Autotuning of an industrial weigh belt feeder

An industrial weigh belt feeder is used to transport solid materials into a manufacturing process at a constant feedrate. It exhibits nonlinear behavior because of motor friction, saturation, and quantization noise in the sensor, which makes standard autotuning methods difficult to implement. This research proposes and experimentally demonstrates three approaches for the weigh belt feeder: unfalsified PI control, fuzzy PI control and self-tuning adaptive control.; Unfalsified control is used here as a means of using either open or closed loop test data to identify a subset of controllers (from an initial set) that is not proved to violate the multiple objectives specified by the control engineer. A novel feature of the unfalsified approach is that it allows controllers to be eliminated from consideration by predicting their performance without actually inserting the controllers in the loop. In addition, this methodology does not require an explicit model. However, in practice it does require some closed-loop experimentation to determine the cost functions used to perform the unfalsification. When the unfalsified PI autotuning approach is applied to the industrial weigh belt feeder, it is able to successfully identify a subset of PI control laws that meets the performance specs. A key feature of this research is the use of a genetic search algorithm to reduce the computational requirements of unfalsified control, especially when the initial set of controllers is large.; Two types of fuzzy logic controllers were designed. The first type is a PI-like fuzzy logic controller (FLC). A gain scheduled PI-like FLC and a self-tuning PI-like FLC are presented. For the gain scheduled PI-like FLC the output scaling factor of the controller is gain scheduled with the change of setpoint. For the self-tuning PI-like FLC, the output scaling factor of the controller is modified on-line by an updating factor whose value is determined by a rule-base with the error and change of error of the controlled variable as the inputs. A fuzzy PI controller is also presented, where the proportional and integral gains are tuned on-line based on fuzzy inference rules. Experimental results show the effectiveness of the proposed fuzzy logic controllers.; Adaptive control tries to accommodate the system and its environment and to change the controller behavior when the plant or disturbances change. Thus it exhibits the ability to compensate for certain nonlinearities. This research designs and implements an indirect self-tuning regulator for the industrial weigh belt feeder. The recursive least-squares algorithm was chosen as the on-line estimation method and pole placement was used for the controller design. Implementation issues are discussed and experimental results show the effectiveness of the self-tuning regulator.; Neither of the three methods is uniformly better than the two alternatives. In the research, the three methods are compared based on on-line computational effort, controller development effort, transient performance and the ability to handle motor saturation.