In this position paper we investigate the security of cyber-physical systems. We (1) identify and define the problem of secure control, (2) investigate the defenses that information security and control theory can provide, and (3) propose a set of challenges that need to be addressed to improve the survivability of cyber-physical systems.

/pdf/secure-control-towards-survivable-cyber-physical-systems-wcpsudtimg.pdf

Secure Control: Towards Survivable Cyber-Physical Systems

We show that for an adaptive controller that uses recorded and instantaneous data concurrently for adaptation, a verifiable condition on linear independence of the recorded data is sufficient to guarantee exponential tracking error and parameter error convergence. This condition is found to be less restrictive and easier to monitor than a condition on persistently exciting exogenous input signal required by traditional adaptive laws that use only instantaneous data for adaptation.

Concurrent learning for convergence in adaptive control without persistency of excitation

Composite learning robot control with guaranteed parameter convergence

Almost all present control strategies for electrically driven robots are under the rigid robot assumption. Few results can be found for the control of electrically driven robots with joint flexibility. This is because the presence of the joint flexibility greatly increases the complexity of the system dynamics. What is worse is when some system dynamics are not available and a good performance controller is required. In this paper, an adaptive design is proposed to this challenging problem. A backstepping-like procedure incorporating the model reference adaptive control is employed to circumvent the difficulty introduced by its cascade structure and various uncertainties. A Lyapunov-like analysis is used to justify the closed-loop stability and boundedness of internal signals. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme

Adaptive Control for Flexible-Joint Electrically Driven Robot With Time-Varying Uncertainties

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

Combined Direct and Indirect Adaptive Control of Constrained Robots

An optimum scheduling algorithm for flexible manufacturing systems using Petri net modeling and modified branch and bound search is proposed in this paper. A Petri net modeling of the system is first discussed. Then the scheduling algorithm is developed using a modified branch and bound search. The method uses a Petri net model to generate and search a partial reachability graph, and presents an optimal makespan in terms of a firing sequence of transitions of the Petri net model of the system. A software package based an the Windows environment is described which produces a Petri net model satisfying the production requirement and this is used to obtain an optimum makespan through a modified branch and bound search. The results of several simulations are presented to demonstrate the validity of the proposed algorithm and show some improvement over previous work.

https://ieeexplore.ieee.org/iel3/3968/11455/00518763.pdf

FMS scheduling using Petri net modeling and a branch & bound search

Addresses a hybrid scheduling methodology for flexible manufacturing systems (FMS) that uses Petri nets (PNs) as a modeling tool and several successfully employed scheduling methods: conflict-solving based on heuristic dispatching algorithms, artificial intelligence (AI) heuristic search, problem decomposition and evolutionary approximation algorithms as search tools. PNs have been traditionally employed in scheduling approaches based on discrete event simulation and more recently, the combination of PNs and AI heuristic search has produced interesting results. PNs also allow easy structural analysis towards a decomposition of the problem. In this paper PNs are employed as a representation paradigm and a decomposition-construction scheduling method is based on them. A PN-based AI systematic heuristic search is used to solve sub-problems which are progressively joined by an evolutionary building procedure. Experimental results based on a preliminary implementation of the method are presented.

An evolutionary hybrid scheduler based in Petri net structures for FMS scheduling

An adaptive control scheme for robot manipulators including motor dynamics is proposed in this paper. The proposed scheme avoids the assumption that the values of motor parameters are known which is required in reference (13). An exponential control law is first developed under the assumption of no uncertainty. This forms a controller structure for the adaptive control. Using this control structure, a full-order adaptive control law is proposed to overcome parameter uncertainty for both robot link and motor. The stability analysis is in the Lyapunov stability sense. The method is further extended to the task space. Extensive simulations are performed to compare the different control schemes.

Adaptive Control of Robot Manipulators Including Motor Dynamics

The paper discusses the Petri net (PN) modelling of a multiproduct batch process plant with uncertain processing time intervals. A closed loop control strategy is outlined in which the PN model functions as a reference input and is compared with the batch output time sequence of events and states. The effect of processing time interval disturbances on the batch makespan are considered and the resulting errors used in an algorithm, which utilises a critical path analysis, to initiate online time-scheduling of the batch sequence of events in order to correct plant disturbances and retain constant batch makespan. Results are presented, in the form of Gantt charts, when the theory is applied to a simple batch process plant.

https://ieeexplore.ieee.org/iel4/5883/15670/00726074.pdf

S. Lloyd

Papers

Combined Direct and Indirect Adaptive Control of Constrained Robots

FMS scheduling using Petri net modeling and a branch & bound search

An evolutionary hybrid scheduler based in Petri net structures for FMS scheduling

Adaptive Control of Robot Manipulators Including Motor Dynamics

Petri net-based closed-loop control and on-line scheduling of the batch process plant