Longitudinal control of an intelligent vehicle using particle swarm optimization based sliding mode control

This paper focuses on the design of longitudinal controller for an intelligent vehicle which was built at Asian Institute of Technology based on sliding mode control. The proposed controller uses particle swarm optimization (PSO) for optimal tuning of sliding surface and controller gain in the sliding mode controller (SMC). The longitudinal control is conducted via controlling of throttle value angle using PSO-based SMC on the simplified first-order linear model of the intelligent vehicle and controlling of brake force using fuzzy logic. In order to achieve the desired headway time, integration of throttle valve angle control and brake force control is required. To obtain the optimal parameters of SMC, two equations velocity updating and position updating are applied. Firstly, the performance of proposed controller is evaluated by using MATLAB simulation to compare with conventional PD controller. Finally, the experimental results show that the proposed PSO-based SMC can perform efficiently in longitudinal control of the intelligent vehicle.     

Petri Net model of repetitive push manufacturing with Polca to minimise value-added WIP

A common simplifying assumption used in the literature is to minimise average Work In Process (WIP), while achieving maximum production rate, which does not consider the fact that the value of WIP increases down the stream of a production process as labour, time, energy and resources are added to it. This paper aims at minimising queues of parts waiting downstream of production process and in turn minimising value-added WIP by constraining more and more inventory of unfinished products at the earlier stages of production. For this reason, generic black token closed loop Petri Net (PN) model of Flexible Flow Shop (FFS) with Paired-cell Overlapping Loops with Card Authorisation (POLCA) is developed. Tokens in the control loops represent Polca cards to control the flow of material from order release till finished products. Marking of PN is done through Mix Integer Linear Programming after the computation of semi-positive P invariants. Simulation is being done to obtain queues of parts, waiting at each station during demand variation. Results were compared with PN model of FFS without Polca. Results showed the constriction of unnecessary WIP towards the initial stage of production instead of at bottlenecks, with proposed model, and in turn minimisation of value-added WIP.     

Technical Note GSGM movement model for cooperative robots system

Robots cooperation means work-accomplishment action with collaboration of multiple robots by applying shared information, transmitted via communication network in a system. The definition implies that the efficiency of the cooperative robots system depends directly upon two main factors: one is communication among the robots; the other is movement of the robots. Here the authors consider the latter effect on the robots system. Robots with appropriate cooperation are expected to work efficiently. Cooperation action of multiple robots is viewed here as the consequence of a collection of basic group-shape generation. Some of the basic group shapes are straight, curve, circle, multi-angle, and so on. Thus, the model, which can be applied to control multiple robots to generate their group shapes generally with the least exceptions, is necessary. Group Shape Generating Model (GSGM) is proposed here as a general movement model for cooperative robots system. It looks like the result of a combination of potential field, rule based, and communication based models. It applies information, transmitted through the system, to determine accelerations of all robots. Evaluation of the proposed model is done by applying it to many different tasks to estimate its efficiency and generality.     

Geometric and force errors compensation in a 3-axis CNC milling machine

This paper proposes a new off line error compensation model by taking into accounting of geometric and cutting force induced errors in a 3-axis CNC milling machine. Geometric error of a 3-axis milling machine composes of 21 components, which can be measured by laser interferometer within the working volume. Geometric error estimation determined by back-propagation neural network is proposed and used separately in the geometric error compensation model. Likewise, cutting force induced error estimation by back-propagation neural network determined based on a flat end mill behavior observation is proposed and used separately in the cutting force induced error compensation model. Various experiments over a wide range of cutting conditions are carried out to investigate cutting force and machine error relation. Finally, the combination of geometric and cutting force induced errors is modeled by the combined back-propagation neural network. This unique model is used to compensate both geometric and cutting force induced errors simultaneously by a single model. Experimental tests have been carried out in order to validate the performance of geometric and cutting force induced errors compensation model.     

Kinematics control of a pneumatic system by hybrid fuzzy PID

In a pneumatic system, normally, the piston can stop at only two terminal endpoints. In order to extend the capabilities of the system, this research is conducted to develop a kinematics control-based pneumatic system. Both position and velocity of the pneumatic piston are controlled in such a way that the controlled piston is able to move with the specified velocity to the target position. A hybrid of fuzzy and proportional-plus-integral-plus-derivative (PID) control algorithm is proposed in this paper as the solution. The control algorithm is separated into two parts: fuzzy control and PID control. The fuzzy controller is used to control the piston when the piston locates far away from the target position whereas the PID controller is applied when the piston is near the desired position. The development starts with designing of a position sensor to detect position information of the piston. The sensor-manipulating circuit consisting of potentiometer, inverting amplifier, summing amplifier, low-pass filter and analog-to-digital converter is then designed and realized. Next, the proposed hybrid of fuzzy and PID control is implemented and programmed on the microprocessor. In order to test performance of the system, settling time and steady-state error of five control algorithms – proportional (P) control, proportional-plus-integral (PI) control, proportional-plus-derivative (PD) control, PID control, and hybrid of fuzzy and PID control – are investigated. The results from the experiments show that the proposed hybrid of fuzzy and PID control gives the most satisfied settling time and steady-state error.