Coordination of Multiple Control Schemes for Mobile Robot Navigation on the Basis of the Generalized Stochastic Petri-Nets

There have been two major streams of research for the motion control of mobile robots: model-based deliberate control and sensor-based reactive control. Since the two schemes have complementary advantages and disadvantages, each cannot completely replace the other. There are a variety of environmental conditions that affect the performance of navigation. The motivation of this study is that multiple motion control schemes are required to survive in dynamic real environments. In this paper, we exploit two discrete motion controllers for mobile robots. One is the deliberate trajectory tracking controller and the other is the reactive dynamic window approach. We propose the selective coordination of two controllers on the basis of the generalized stochastic Petri net (GSPN) framework. The major scope of this paper is to clarify the advantage of the proposed controller based on the coordination of multiple controllers from the results of quantitative performance comparison among motion controllers. For quantitative comparison, both simulations and experiments in dynamic environments were carried out. In addition, it is shown that navigation experiences are accumulated in the GSPN formalism. The performance of navigation service can be significantly improved owing to the automatically stored experiences.     

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

Emergent behaviors of a fuzzy sensory-motor controller evolved bygenetic algorithm

Recently, there has been extensive work on the construction of fuzzy controllers for mobile robots by a genetic algorithm (GA); therefore, we can realize evolutionary optimization as a promising method for developing fuzzy controllers. However, much investigation on the evolutionary fuzzy controller remains because most of the previous works have not seriously attempted to analyze the fuzzy controller obtained by evolution. This paper develops a fuzzy logic controller for a mobile robot with a GA in simulation environments and analyzes the behaviors of the controller with a state transition diagram of the internal model. Experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved wen enough to smoothly drive the robot in different environments. The robot produces emergent behaviors by the interaction of several fuzzy rules obtained.     

神经网络PID 控制的机器人视觉反馈跟踪

探讨针对视觉空间的非完整移动机器人的跟踪控制问题。在不校准摄像机视觉参数的情况下,利用视觉反馈得到的信息,设计出非完整移动机器人轨迹跟踪的神经网络控制器。将BP网络与PID控制相结合,避免复杂的公式推导,解决参数不校准下的控制问题,并很好的实现跟踪。仿真结果证明了文中方法的有效性。     

Automatic controller tuning via unfalsified control

In this paper, we present a new algorithm for the automatic adaptation of linear single-loop controllers. The algorithm is based on the idea of unfalsified control, where a set of controllers is defined and using measured data, the controller from the set is chosen online which provides the best performance. This scheme is extended here by an adaptation of the set of controllers after a change of the operating point of the plant. The original concept of unfalsified control relies on the computation of the performance of controllers that are not in the loop using so-called fictitious signals. However, the proposed cost function leads to erroneous results, in particular, it is not possible to detect closed-loop instability that may result from inserting a controller. Therefore a different cost function and a new method for online evaluation of controllers that are not in the loop without an explicit plant model are proposed. With this new cost function, the set of controllers can be adapted as well which is performed using an evolutionary algorithm. The method is demonstrated for a well-known example, the nonlinear non-minimum phase CSTR model with van der Vusse reaction scheme and PID controllers.     

移动机器人网络控制系统的输出反馈控制

以线性时不变系统为被控对象,建立了四轮移动机器人网络控制系统的离散数学模型。诱导时延是影响系统性能的关键因素,通过在节点中设置缓冲区的方法可以将网络控制系统中的随机诱导时延转化为确定性时延,从而将网络控制系统由随机系统转化为确定性系统。通过被控对象移动机器人控制实验系统,设计了一个能处理网络诱导时延的输出反馈控制器,分析了采样周期和网络诱导时延对网络控制系统稳定性的影响。仿真结果表明了该控制器和控制策略的正确性及有效性。     

Stabilisation of perturbed chains of integrators using Lyapunov-based homogeneous controllers

In this paper, we present a Lyapunov-based homogeneous controller for the stabilisation of a perturbed chain of integrators of arbitrary order r ≥ 1. The proposed controller is based on homogeneous controller for stabilisation of pure chain of integrators. The control of homogeneity degree is also introduced and various controllers are designed using this concept, namely a bounded-controller with minimum amplitude of discontinuous control and a controller with globally fixed-time convergence. The performance of the controller is validated through simulations.     

A practical multiple model adaptive strategy for single-loop MPC

This paper details a multiple model adaptive control strategy for model predictive control (MPC). To maintain performance of this linear controller over a wide range of operating levels, a multiple model adaptive control strategy for dynamic matrix control (DMC), the process industry's standard for MPC, is presented. The method of approach is to design multiple linear DMC controllers. The tuning parameters for the linear controllers are obtained using novel analytical expressions. The controller output of the adaptive DMC controller is a weighted average of the multiple linear DMC controllers. The capabilities of the multiple model adaptive strategy for DMC are investigated through computer simulations and an experimental system.     

Hybrid trigonometric compound function neural networks for tracking control of a nonholonomic mobile robot

The purpose of this paper is to propose a hybrid trigonometric compound function neural network (NN) to improve the NN-based tracking control performance of a nonholonomic mobile robot with nonlinear disturbances. In the mobile robot control system, two NN controllers embedded in the closed-loop control system have the simple continuous learning and rapid convergence capability without the dynamics information of the mobile robot to realize the tracking control of the mobile robot. The neuron functions of the hidden layer in the three-layer feedforward network structure consist of the compound cosine function and the compound sine function combining a cosine or a sine function with a unipolar sigmoid function. The main advantages of this NN-based mobile robot control system are better real-time control capability and control accuracy by use of the proposed NN controllers for a nonholonomic mobile robot with nonlinear disturbances. Through simulation experiments applied to the nonholonomic mobile robot with the nonlinear disturbances of dynamics uncertainty and external disturbances, the simulation results show that the proposed NN control system of a nonholonomic mobile robot has better real-time control capability and control accuracy than the compound cosine function NN control system of a nonholonomic mobile robot and then verify the effectiveness of the proposed hybrid trigonometric compound function NN controller for improving the tracking control performance of a nonholonomic mobile robot with nonlinear disturbances.     

移动机器人视觉伺服镇定quasi-min-max预测控制

针对受限移动机器人视觉伺服系统,提出一种移动机器人视觉伺服镇定准最小最大模型预测控制策略. 基于移动机器人视觉伺服镇定误差模型,建立移动机器人视觉伺服线性参数时变预测模型,进而引入准最小最大策略,设计移动机器人视觉伺服镇定模型预测控制器.与传统视觉伺服预测控制器相比,所提控制器只需求解线性矩阵不等式表示的凸优化问题,降低了视觉伺服预测控制器的计算耗时,同时保证了闭环视觉伺服系统的渐近稳定性.仿真结果验证了所提出策略的有效性和在计算效率上的优越性.     

Low-order feedforward controllers: Optimal performance and practical considerations

Feedforward control from measurable disturbances can significantly improve the performance in control loops. However, tuning rules for such controllers are scarce. In this paper design rules for how to choose optimal low-order feedforward controller parameter are presented. The parameters are chosen so that the integrated squared error, when the system is subject to a step disturbance, is minimized. The approach utilizes a controller structure that decouples the feedforward and the feedback controller. The optimal controller can suffer from undesirable high-frequency noise characteristics and tuning methods for how to filter the control signal are also provided. For scenarios where perfect disturbance attenuation in theory is achievable but where noise-filtering is needed, the concept of precompensation is introduced as a way to shift the controller time-delay to compensate for the low-pass filtering.     

Robust adaptive control for a nonholonomic mobile robot with unknown parameters

A robust adaptive controller for a nonholonomic mobile robot with unknown kinematic and dynamic parameters is proposed. A kinematic controller whose output is the input of the relevant dynamic controller is provided by using the concept of backstepping. An adaptive algorithm is developed in the kinematic controller to approximate the unknown kinematic parameters, and a simple single-layer neural network is used to express the highly nonlinear robot dynamics in terms of the known and unknown parameters. In order to attenuate the effects of the uncertainties and disturbances on tracking performance, a sliding mode control term is added to the dynamic controller. In the deterministic design of feedback controllers for the uncertain dynamic systems, upper bounds on the norm of the uncertainties are an important clue to guarantee the stability of the closed-loop system. However, sometimes these upper bounds may not be easily obtained because of the complexity of the structure of the uncertainties. Thereby, simple adaptation laws are proposed to approximate upper bounds on the norm of the uncertainties to address this problem. The stability of the proposed control system is shown through the Lyapunov method. Lastly, a design example for a mobile robot with two actuated wheels is provided and the feasibility of the controller is demonstrated by numerical simulations.     

Liquid level control: Simplicity and complexity

From a superficial perspective, the control of liquid level in a vessel is a “non-problem.” Well-known process control heuristics advise using a proportional-only controller with a gain K_c = 2. However many plant operators are uncomfortable with the resulting “offset” (“steady-state error” or “droop”) between the process variable and the controller setpoint that is inherent in a proportional controller because of the lack of integral action to drive the error to zero. Therefore level controllers with proportional-integral (PI) action are often found in process units.The purpose of this paper is to illustrate that PI control does not provide effective attenuation of flow disturbances and that it actually amplifies them. We also show that PI level controllers can give confusing tuning results. In most closedloop systems, increasing controller gain makes the system more oscillatory (smaller closedloop damping coefficient). When PI controllers are used to control liquid level, the exact opposite occurs (increasing controller gain makes the system less oscillatory).     

The smooth switching of double modes fuzzy control

Various multimode controls are more and more widely applied in industry to improve the performance of control systems. Double modes fuzzy control is one of multimode controls, which has two independent and different mode controllers to satisfy different control demands. The smooth switching of different controllers is the key technology in industrial application of multimode modes control. Double modes fuzzy control is used to improve the dynamic and steady-state performances of control systems. This paper focuses on the unsteady problem at switching point of controllers in double modes control system. Three structures of double modes fuzzy control systems are proposed and discussed. The design principles of multimode control are analyzed. Three different switching methods are analyzed and their feasibility is studied. The concept of smooth switching from one controller to another controller is proposed. Especially the smooth switching of fuzzy/PI double modes control is analyzed, and the corresponding fuzzy controller is designed. The simulation of smooth switching at natural switching point of fuzzy/PI double modes control system is carried out in order to prove the superiority of smooth switching at natural switching point. The results of this paper can offer effective reference for other multimode control design.     

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

Control of a nonholomic mobile robot: Backstepping kinematics into dynamics

A dynamical extension that makes possible the integration of a kinematic controller and a torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed using backstepping, and asymptotic stability is guaranteed by Lyapunov theory. Moreover, this control algorithm can be applied to the three basic nonholonomic navigation problems: tracking a reference trajectory, path following, and stabilization about a desired posture. The result is a general structure for controlling a mobile robot that can accommodate different control techniques, ranging from a conventional computed-torque controller, when all dynamics are known, to robust-adaptive controllers if this is not the case. A robust-adaptive controller based on neural networks (NNs) is proposed in this work. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. On-line NN weight tuning algorithms that do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized. © 1997 John Wiley & Sons, Inc.     

Model predictive control of an electric arc furnace off-gas process

This paper shows that an electric arc furnace off-gas system can provide valuable manipulated variables for feedback control which can improve furnace efficiency and contribute to safety in the workplace. Model predictive control (MPC) is used to illustrate this concept using practically motivated control objectives. An initial verification of a non-linear furnace model with plant data is shown. The design of MPC controllers for the furnace is discussed and results are shown by way of simulation. Evaluation of the final controller against traditional manual operation is done, and the setpoint tracking capability of the controller is tested.     

Model-based PI–fuzzy control of four-wheeled omni-directional mobile robots

The purpose of this study is to suggest and examine a PI–fuzzy path planner and associated low-level control system for a linear discrete dynamic model of omni-directional mobile robots to obtain optimal inputs for drivers. Velocity and acceleration filtering is also implemented in the path planner to satisfy planning prerequisites and prevent slippage. Regulated drivers’ rotational velocities and torques greatly affect the ability of these robots to perform trajectory planner tasks. These regulated values are examined in this research by setting up an optimal controller. Introducing optimal controllers such as linear quadratic tracking for multi-input–multi-output control systems in acceleration and deceleration is one of the essential subjects for motion control of omni-directional mobile robots. The main topics presented and discussed in this article are improvements in the presented discrete-time linear quadratic tracking approach such as the low-level controller and combined PI–fuzzy path planner with appropriate speed monitoring algorithm such as the high-level one in conditions both with and without external disturbance. The low-level tracking controller presented in this article provides an optimal solution to minimize the differences between the reference trajectory and the system output. The efficiency of this approach is also compared with that of previous PID controllers which employ kinematic modeling. Utilizing the new approach in trajectory-planning controller design results in more precise and appropriate outputs for the motion of four-wheeled omni-directional mobile robots, and the modeling and experimental results confirm this issue.     

考虑运动受限的履带式移动机器人轨迹跟踪控制

针对履带式移动机器人的轨迹跟踪控制问题进行研究,首先,建立了履带式移动机器人的运动学模型和跟踪误差模型;其次,设计了转速有限时间控制和线速度滑模控制的轨迹跟踪控制律,并给出了考虑运动受限作用下的控制律修正表达式;最后,基于MATLAB对所提控制律进行仿真,对比分析了不考虑运动受限情况下跟踪控制效果;结果表明,设计的跟踪控制律能够实现履带式移动机器人对圆轨迹的有效跟踪,且考虑运动受限作用的控制律更加符合实际;文章研究分析了运动受限作用对于移动机器人轨迹跟踪控制的影响,分析结果对其他移动机器人的运动控制研究具有参考价值。     

Bio-inspired behaviour-based control

The design and development of conventional controllers for robot platforms are sometimes too complex to achieve due to the fact that they require an exact model of the system and of the operating environment. The ability to pre-account for unknown operating environments is an important task for the controller to be robust. In contrast, biological controllers are model free and are based on simple working principles. Due to natural biological principles these controllers are adaptive and more robust than their conventional counterparts. In this paper, a behaviour-based controller has been developed, inspired by the concept of spinal fields found in frogs and rats. The performance of the controller has been verified on a Khepera robot platform.