Multilayer perceptron adaptive dynamic control of mobile robots : experimental validation

This paper presents experimental results acquired from the implementation of an adaptive control scheme for nonholonomic mobile robots, which was recently proposed by the same authors and tested only by simulations. The control system comprises a trajectory tracking kinematic controller, which generates the reference wheel velocities, and a cascade dynamic controller, which estimates the robot's uncertain nonlinear dynamic functions in real-time via a multilayer perceptron neural network. In this manner precise velocity tracking is attained, even in the presence of unknown and/or time-varying dynamics. The experimental mobile robot, designed and built for the purpose of this research, is also presented in this paper.peer-reviewe

Dual adaptive dynamic control of mobile robots using neural networks

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.peer-reviewe

Unscented transform-based dual adaptive control of nonlinear MIMO systems

The paper proposes a multilayer perceptron neural network controller for dual adaptive control of a class of stochastic MIMO nonlinear systems subject to functional uncertainty. The neural network parameters are adjusted in real-time using the Unscented Kalman filter algorithm and no pre-operational training phase is required. Dual adaptive control aims to strike a compromise between the two control characteristics of caution and probing, leading to an improved overall performance. The system is evaluated through numerical simulations and Monte Carlo analysis. The resulting performance of the dual adaptive controller is not only consistently superior to non-dual adaptive control schemes, but also surpasses the performance of similar controllers that are based on Extended Kalman filter estimators. This reflects the enhanced accuracy of the Unscented Kalman filter estimator, despite being a local estimation method. In addition, unlike use of other estimators, the proposed approach neither requires the computation of complex Jacobian matrices as part of the design, nor the evaluation of such matrices in real-time. This renders the proposed controller inherently amenable and practical for real-time implementation.peer-reviewe

Multi-robot energy-aware coverage control in the presence of time-varying importance regions

Multi-robot systems are becoming widely popular in applications where a rapid response is required or where various different robotic capabilities are required. Applications such as surveillance, or search and rescue, would require an efficient team that can be deployed and optimally dispersed over the environment. This is known as the coverage control problem. The solution to this research optimization problem is affected by several external aspects, such as characteristics of the environment as well as factors that pertain to the robotic team. This work proposes a novel solution to the complete coverage problem where the team of robots is restricted with energy limitations, and must cover an environment that has time-varying regions of importance. Our results show that in a realistic scenario, where the robots have limited energy for the task in question, the proposed solution performs significantly better than a typical coverage algorithm which disregards the energy considerations of the robotic team.peer-reviewe

Non-linear swing-up and stabilizing control of an inverted pendulum system

This paper presents the design and implementation of a complete control system for the swing-up and stabilizing control of an inverted pendulum. In particular, this work outlines the effectiveness of a particular swing-up method, based on feedback linearization and energy considerations. The power of modern state-space techniques for the analysis and control of Multiple Input Multiple Output (MIMO) systems is also investigated and a state-feedback controller is employed for stabilizing the pendulum. Cascade control is then utilized to reduce the complexity of the complete controller by splitting it into two separate control loops operating at well distinct bandwidths.Electrotechnical Association of Slovenia,et al.,IEEE Region 8,IEEE Slovenia Section,Ministry of Education, Science and Sport of the Republic of Slovenia,University of Ljubljana.peer-reviewe

Control of an open-loop hydraulic offshore wind turbine using a variable-area orifice

The research work disclosed in this publication is partly funded by the Malta Government Scholarship Scheme.The viability of offshore wind turbines is presently affected by a number of technical issues pertaining to the gearbox and power electronic components. Current work is considering the possibility of replacing the generator, gearbox and electrical transmission with a hydraulic system. Efficiency of the hydraulic transmission is around 90% for the selected geometries, which is comparable to the 94% expected for conventional wind turbines. A rotor-driven pump pressurises seawater that is transmitted across a large pipeline to a centralised generator platform. Hydroelectric energy conversion takes place in Pelton turbine. However, unlike conventional hydro-energy plants, the head available at the nozzle entry is highly unsteady. Adequate active control at the nozzle is therefore crucial in maintaining a fixed line pressure and an optimum Pelton turbine operation at synchronous speed. This paper presents a novel control scheme that is based on the combination of proportional feedback control and feed forward compensation on a variable area nozzle. Transient domain simulation results are presented for a Pelton wheel supplied by sea water from an offshore wind turbine-driven pump across a 10 km pipeline.peer-reviewe

The 25th Mediterranean Conference on Control and Automation

The 25th Mediterranean Conference on Control and Automation (MED 2017) was held on July 3-6, 2017 on the island of Malta. The first MED took place in 1993 in Chania, Greece, and a complete list of the locations of later MED conferences is available at www.med-control.org. The 2017 edition was held for the first time in Malta, at the University of Malta Valletta Campus. The campus is a 17th century baroque building endowed with striking historical frescoes, portraits, and architectural features that is also well equipped with modern conference facilities. The conference was organized under the auspices of the Mediterranean Control Association (MCA) and the technical cosponsorship of the IEEE Control Systems Society and the IEEE Robotics and Automation Society.peer-reviewe

Multilayer perceptron functional adaptive control for trajectory tracking of wheeled mobile robots

Sigmoidal multilayer perceptron neural networks are proposed to effect functional adaptive control for handling the trajectory tracking problem in a nonholonomic wheeled mobile robot. The scheme is developed in discrete time and the multilayer perceptron neural networks are used for the estimation of the robot’s nonlinear kinematic functions, which are assumed to be unknown. On-line weight tuning is achieved by employing the extended Kalman filter algorithm based on a specifically formulated multiple-input, multiple-output, stochastic model for the trajectory error dynamics of the mobile base. The estimated functions are then used on a certainty equivalence basis in the control law proposed in (Corradini et al., 2003) for trajectory tracking. The performance of the system is analyzed and compared by simulation.peer-reviewe

A comprehensive review of endogenous EEG-based BCIs for dynamic device control

Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.peer-reviewe

Multilayer perceptron dual adaptive control for mobile robots

This paper presents a novel dual adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed in discrete-time and the robot's nonlinear dynamic functions are assumed to be unknown. A sigmoidal multilayer perceptron neural network is employed for function approximation, and its weights are estimated stochastically in real-time. In contrast to adaptive certainty equivalence controllers hitherto published for mobile robots, the proposed control law takes into consideration the estimates' uncertainty, thereby leading to improved tracking performance. The proposed method is verified by realistic simulations and Monte Carlo analysis.non peer-reviewe