally verified on a prototype mobile robot with rotation.

Trajectory tracking nonlinear H∞ controller for wheeled mobile robots with disturbances observer

Wheeled Mobile Robots (WMRs) are systems with multiple industrial and civilian applications. Trajectory tracking is essential in many applications, such as surveillance, monitoring, and autonomous driving. However, in practical applications, a WMR is always affected by kinematic disturbances, state estimation error, and measurement noise, which may diminish the system's performance. Hence, this work proposes a novel observer-based H∞ controller that is robust against matched and unmatched disturbances. The proposed methodology compensates for disturbances through a disturbance observer, transforming the closed-loop system into a new one affected by uniformly bounded disturbances. Then, an H∞ controller is designed to make the WMR track a desired reference signal. A formal stability proof demonstrates the feasibility of the new proposal. Also, feedback and finite-time controllers are used to assess the novel controller. Numerical simulations and experimental results with a scaled autonomous car-like robot demonstrate the novel controller's efficiency and outstanding performance, despite disturbances when compared against finite-time and feedback controllers.

Self-learning sliding mode control based on adaptive dynamic programming for nonholonomic mobile robots

This paper proposes a self-learning sliding mode control (SlSMC) strategy with stability guarantee for the trajectory tracking of nonholonomic mobile robots (NMRs) under matched uncertainties, which improves the control performance of NMRs by optimizing the reaching law and the sliding mode surface of SMC as well as retaining the finite-time convergence and the robustness to uncertainties. In the presence of adverse factors such as skidding, slipping and environmental noise, the kinematic model of NMRs is reconstructed and an integral terminal sliding mode controller is designed for the trajectory tracking of NMRs. Then, based on the sliding mode controller, the proposed control strategy formulates the optimization of the SMC's reaching law and the sliding mode surface under stability constraints as two asynchronous optimal control problems with control constraints. Meanwhile, an online continuous-time receding-horizon optimization mechanism based on an actor-critic algorithm is proposed to solve the optimal problems asynchronously and improve online learning efficiency. The stability and the convergence of the proposed strategy are validated both in theory and simulations. Furthermore, extensive contrastive simulation results illustrate that the proposed receding horizon learning-based control strategy outperforms three recent methods in control performance. Finally, experiments of the proposed self-learning SMC strategy are carried out based on a real intelligent vehicle, and the experimental results also verify that the proposed method can meet the actual control needs of NMRs.

Constructive exponential tracking control for mechanical systems via Hamiltonian realization and contraction analysis 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.

Time-varying encounter angle trajectory tracking control of unmanned surface vehicle based on wave modeling

In this note, a wave simulation method based on the wave spectrum is proposed, and the wave simulation is transformed into external interference to verify the necessity of using variable encounter angle real wave interference. Firstly, A wave simulation method based on wave spectrum and equidistant method is proposed and demonstrated. Secondly, wave modeling is transformed into interference force related to the encounter angle by fully considering the real marine environment. Furthermore, a trajectory tracking controller with variable encounter angles and the actual sea state is designed using the disturbance modeling method. Finally, the necessity and authenticity of considering varying encounter angles and real sea conditions in the motion control of unmanned surface vehicles (USVs) are proved by simulation.

Formation Control and Tracking of Mobile Robots using Distributed Estimators and A Biologically Inspired Approach

This paper investigates the formation control problem for multiple nonholonomic wheeled mobile robots using distributed estimators and a biologically inspired approach. The formation pattern of the system adopts leader-follower structure and the communication topology among the multi-robot system is modelled by an undirected graph. In our proposed methodology, first, we develop an adaptive trajectory tracking control for the leader robot to follow the desired trajectory. Second, a distributed estimator is designed for each follower mobile robot, which uses its own information to estimate the leader's states, such as position, orientation, and linear velocity. Then, distributed formation tracking control laws are designed based on the distributed estimator. Furthermore, a bioinspired controller is developed to address the impractical velocity jump problem. The closed-loop system stability is analysed with the Lyapunov stability theory showing that tracking errors are asymptotically converge to zero. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods.

Distributed event-triggered fixed-time formation and trajectory tracking control for multiple stratospheric airships

In this study, an event-triggered fixed-time multiple stratospheric airship formation trajectory tracking controller is designed, and it is composed of two parts: the airship leader trajectory tracking controller (ALTTC) and the airship follower formation tracking controller (AFFTC). First, based on the framework of backstepping, the fixed-time ALTTC is designed to allow the trajectory tracking error to converge to zero within a fixed time. Subsequently, the event-triggered fixed-time AFFTC is designed to reduce the formation tracking error to zero within a fixed time. Two event-triggering conditions are designed to reduce the transmission times of control inputs and calculation times of control outputs. The fixed-time stability and the trajectory-tracking and formation-tracking performance of event-triggered closed-loop systems are theoretically shown to be ensured, and Zeno behavior is excluded in the proposed asynchronous event-triggering mechanism. Finally, simulations indicate the effectiveness of the proposed controller.

Optimization of autonomous driving state control of low energy consumption pure electric agricultural vehicles based on environmental friendliness

The distributed driven agricultural vehicle platform studied in this paper is a basic carrying platform that can be used for a variety of agricultural activities (pesticide spraying, weeding, and other plant protection operations). It does not need to rely on traditional fuel engine to drive but is driven by electric power, which is more environmentally friendly and conducive to the development of ecological agriculture in the future. In this paper, according to the driving and structure characteristics of distributed driving electric agricultural vehicles, in order to ensure the vehicle trajectory tracking ability, under conditions of agricultural operation, the driving attitude and drive coordination control of electric agricultural vehicles are taken as the key research objectives. In this way, the driving path of the vehicle platform can be accurately controlled during the operation of pesticide spraying and herbicide, so as to reduce the excessive use or misuse of pesticides and herbicides and greatly reduce the field pollution. Considering the specific driving environment as well as complex and changeable motion patterns of agricultural vehicles, when a single motion model is used to track and estimate the driving state, there will be low filtering accuracy or even loss of the target during vehicle maneuvering. In this paper, interactive multiple model (IMM) algorithm is combined with extended Kalman filter to effectively track changes of the target's motion mode, thereby avoiding low filtering accuracy or serious state estimation inaccuracy. Finally, through the distributed electric drive agricultural operation experimental platform developed by the research group, the working conditions close to the actual agricultural production activities was set up according to the needs of actual agricultural production activities in this paper, and applicability and accuracy of the algorithm state estimation are verified by experiments.

State of the art in parallel ankle rehabilitation robot: a systematic review

Background

The ankle joint complex (AJC) is of fundamental importance for balance, support, and propulsion. However, it is particularly susceptible to musculoskeletal and neurological injuries, especially neurological injuries such as drop foot following stroke. An important factor in ankle dysfunction is damage to the central nervous system (CNS). Correspondingly, the fundamental goal of rehabilitation training is to stimulate the reorganization and compensation of the CNS, and to promote the recovery of the motor system’s motor perception function. Therefore, an increasing number of ankle rehabilitation robots have been developed to provide long-term accurate and uniform rehabilitation training of the AJC, among which the parallel ankle rehabilitation robot (PARR) is the most studied. The aim of this study is to provide a systematic review of the state of the art in PARR technology, with consideration of the mechanism configurations, actuator types with different trajectory tracking control techniques, and rehabilitation training methods, thus facilitating the development of new and improved PARRs as a next step towards obtaining clinical proof of their rehabilitation benefits.

Methods

A literature search was conducted on PubMed, Scopus, IEEE Xplore, and Web of Science for articles related to the design and improvement of PARRs for ankle rehabilitation from each site’s respective inception from January 1999 to September 2020 using the keywords “ parallel”, “ ankle”, and “ robot”. Appropriate syntax using Boolean operators and wildcard symbols was utilized for each database to include a wider range of articles that may have used alternate spellings or synonyms, and the references listed in relevant publications were further screened according to the inclusion criteria and exclusion criteria.

Results and discussion

Ultimately, 65 articles representing 16 unique PARRs were selected for review, all of which have developed the prototypes with experiments designed to verify their usability and feasibility. From the comparison among these PARRs, we found that there are three main considerations for the mechanical design and mechanism optimization of PARRs, the choice of two actuator types including pneumatic and electrically driven control, the covering of the AJC’s motion space, and the optimization of the kinematic design, actuation design and structural design. The trajectory tracking accuracy and interactive control performance also need to be guaranteed to improve the effect of rehabilitation training and stimulate a patient’s active participation. In addition, the parameters of the reviewed 16 PARRs are summarized in detail with their differences compared by using figures and tables in the order they appeared, showing their differences in the two main actuator types, four exercise modes, fifteen control strategies, etc., which revealed the future research trends related to the improvement of the PARRs.

Conclusion

The selected studies showed the rapid development of PARRs in terms of their mechanical designs, control strategies, and rehabilitation training methods over the last two decades. However, the existing PARRs all have their own pros and cons, and few of the developed devices have been subjected to clinical trials. Designing a PARR with three degrees of freedom (DOFs) and whereby the mechanism’s rotation center coincides with the AJC rotation center is of vital importance in the mechanism design and optimization of PARRs. In addition, the design of actuators combining the advantages of the pneumatic-driven and electrically driven ones, as well as some new other actuators, will be a research hotspot for the development of PARRs. For the control strategy, compliance control with variable parameters should be further studied, with sEMG signal included to improve the real-time performance. Multimode rehabilitation training methods with multimodal motion intention recognition, real-time online detection and evaluation system should also be further developed to meet the needs of different ankle disability and rehabilitation stages. In addition, the clinical trials are in urgent need to help the PARRs be implementable as an intervention in clinical practice.

A structure-improved extended state observer based control with application to an omnidirectional mobile robot

This paper presents a structure-improved extended state observer (SESO) based trajectory tracking control scheme with application to an omnidirectional mobile robot. To alleviate the initial peaking phenomenon of the traditional extended state observer (TESO), a SESO with reduced order is proposed by improving the structure of TESO. Moreover, the designed SESO can achieve superior estimation performances. The total disturbances are estimated by SESO and then compensated in the controller. Then a phase-based nonlinear proportional-differential controller with time-varying gains is applied for high trajectory tracking performance. The stability of SESO and the closed-loop system are analyzed, respectively. Finally, the effectiveness of the proposed control scheme is validated through simulations in both frequency domain and time domain as well as experimental tests.

A family of saturated controllers for UWMRs

Input saturation appears in a physical system when a large power dissipation is requested. In this situation, and specifically for unicycle-type wheeled mobile robots, actuators only can deliver a finite amount of power. Thus, in practice the linear and angular velocity input of this class of mobile robots is limited and this should be considered in the control design. In this paper, a family of controllers that produce saturated velocity input for unicycle-type wheeled mobile robots is presented. The proposed family of controllers is designed to satisfy the trajectory tracking control goal. Sufficient conditions to prove the closed-loop system global asymptotic stability are established by using Lyapunov's theory. Already reported schemes and original designs are shown to satisfy the properties of the given family of controllers. By using two different motion tasks, experimental tests in real-time with five saturated control schemes are presented in order to validate the proposed theory. In order to show the ability of the family of controllers to produce limited control action, experiments have also been carried out with an unsaturated algorithm. Better tracking accuracy is obtained with the original design derived from the proposed class of algorithms.

Trajectory planning and tracking control of a ground mobile robot:A reconstruction approach towards space vehicle

With the development of the similarity calculation method, the orbital motion of space vehicle can be translated into a sequence of waypoints that reflect position and velocity on the ground. In this paper, a motion control system is proposed to make the mobile robot pass through the desired waypoints for reconstructing the orbital motion. First, the parameterized trajectory optimization method is applied to generate a curvature-continuous trajectory from the waypoints, the position and velocity demands are presented as the equality constraints. Virtual positions are introduced to reduce the oscillation, and the total execution time of the whole trajectory is selected as the optimization parameter to reduce the computational burden. Then, an equivalence transformation is provided to translate the error system into an affine form, which is beneficial for the feedback controller design. Based on this, a nonlinear trajectory tracking controller is proposed, which includes a feedforward controller and an error feedback controller, and its exponential stability is proved using Persistency of Excitation Lemma. In addition, a shunting neural dynamics model is employed to avoid sharp velocity jumps. Finally, the performed experiments verify the effectiveness of the proposed method.

Trajectory exponential tracking control of unmanned surface ships with external disturbance and system uncertainties

Any unmanned surface ship is subject to system uncertainty, unknown parameters, and external disturbance induced by the wind, the wave loads, and the ocean currents. They may deteriorate ship's control accuracy. This paper aims to solve the trajectory tracking control problem of unmanned surface ships with disturbance and system uncertainty accommodated simultaneously. An estimator-based backstepping controller is presented with an estimator designed to provide a precise estimation of the disturbance and uncertainties. The proposed controller ensures the closed-loop tracking system to be globally exponentially stable. The trajectory tracking error and the estimation error of disturbance and uncertainties are globally exponentially stable. The key feature of the developed control scheme is that it is more robust to disturbances and system uncertainties. Simulation results are further presented to validate the effectiveness of the approach.

Lane changing trajectory planning and tracking control for intelligent vehicle on curved road

This paper explores lane changing trajectory planning and tracking control for intelligent vehicle on curved road. A novel arcs trajectory is planned for the desired lane changing trajectory. A kinematic controller and a dynamics controller are designed to implement the trajectory tracking control. Firstly, the kinematic model and dynamics model of intelligent vehicle with non-holonomic constraint are established. Secondly, two constraints of lane changing on curved road in practice (LCCP) are proposed. Thirdly, two arcs with same curvature are constructed for the desired lane changing trajectory. According to the geometrical characteristics of arcs trajectory, equations of desired state can be calculated. Finally, the backstepping method is employed to design a kinematic trajectory tracking controller. Then the sliding-mode dynamics controller is designed to ensure that the motion of the intelligent vehicle can follow the desired velocity generated by kinematic controller. The stability of control system is proved by Lyapunov theory. Computer simulation demonstrates that the desired arcs trajectory and state curves with B-spline optimization can meet the requirements of LCCP constraints and the proposed control schemes can make tracking errors to converge uniformly.