During the last decade, there has been considerable interest in autonomous systems (ASs). With the continuous development of control theory, powerful computer systems, engineering design, and sensor miniaturization, unmanned autonomous systems have been widely applied in agriculture, robotics, and industry, including (but not limited to) ground vehicles, process control systems, robotics, and manufacturing systems.

Modelling and control are two strongly related phases to study the ASs. The mathematical model describes the basic characteristics of the system and is the basis for the controller design. Although promising results on the modelling and control of ASs have been reported, many theoretical problems and application challenges in complex environments are still open. From the perspective of modelling, the challenges come from two aspects. Firstly, due to its own complexity (such as high order, large-scale, multi loop, and nonlinear information feedback structure, etc.), the more accurate the description is, the more complex the mathematical model of the AS will be. Therefore, how to make a reasonable compromise between the accuracy and complexity of the model is a problem we must face. Secondly, the model of the ASs in complex environments (e.g. electromagnetic interference, unknown disturbance) will undergo complex changes which cannot be accurately described. From the perspective of control design, since the accurate models of the ASs are extremely complex and impossible to be obtained, the researchers can only design the controller and analyze the system performance based on the approximate models. In addition, the ASs always operate under diverse and complex environmental conditions. These factors may lead to the controller being unable to meet the control performance requirements. Therefore, control solutions for ASs are required to be reliable and robust. With the advantages such as fast response, insensitive to the uncertainties and disturbance, easy to implement, etc., sliding mode control (SMC) has a broad application prospect in the field of complex system control. Recently, various SMC-based approaches have been proposed for the control of ASs in complex environments.

The aim of this Special Issue is to provide a forum for researchers and practitioners to exchange their latest theoretical and technological achievements and to identify critical issues and challenges for future investigation on the development of SMC for ASs in complex environments. The submitted papers are expected to bring up original ideas and potential contributions for theory and practice, including electrical machines, power electronics and drives, hydraulic/pneumatic actuators, robotics, automotive industry, vehicles, and complex industrial process. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Modelling and identification for ASs
• Model optimization algorithm
• Advanced sliding mode observer and control for ASs
• Intelligent, learning-based, and data-driven control for ASs
• State estimation and monitoring for ASs
• Fault diagnosis/tolerant control for ASs
• SMC for unmanned ASs
• Adaptive SMC for ASs
• Fuzzy-model-based SMC for ASs
• Event-triggered based SMC for ASs
• Finite-time stability for ASs
• Path planning and collision avoidance control for unmanned vehicles
• Cooperative control for multi-ASs
• Applications to agriculture, robotics, and industry fields