A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

In this paper, control of stable, unstable and integrating processes with large time delay in the presence of uncertainty, external disturbances and measurement noise is investigated. For this purpose, fractional order controllers in the parallel cascade scheme based on smith predictor are designed. The applied parallel cascade control strategy consists of a set point tracking controller, a disturbance rejection and a stabilizing controller. Set point tracking and disturbance rejection controllers are fractional order controllers and stabilizing controller is designed based on Routh–Hurwitz stability criterion. Also, the robust stability and the performance of the proposed strategy are studied in some details. The numerical simulation results are used to make comparisons with the other researchers’ works to show superiority of the designed controllers.

Parallel cascade control of dead time processes via fractional order controllers based on Smith predictor.

This paper proposes a Cascaded PIDA (Proportional-Integral-Derivative-Acceleration) load frequency controller (LFC) for interconnected power systems with Wave Energy Conversion Systems (WECS). The proposed controller parameters are optimized using the Marine Predator Algorithm (MPA), which is a recent optimization technique known to be robust. The Cascaded PIDA controller results are compared to the results of a single structure MPA based PIDA controller and other PIDA controllers whose parameters are optimized using various optimization techniques presented in previous literature. Results show that the MPA-based PIDA-LFC has better results than other PIDA controllers optimized by other techniques, while the MPA-Cascaded PIDA-LFC has proven to have the best performance where the frequency and the tie-line power fluctuations, due to the connected Wave Energy Conversion System, are efficiently damped. Moreover, the results are presented in the form of time-domain simulation conducted via MATLAB /SIMULINK.

Marine Predator Algorithm based Cascaded PIDA Load Frequency Controller for Electric Power Systems with Wave Energy Conversion Systems

Smith predictor based parallel cascade control strategy for unstable and integrating processes with large time delay

This paper deals with the event-triggered control for networked cascade control systems. Unlike conventional event-triggered schemes that predetermine a fixed threshold to reduce the data-releasing rate, this paper proposes a novel event-triggered mechanism (ETM) in an adaptive way. Under this ETM, it has the following merits: (1) the data-releasing rate remains at a lower level so as to save limited network bandwidth; (2) the reliability of control systems can be improved since the threshold of ETM is increased gradually with the elapse of time till the next event is generated. An integrated model of networked cascade control systems with consideration of stochastic nonlinearity, actuator failures and ETM is established. Sufficient conditions are obtained to ensure the mean-square stability and stabilization of networked cascade control systems. Finally, two examples are exploited to show the effectiveness of the proposed method.

A novel event-triggered mechanism for networked cascade control system with stochastic nonlinearities and actuator failures

Industrial processes of unstable/integrating nature having a dead time and inverse response characteristics are challenging to control. For controlling such processes, double-loop control structures have proven to be more efficient than conventional PID controllers in a unity feedback configuration. Therefore, a new design method to obtain PI-PD controller settings is proposed for a set of unstable/integrating plant models with dead time and inverse response. The stabilizing proportional–derivative (PD) controller is designed using maximum sensitivity considerations and Routh–Hurwitz stability criteria. The PI controller settings are obtained by comparing the first and second derivatives of expected and actual closed-loop transfer functions about the origin of the s-plane. Adjustable parameters of the inner and outer loops are selected such that the desired value of maximum sensitivity is achieved. Simulation studies are conducted on some benchmark linear and nonlinear plant models used in literature. Robustness of the proposed design is analyzed with perturbed plant models, and quantitative performance measures are computed. It is found that the proposed design yields enhanced and robust closed-loop response than some contemporary works.

New PI-PD Controller Design Strategy for Industrial Unstable and Integrating Processes with Dead Time and Inverse Response

In industrial unstable processes, disturbance rejection is more challenging task than setpoint tracking. So, cascade control structure is widely used in many chemical processes to reject disturbances. In this work, an advanced dead-time compensator-based series cascade control structure (SCCS) is suggested for unstable processes. The suggested SCCS has three controllers (named as primary, secondary and stabilizing controllers). Both primary and secondary controllers are designed using fractional order-based internal model control (IMC) approach. The stabilizing proportional–derivative controller is designed using maximum sensitivity considerations and Routh–Hurwitz stability criteria. Optimal values of the closed-loop time constants and fractional orders of IMC filters are obtained using constrained artificial bee colony (ABC) algorithm. This ABC algorithm uses a multi-objective function involving minimization of integral of absolute error, integral of time weighted absolute error and integral of squared error. Simulation studies are conducted using some benchmark plant models used in literature for illustrating the advantages of the proposed strategy compared to the state of the art. Moreover, robust stability of the proposed design is analysed and quantitative performance measures are also computed.

Optimal Fractional Order IMC-Based Series Cascade Control Strategy with Dead-Time Compensator for Unstable Processes

This manuscript presents a modified parallel cascade control structure (PCCS) for a class of stable, unstable and integrating process models with time delay. The proposed PCCS consists of three controllers. Internal Model Control (IMC) approach is used to design the disturbance rejection controller in the secondary loop. Parameters of the proportional-integral (PI) controller which is used for setpoint tracking is obtained by equating the first and second derivatives of desired and actual closed loop transfer functions at the origin of s-plane. Routh Hurwitz stability criterion is used to design the proportional-derivative (PD) controller which stabilizes the unstable/integrating primary process model. An analytical expression is proposed for computing the desired closed loop time constant of the primary loop in terms of plant model parameters so as to achieve an user-defined maximum sensitivity. Based on extensive simulation studies, a suitable value for the secondary closed loop time constant is also recommended. This is an advantage of the present work over the reported parallel cascade control schemes where authors provide a suitable range of values for the closed loop time constants. The proposed tuning strategy requires tuning of four/six controller parameters for stable/unstable and integrating process models which is less compared to the reported strategies. Simulation results illustrate that the proposed method yields significant improvement in closed loop performance compared to some of the recently reported tuning strategies for both nominal and perturbed process models.

Modified parallel cascade control strategy for stable, unstable and integrating processes

Series cascade control structure (SCCS) is widely used in process industries as unity feedback configuration fails to reject the load disturbances before the controlled variable deviates from the setpoint. Several variants of the conventional SCCSs have been reported in the literature to obtain improved closed-loop performance as compared to the conventional structure. For processes with large time delay, cascade control may not yield satisfactory closed-loop performance. To overcome this problem, several modified Smith predictor based SCCS have been reported in the literature. In this manuscript, various series cascade control strategies are briefly reviewed and their advantages and disadvantages are discussed. Suitable tuning strategies for a class of stable, unstable and integrating process models are recommended in order to help the user in selecting the appropriate control strategy. This review covers only model-based series cascade control strategies in which controller parameters are expressed in terms of known process model parameters.

G. Lloyds Raja

Papers

New PI-PD Controller Design Strategy for Industrial Unstable and Integrating Processes with Dead Time and Inverse Response

Smith predictor based parallel cascade control strategy for unstable and integrating processes with large time delay

Optimal Fractional Order IMC-Based Series Cascade Control Strategy with Dead-Time Compensator for Unstable Processes

Modified parallel cascade control strategy for stable, unstable and integrating processes

Series cascade control: An outline survey