The paper addresses the problem of constrained pole placement in discrete-time linear systems. The design conditions are outlined in terms of linear matrix inequalities for the Dstable ellipse region in the complex Z plain. In addition, it is demonstrated that the D-stable circle region formulation is the special case of by this way formulated and solved pole placement problem. The proposed principle is enhanced for discrete-lime linear systems with polytopic uncertainties.
Swing-up control of a single pendulum from the pendant to the upright position is ﬁrstly surveyed. The control laws are comparatively studied based on swing-up time from a given initial state to the upright position. The State Dependent Riccati Equation is found eﬀective for designing the swing-up control law under saturating control input. The control law is extended to a linear combination of sine function of the angle and the angular velocity, and a variable structure control with a sliding mode given by the linear combination. Making the swing-up time correspond to a colour, which is similar to the Fractal analysis, colour maps of the swing-up time for given control parameters and initial conditions yield interesting Fractal-like ﬁgures.
This study presents cause-effect dependencies between inputs and outputs of business transitions that are software objects designed for processing information-decision state variables in integrated enterprise process control (EntPC) systems. Business transitions are elementary components of controlling units in enterprise processes that have been defined as self-controlling, generalized business processes, which may serve not only as business processes but also as business systems or their roles. Business events, which have zero durations by definition, are interpreted as executions of business actions that are main operations of business transitions. Any ordered set of business actions, performed in the controlling unit of a given enterprise process and attributed to the same discrete-time instant, is referred to as ‘the information-decision process’. The i-d processes may be substituted by managerial business processes, performed on the lower organizational level, where durations of activity executions are greater than zero, but discrete-time periods are considerably shorter. In such a case, procedures of business actions are performed by corresponding activities of managerial processes, but on the level of business transitions the durations of their executions are imperceptible, and many different business events may occur at the same discrete-time instant. It has been demonstrated in the paper how to control business actions to ensure that a given i-d state variable may not change more than once at a given instant. Furthermore, the rules of designing the i-d process structures, which prevent random changes of transitory states, have been presented.
There exist numerous modelling techniques and representation methods for digital control algorithms, aimed to achieve required system or process parameters, e.g. precision of process modelling, control quality, fulfilling the time constrains, optimisation of consumption of system resources, or achieving a trade-off between number of parameters. This work illustrates usage of Finite State Machines (FSM) modelling technique to solve a control problem with parameterized external variables. The structure of this work comprises six elements. The FSM is presented in brief and discrete control algorithm modelling is discussed. The modelled object and control problem is described and variables are identified. The FSM model is presented and control algorithm is described. The parameterization problem is identified and addressed, and the implementation in PLC programming LAD language is presented. Finally, the conclusion is given and future work areas are identified.
This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.