Application of Hidden Markov Model to locate soccer robots

© 2015 Technical Committee on Control Theory, Chinese Association of Automation. This paper adopts a Hidden Markov Model as a basis for predicting the probabilities in location of soccer robot's trajectories, develops the corresponding algorithms, and then demonstrates the simplicity of the procedure with simulations. The purpose of the initial presentation is to establish a proper platform for the future comprehensive studies of using Hidden Markov Models to assemble critical observations with uncertainties or random measurement errors in stochastic system modelling and control

轮式移动机器人瞬态模型鲁棒自适应同步终端滑模编队控制 (Robust adaptive synchronized formation control for the transient model of wheeled mobile robots with terminal sliding mode)

In the cooperative formation of wheeled mobile robots, the problem how to guarantee that mobile robots can track their own trajectories while synchronizing motions with others puts forward higher requirements on the design of control algorithms. A robust adaptive synchronized control with terminal sliding mode based on the algebraic graph theory is developed to solve this problem. Firstly, the nonlinear kinematics transient model of wheeled mobile robot is introduced. This model avoids the problem of multi-input coupling mutual interference in general kinematics model. Then, the synchronized control algorithm is designed according to the cross-coupling errors to realize the motion synchronization, and the external disturbance of the system is suppressed by the robust control. The adaptive law ensures the real-time adjustment of the switching gain. The stability analysis is carried out by using the Lyapunov method, which proves the convergence of the system tracking errors. Finally, the effectiveness of the designed algorithm is verified by MATLAB simulation

Terminal sliding mode control for continuous stirred tank reactor

© 2014 The Institution of Chemical Engineers. A continuous stirred tank reactor (CSTR) is a typical example of chemical industrial equipment, whose dynamics represent an extensive class of second order nonlinear systems. It has been witnessed that designing a good control algorithm for the CSTR is very challenging due to the high complexity. The two difficult issues in CSTR control are state estimation and external disturbance attenuation. In general, in industrial process control a fast and robust response is essential. Driven by these challenging issues and desired performance, this paper proposes an output feedback terminal sliding mode control (TSMC) framework which is developed for CSTR, and can estimate the system states and stabilize the system output tracking error to zero in a finite time. The corresponding stability analysis is presented in terms of the Lyapunov method. Illustrative examples are demonstrated by using Matlab simulations to validate the effectiveness of the proposed approach

Adaptive output feedback finite time control for a class of second order nonlinear systems

This paper develops a finite time output feedback based control scheme for a class of nonlinear second order systems. The system representation includes both model uncertainty and uncertain parameters. A finite time parameter estimator is developed. This facilitates the design of a finite time observer based on the well-established step-by-step sliding mode observer design approach. A terminal sliding mode control scheme is then developed using the corresponding state estimates. The methodology is applied to a continuous stirred tank reactor system to validate the effectiveness of the proposed approach

A co-design method of online learning SMC law via an input-mappping strategy

The research on sliding mode control strategy is generally based on the robust approach. The larger parameter space consideration will inevitably sacrifice part of the performance. Recently, the data-driven sliding mode control method attracts much attention and shows excellent benefits in the fact that data is introduced to compensate the controller. Nevertheless, most of the research on data-driven sliding mode control relied on identification techniques, which limits its online applications due to the special requirements of the data. In this paper, an input-mapping technique is inserted into the design framework of sliding mode control to compensate for the influence generated by the unknown dynamic of the system. The major novelty of the proposed input-mapping sliding mode control strategy lies in that the sliding mode surface and the sliding mode controller are co-designed through online learning from historical input-output data to minimize an objective function. Then, the convergence rate of the system is improved significantly based on the method designed in this work. Finally, some simulations are provided to show the effectiveness and superiority of the proposed methods

A new stepwise and piecewise optimization approach for CO2 pipeline

© 2016 . The process of CO2 capture, transportation, enhanced oil recovery (EOR) and storage is one of the best ways for CO2 emission reduction, which is also named as Carbon Capture, Utilization and Storage (CCUS). It has been noted that CO2 transportation cost is an important component of the total investment of CCUS. In this paper, a novel stepwise and piecewise optimization is proposed for CO2 transportation design, which can compute the minimum transportation pipeline levelized cost under the effect of temperature variation. To develop the proposed approach, several models are referred to lay a foundation for the optimization design. The proposed optimal algorithm is validated by using numerical studies, which shows the approach can reduce the levelized cost and improve the optimization performance in comparison with the existing methods

Nonlinear Sliding Mode Control for Interconnected Systems with Application to Automated Highway Systems

In this paper, a decentralised control strategy based on sliding mode techniques is proposed for a class of nonlinear interconnected systems. Both matched uncertainties in the isolated subsystems and mismatched uncertainties associated with the interconnections are considered. Under mild conditions, sliding mode controllers for each subsystem are designed in a decentralised manner by only employing local information. Conditions are determined which enable information on the interconnections to be employed within the decentralised controller design to reduce conservatism. The developed results are applied to an automated highway system. Simulation results pertaining to a high-speed following system are presented to demonstrate the effectiveness of the approach

Review of rational (total) nonlinear dynamic system modelling, identification, and control

© 2013 Taylor & Francis. This paper is a summary of the research development in the rational (total) nonlinear dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined as those where the model parameters and input (controller outputs) are subject to nonlinear to the output. Previously, this class of models has been known as rational models, which is a model that can be considered to belong to the nonlinear autoregressive moving average with exogenous input (NARMAX) model subset and is an extension of the well-known polynomial NARMAX model. The justification for using the rational model is that it provides a very concise and parsimonious representation for highly complex nonlinear dynamic systems and has excellent interpolatory and extrapolatory properties. However, model identification and controller design are much more challenging compared to the polynomial models. This has been a new and fascinating research trend in the area of mathematical modelling, control, and applications, but still within a limited research community. This paper brings several representative algorithms together, developed by the authors and their colleagues, to form an easily referenced archive for promotion of the awareness, tutorial, applications, and even further research expansion

Distributed Robust Partial State Consensus Control for Chain Interconnected Delay Systems

Partial state consensus (PSC) is investigated for chain interconnected systems with time-varying delays and parameter uncertainties. A novel design philosophy of PSC control is proposed and a sequential calculation method is presented to guarantee the robustness of the controller. A sufficient condition based on linear matrix inequalities (LMIs) is derived and the stability is proven by the Lyapunov method. The proposed approach can ensure that the states which are subject to a consensus constraint achieve consensus, while those without a consensus constraint track their own set points. Finally, numerical simulations and a solution proportioning experiment are developed to validate the effectiveness of the proposed method