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.

Literature reviews on Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Other reviews only consider works that aim to address a specific problem or one particular application of MRS. In contrast, this paper presents a survey of recent research works on MRS and categorises them according to their application domain. Furthermore, this paper compiles a number of seminal review works that have proposed specific taxonomies in classifying fundamental concepts, such as coordination, architecture and communication, in the field of MRS.

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.

This paper proposes and investigates the application of sliding mode control to the ball and plate problem. The nonlinear properties of the ball and plate control system are first presented. Then the experimental setup designed and built specifically for the purpose of this research is discussed. The paper then focuses on the implementation and thorough evaluation of the experimental results obtained with two different control schemes: the linear full-state feedback controller and the sliding mode controller. The latter control strategy was selected for its robust and order reduction properties. Finally the control performance of the two controllers is analysed. The sliding controller manages to obtain a faster and more accurate operation for continuously changing reference inputs. The robustness of the proposed control scheme is also verified, since the system’s performance is shown to be insensitive to parameter variations.

Current research in offshore wind turbines is proposing a novel concept of using seawater-based hydraulics for large-scale power transmission and centralized electrical generation. The objective of this paper is to investigate the control of such an open-loop circuit, where a fixed line pressure is desirable for the sake of efficiency and stability. Pressure control of the open-loop hydraulic circuit presents an interesting control challenge due to the highly fluctuating flow rate along with the nonlinear behavior of the variable-area orifice used by the pressure controller. The present analysis is limited to a single turbine and an open-loop hydraulic line with a variable-area orifice at the end. A controller is proposed which uses a combination of feed-forward compensation for the nonlinear part along with a feedback loop for correcting any errors resulting from inaccuracies in the compensator model. A numerical model of the system under investigation is developed in order to observe the behavior of the controller and the advantages of including the feedback loop. An in-depth analysis is undertaken, including a sensitivity study of the compensator accuracy and a parametric analysis of the actuator response time. Finally, a Monte Carlo analysis was carried out in order to rank the proposed controller in comparison to a simple feed-forward controller and a theoretical optimally tuned controller. Results indicate an advantageous performance of the proposed method of feedback with feed-forward compensation, particularly its ability to maintain a stable line pressure in the face of high parameter uncertainty over a wide range of operating conditions, even with a relatively slow actuation system.