Point stabilization of mobile robots with nonlinear model predictive control

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.

This paper is concerned with a computationally efficient (suboptimal) nonlinear model-based predictive control (MPC) algorithm and its application to a high-purity high-pressure ethylene-ethane distillation column. A neural model of the process is used on-line to determine the local linearisation and the nonlinear free response. In comparison with general nonlinear MPC technique, which hinges on non-convex optimisation, the presented structure is far more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop control performance is similar.

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Nonlinear model predictive control for the space heating system of a university building in Norway

This article presents the development of a Computed Torque Control (CTC) scheme for Cartesian velocity control of Wheeled Mobile Robots (WMRs). The literature presents extensive study on the need and suitability of the CTC scheme for robot arms. Many researchers have identified the benefits of using a CTC scheme for mobile robots. There is however very little information on CTC schemes for controlling mobile robots. The need for the CTC scheme stems from the fact that mobile robots (industrial AGVs) employing conventional velocity control schemes experience side slip due to suspended loads while negotiating curves, and the controller gains and accelerations need to be modified for changes in the dynamics. The structure of the proposed control scheme can be employed to control any mobile robot for which an inverse dynamic model exists, as a CTC scheme requires an inverse dynamic model to compute unique values for the motor current for a given trajectory. It is demonstrated that the existence of the inverse dynamic model is guaranteed for all differentially driven WMRs for all operating conditions, and is not affected by the number of castor wheels in the WMR. In the proposed CTC scheme, the linear and angular velocities of the WMR are controlled by adjusting the WMR accelerations, which are computed based on the motor torques required to follow a given trajectory. The motor torque is pre-computed based on a dynamic model of the mobile robotic system. The simulation and experimental results presented for a differentially driven WMR demonstrate that a computed-torque control scheme provides adaptive cruising and steering control for nominally tuned controller gains compared to a conventional velocity controller to achieve proper road following in the presence of changes in the dynamics. © 1997 John Wiley & Sons, Inc.

A recurrent neuro-fuzzy network-based nonlinear long range model predictive control strategy is proposed in this paper. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification. Based upon a neuro-fuzzy network model, a nonlinear model-based predictive controller can be developed by combining several local linear model-based predictive controllers which usually have analytical solutions. This strategy avoids the time consuming numerical optimisation procedure, and the uncertainty in convergence to the global optimum which are typically seen in conventional nonlinear modelbased predictive control strategies, Furthermore, control actions obtained based on local incremental models contain integration actions which can naturally eliminate static control offsets. The technique is demonstrated by an application to the modelling and control of liquid level in a water tank.

Several research studies are conducted based on the control of wheeled mobile robots. Nonholonomy constraints associated with wheeled mobile robots have encouraged the development of highly nonlinear control techniques. Nonholonomic wheeled mobile robot systems might be exposed to numerous payloads as per the application requirements. This can affect statically or dynamically the complete system mass, inertia, the location of the center of mass, and additional hardware constraints. Due to the nonholonomic and motion limited properties of wheeled mobile robots, the precision of trajectory tracking control is poor. The nonholonomic wheeled mobile robot tracking system is therefore being explored. The kinematic model and sliding mode control model are analyzed, and the trajectory tracking control of the robot is carried out using an enhanced variable structure based on sliding mode. The shear and sliding mode controls are designed, and the control stability is reviewed to control the trajectory of a nonholonomic wheeled mobile robot. The simulation outcomes show that the projected trajectory track control technique is able to improve the mobile robot’s control, the error of a pose is small, and the linear velocity and angular speed can be controlled. Take the linear and angular velocity as the predicted trajectory.

The class of model-based predictive controllers (MBPCs) has been quite successful at overcoming many of the well-known difficulties inherent in nonminimum phase (NMP) systems, e.g., flexible structures, and among the various MBPCs, Unified Predictive Control (UPC) was selected for the control tasks in this paper. Two methods are used to model the tip force of a flexible manipulator. Modal expansion (summation) is a pole but not zero preserving approach, while Infinite Product Expansion preserves both poles and zeros of a system. It is shown that in a flexible structure framework the difference between the exact and modal expansion zeros is significant. The effect of transmission zero mismatch will be observed in a closed loop system, especially when a model based controller is used. Simulation results show that when a UPC is designed based on the modal expansion model, it destabilizes the closed loop system when an exact model is used as the plant. This suggests a severe problem in implementing the designed UPC experimentally. The use of an exact model in a model-based controller design procedure, such as UPC, is therefore suggested. (Author)

Fuzzy predictive control integrates conventional model-based predictive control with techniques from fuzzy multicriteria decision making, and translates the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the fuzzy set theory. This paper investigates the use of fuzzy decision making in predictive control and compares the results to those obtained from conventional model-based predictive control. Experiments on a nonminimum phase, unstable linear system and air conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model-based predictive controller can be improved by the use of fuzzy goals and criteria with fuzzy decision-making techniques.

This paper proposes a hybrid control algorithm with sliding sectors for the control of a wheeled mobile robot (WMR), which is a non-holonomic system. The WMR control system with a set of available or pre-designed control laws is considered as a hybrid system with a set of subsystems. The WMR with one of control laws forms a non-linear subsystem of the hybrid system. The subsystems may be unstable but for each subsystem of the hybrid system, a stable subset called a sliding sector is designed such that a Lyapunov function candidate decreases inside the sliding sector. The hybrid control rule is used to stabilize the WMR control system by switching the hybrid system among its subsystems such that the system is always inside a sliding sector of some subsystem. The proposed hybrid sliding sector control algorithm has been successfully implemented to the real-time control of WMR with good control performance.

Application of intelligent Wheeled Mobile Robots (WMR) for material handling in the manufacturing environment has been the topic of research in the past decade. Even though researchers have succeeded in applying mobile robots for material handling purpose in the shop floor environment, transporting heavy objects in the assembly line is still a challenge. The dynamical characteristics of a manufacturing environment impose particular abilities a mobile robot should have if it is to operate on the shop floor efficiently, accurately and successfully. Consequently, a WMR needs to adapt itself to everlasting changes. Under such conditions, the use of multiple WMR in closed defined geometric spatial pattern/ formation can be a solution for such applications. One of the essential problems in guiding multiple mobile robots in such dynamically changing environments is to plan, navigate and coordinate the motion of robots, avoiding obstacles as well as each other while transporting the materials/objects towards the goal. Further it requires the robots to control their relative position and orientation between them on the fly. Hence the control of group of mobile robots performing such tasks requires coordination at different levels starting from navigation to formation (Sugar et al., 2001). A variety of strategies and control approaches for formation control of group of coordinated robots, have been adopted in the literature such as Graph Theory (Desai, 2002), Vector Potential Field (Yamaguchi et al., 2001), Virtual Structure (Belta & Kumar, 2004), Leader Follower (Fierro et al., 2002) and Behaviour Based Approaches (Arkin, 1998; Goldberg & Mataric, 2002). Further, a comprehensive review of robotic formation, control approaches and algorithms, applications and their advantages and disadvantages have also been addressed (Chen & Wang, 2005). Among all the approaches mentioned in the literature, behaviour – based and leader – follower approach has been widely adopted and well recognized by the researchers because of their simplicity and scalability. Even though robots are able to move in a closed defined formation when controlled using the various methods reported in the literature, the major limitation has been the difficulty to achieve a stable formation between the robots in the group in dynamically changing, unknown environments filled with obstacles. Under such circumstances, as the number of robots increases, the control methods such as the virtual structure, leader follower and

This study deals with a nonlinear constrained control system for the trajectory tracking of wheeled mobile robots (WMR) with constraints. To this aim, a new constrained controller is developed for WMR based on a predictive approach with input limitations for trajectory tracking. The proposed method leads to an optimization problem that is solved by the Karush Kuhn Tucker conditions (KKT) which employ a continuous nonlinear model of WMR. Finally, the performance of the proposed control method is evaluated by realistic motion models within a virtual robot experimentation platform (V-Rep). The results indicate that the proposed control method not only decreases the trajectory tracking error in the presence of input constraints but also it is suitable for online implementation compared with conventional nonlinear model predictive control (NMPC).

Model Predictive Control in Milling based on Support Vector Machines

The growth of distributed solar photovoltaic (PV) generation in low-voltage grids in recent years has raised concerns about power quality disruptions introduced by solar variability. In response, some electricity utilities have introduced restrictions to new PV installations, such as imposing limits to power ramp rates and exports to the grid. These restrictions effectively require PV installations to locally compensate rapid variations in power output, such as those arising from passing clouds, by using batteries, for example. The direct control of responsive loads to follow variations in solar capacity is a cost-effective alternative to batteries to mitigate impacts to the grid. This paper explores the application of model-based predictive control (MPC) of a population of air conditioners (ACs) to achieve optimal tracking of PV generation to compensate solar variability at a substation or distribution transformer aggregation level. The proposed approach exploits an existing model for aggregate AC demand to design an MPC scheme to modulate the demand of clusters of ACs to track fluctuations in PV capacity. The effectivity of the proposed MPC scheme to achieve AC load following and regulation at different levels of PV penetration is analysed in a numerical simulation study, which shows that solar variability can be reduced by 80 to 95% at PV capacities between 25 and 10% of the peak AC demand with minimal impact to end-user comfort levels.

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments.