Theory and Application of the Z-Transform Method

Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems

Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

AUV Trajectory Tracking Models and Control Strategies: A Review

Standard model predictive control is an optimization-based control strategy that can handle multiple control objectives and system nonlinear constraints. However, it typically suffers from the limitation of the uncertainties in practical systems, such as external unknown disturbances and parametric uncertainties. Motivated by aforementioned limitation, in this article, a novel robust model predictive control framework, endowed with the merits of fuzzy logic system and finite control-set model predictive control solution, is proposed. The main objective of this article is to enhance the system robustness while guaranteeing adaptability to different conditions. More specifically, a fuzzy approximation point of view, which has a good potential to approximate the unknown nonlinear functions, is deployed and incorporated into the proposed design, which allows one to explicitly take the system nonlinear dynamics and uncertainties into account. The novelty of the proposed methodology relies on the fact that any prior knowledge and explicit information of system model parameters are not required, thereby resulting in considerable enhancement of robustness. Furthermore, the input-to-state stability of the approximation error system is proven through Lyapunov analysis, and it demonstrates that the estimated errors are uniformly ultimately bounded. Finally, the interest of the proposal is experimentally confirmed for modular multilevel converter.

A Fuzzy Approximation for FCS-MPC in Power Converters

The control issue of Autonomous Underwater Vehicles (AUV) is very challenging since the precise mathematical model of AUV is hard to establish due to its strong coupling and time-varying features. Meanwhile, AUV movement is easily interfered with by ocean currents and waves, causing anti-interference performance of traditional Proportional-Integral-Derivative (PID) control to be unsatisfactory. Aiming to solve those problems, an algorithm of fuzzy optimized model-free adaptive control (MFAC) based on auto-disturbance rejection control (ADRC) was proposed and used in AUV heading control. The MFAC is used to overcome the difficulty with establishing a precise mathematical model, and the ADRC is introduced to handle the interference of currents and waves. In this paper, MFAC and ADRC are combined. First, the MFAC is performed based only on the I/O data of the controlled object, which is simple to implement with low calculation complexity and strong robustness. Then, a tracking differentiator (TD) is employed to track the input signal to overcome the antinomy of rapidity and hypertonicity in MFAC. After that, an extended-state observer (ESO) is added to control the variables of MFAC to estimate all the disturbances, which can greatly improve the anti-interference ability of the system. Due to the complexity and diversity of the marine environment, a fuzzy optimized MFAC based on ADRC is proposed to improve the adaptability of AUV to the marine environment. Simulations and experiments were carried out to verify the control effect of this algorithm in complex sea conditions.

https://www.mdpi.com/2079-9292/8/6/608/pdf

Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control

Linearization is often used for control design of nonlinear systems but what degree of a linearization is sufficient for the controller design is always a question. Furthermore, most of the existing linearization methods aim to develop a completely linear model without retaining any nonlinearity and thus the unmodeled dynamics unavoidably exists due to omitted higher order terms. In this article, a local compact form dynamic linearization (local-CFDL) is developed at first to transform the original nonlinear nonaffine system into an affine structure consisting of both an unknown residual nonlinear time-varying term and a linearly parametric term affine to the control input. A discrete-time extended state observer (DESO) is introduced to estimate the unknown residual nonlinear time-varying term as a new extended state. Then, a local-CFDL-based DESO-model-free adaptive control (MFAC) is proposed where the estimation of DESO is incorporated to compensate for the disturbances and uncertainties. Furthermore, a local partial-form dynamic linearization (local-PFDL) is also presented using multi-lag inputs and partial derivatives. And, a corresponding local-PFDL-based DESO-MFAC is proposed utilizing additional control information to improve control performance. The two proposed methods are both data-driven and do not require any explicit model information. Theoretical analysis shows the robust convergence of the proposed methods in the presence of disturbances. Simulations verify the effectiveness of the proposed method and show that the local-PFDL-based DESO-MFAC outperforms the local-CFDL-based one owing to the use of additional control information.

Discrete-Time Extended State Observer-Based Model-Free Adaptive Control Via Local Dynamic Linearization

This work explores a model-free adaptive PID (MFA-PID) control for nonlinear discrete-time systems with rigorous mathematical analysis under a data-driven framework. An improved compact form dynami...

Model-free adaptive PID control for nonlinear discrete-time systems:

In this work, a new model-free adaptive control with an extended state observer for a class of nonlinear non-affine system has been proposed. The main feature is that the controller design depends merely on the I/O data, and an ESO has been introduced to deal with the uncertainty and disturbance problem. The effectiveness of the proposed control scheme is demonstrated by a numerical simulation.

A New Model-Free Adaptive Control with an Extended State Observer

Linearization technique is inevitable for a nonlinear control system design. However, the traditional linearization methods require model information, which is difficult to obtain for the complex nonlinear system. In this article, a new local dynamic linearization method is proposed via a mean-value theorem and can be estimated by using the I/O data only. Then a new adaptive iterative learning control is proposed by using the optimal technology. The simulation verifies the monotonic convergence and practicability of this method.

Data-driven Adaptive Iterative Learning Control Based on a Local Dynamic Linearization

This paper is concerned with the consensus tracking problem for a class of nonlinear discrete-time multi-agent systems (MASs). The dynamic linearization method is used to approximate the nonlinear dynamics of the addressed MASs, resulting in an equivalent linear time-varying data model. With the purpose of mitigating the effects from limited communication bandwidth, a uniform-quantization-based encoding-decoding mechanism is exploited. A model-free adaptive distributed control protocol is put forward to deal with the tracking problem, which is totally data-driven without any requirement of model information except for I/O data. Finally, two illustrative simulation examples are utilized to demonstrate the effectiveness of the proposed control scheme.

Shuhua Zhang

Papers

Discrete-Time Extended State Observer-Based Model-Free Adaptive Control Via Local Dynamic Linearization

Model-free adaptive PID control for nonlinear discrete-time systems:

A New Model-Free Adaptive Control with an Extended State Observer

Data-driven Adaptive Iterative Learning Control Based on a Local Dynamic Linearization

Model-Free Adaptive Control for Nonlinear Multi-Agent Systems With Encoding-Decoding Mechanism