With the developments of wireless data communication and network technology, time-delays are widely found in nowadays' control systems, e.g. networked control systems, mobile robot systems, and multi-agent systems. Predictor-based control is an effective method dealing with long time-delays because it can generally lead to a delay-free closed-loop system by introducing a prediction for future states. Recently, various predictor-based control methods have been developed for numerous control systems subject to different time-delays, which motivates this survey. This paper presents a comprehensive review of the up-to-date results on the predictor-based control of time-delay systems. Firstly, the ordinary differential equation-based approaches for designing and analysing predictor-based controllers are summarised. Secondly, one reports an alternative method of predictor-based control, in which the systems/controllers are understood in the sense of partial differential equations. Next, several integration-free predictor-based controllers are introduced: by abandoning the infinite-dimensional integral terms, the control laws become easier to realise in practice. Hereafter, the paper discusses the real-time implementations and the practical applications of predictor-based control methods to several particular control systems. Finally, this paper suggests some new trends of predictor-based control for future research.

This article presents a learning-based control framework for a class of underactuated bicycle robots with an active pendulum attached to the rear body as a balancer. In contrast with the extent methods that require the exact dynamic modeling, the proposed solution deals with the uncertainties, including the impacts of changing road environment, unmodeled dynamics, and external disturbances by virtue of Gaussian process and disturbance cancellation on control device. The tracking controller using traction and steering is presented based on Lyapunov redesign. The self-balancing control of the roll-pendulum underactuated subsystem is considered. This subsystem is first converted into a pure-feedback form, and a novel backstepping controller based on dynamic inversion technique is developed to maintain the roll and pendulum angles in the balance equilibrium manifold. By using an event-triggered update in the learning process, efficient prediction can be achieved, and the tracking error and the roll angle are limited to smaller bounds that are explicitly given. The simulation verifies that adjustable performance can be offered for tracking by tuning the control gains and the hyperparameters of the Gaussian process predictor. Finally, experiments are conducted on a real bicycle platform to illustrate the effectiveness of the control framework.

There are significant couplings between control techniques and disturbance rejection techniques in traditional control schemes for systems with mismatched disturbances, such that both techniques have to be designed simultaneously. In this paper, independent of specific control methods, a universal disturbance rejection scheme named equilibrium compensation based control, is proposed for general systems to attenuate mismatched disturbances and sensor errors while retaining the nominal controller's performance. In addition, several criteria are provided to analyze the observability and compensability of the disturbances and sensor errors. Simulations and experiments of tracking tasks for a single-track-two-wheeled robot with four different controllers demonstrate the effectiveness of the proposed scheme.

The reaction wheel is able to help improve the balancing ability of a bicycle robot on curved pavement. However, preserving good control performances for such a robot that is driving on unstructured surfaces under matched and mismatched disturbances is challenging due to the underactuated characteristic and the nonlinearity of the robot. In this paper, a controller combining proximal policy optimization algorithms with terminal sliding mode controls is developed for controlling the balance of the robot. Online reinforcement-learning-based adaptive terminal sliding mode control is proposed to attenuate the influence of the matched and mismatched disturbance by adjusting parameters of the controller online. Different from several existing adaptive sliding mode approaches that only tune parameters of the reaching controller, the proposed method also considers the online adjustment of the sliding surface to provide adequate robustness against mismatched disturbances. The co-simulation experimental results in MSC Adams illustrate that the proposed controller can achieve better control performances than four existing methods for a reaction wheel bicycle robot moving on curved pavement, which verifies the robustness and applicability of the method.

The objective of this paper is to build feedback control solutions for LTI systems with unknown constant input-delay. The control scheme includes a time-delay estimation algorithm and a predictor-based controller. The Lyapunov-Krasovskii theorem provides the global exponential stability of the closed-loop system. Some examples illustrate the theoretical results and the performances of the proposed method.

The reaction wheel bicycle robot is a kind of unmanned mobile robot with great potential. However, the control of such bicycle robots on a curved pavement under inaccurate model parameters, model uncertainties and disturbances is challenging due to the lateral instability and udneractuated characteristic. Applying conventional control methods to this problem often results in brittle and inaccurate controllers. In this paper, an online serial-parallel combination reinforcement learning with conventional control methods is designed to achieve the path tracking and banlancing control for a reaction wheel bicycle robot on curved pavements. The parallel part of the controller refers to compensate the equilibrium point and the serial part of the controller refers to adjust the parameters of a sliding mode controller that tracks the target roll equilibrium point. The comparison between the proposed controller and several existing controllers in experimental test built in Matlab Simscape illustrates stronger robustness and better control performances.