In this paper we address the problem of widearea control of power systems in presence of different classes of network delays. We pose the control objective as an LQR minimization of the electro-mechanical states of the swing equations, and exploit flexibilities and transparencies of the communication network such as scheduling policies, bandwidth to co-design a delay-aware state feedback control law. Hence, unlike the traditional robust control designs, our design is delayaware, not delay-tolerant. A key feature of our method is to retain the samples of the control input until a desired time instant using shapers before releasing them for actuation to regulate the delays entering the controller. In addition, our codesign includes an overrun management strategy to guarantee stability of the closed-loop power system model in case of occasional PMU data losses. This strategy allows dropping messages with very large delays, reducing resource utilization during busy network times, and improving overall performance of the system. We illustrate our results using a 50-bus, 14generator, 4-area power system model, and show how the proposed arbitrated controller can guarantee significantly better closed-loop performance than traditional robust controllers.

In this paper, the possibility of performing severe collision avoidance maneuvers using trajectory optimization is investigated. A two degree of freedom vehicle model was used to represent dynamics of the vehicle. First, a linear tire model was used to calculate the required steering angle to perform the desired evasive maneuver, and a neighboring optimal controller was designed. Second, direct trajectory optimization algorithm was used to find the optimal trajectory with a nonlinear tire model. To evaluate the results, the calculated steering angles were fed to a full vehicle dynamics model. It was shown that the neighboring optimal controller was able to accommodate the introduced disturbances. Comparison of the resultant trajectories with other desired trajectories showed that it results in a lower lateral acceleration profile and a smaller maximum lateral acceleration; thus the time to perform an obstacle avoidance maneuver can be reduced using this method. A simulation case study of a limited lateral acceleration with constrained direct trajectory optimization shows that using the proposed trajectory optimization technique requires less time than that of trapezoidal acceleration profile for a lane change maneuver.

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This paper examines a co-design of control and platform in the presence of dropped signals. In a cyber-physical system, due to increasing complexities such as the simultaneous control of several applications, limited resources, and complex platform architectures, some of the signals transmitted may often be dropped. In this paper, we address the challenges that arise both from the control design and the platform design point of view. A dynamic model is proposed that accommodates these drops, and a suitable switching control design is proposed. A Multiple Lyapunov function based approach is used to guarantee the stability of the system with the switching controller. We then present a method for optimizing the amount of platform resource required to ensure stability of the control systems via a buffer control mechanism that exploits the ability to drop signals of the control system and an associated analysis of the drop bound. The results are demonstrated using a case study of a co-designed lane keeping control system in the presence of dropped signals.

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Model predictive control (MPC) has been used in many industrial applications because of its ability to produce optimal performance while accommodating constraints. However, its application on plants with fast time constants is difficult because of its computationally expensive algorithm. In this research, we propose a parallelized MPC that makes use of the structure of the computations and the matrices in the MPC. We show that the computational time of MPC with prediction horizon N can be reduced to O(log(N)) using parallel computing, which is significantly less than that with other available algorithms.

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This paper presents different control approaches to perform an evasive collision avoidance manoeuvre using active steering. Linear and non-linear controllers to control the combined lateral and longitudinal motion of the vehicle using predefined trajectories are compared. A proportional–derivative controller, a linear quadratic regulator (LQR), and two different sliding-mode controllers (SMC) were developed. The second SMC model includes an additional velocity error term, which augments the model with a steering actuator term. The controllers were implemented on a bicycle model and a 17 degrees-of-freedom (DOF) vehicle model. The results showed that all controllers perform similarly in controlling the trajectory of the bicycle model. However, in implementation on the non-linear full vehicle dynamics model, the LQR and SMCs provided similar position tracking, but the two SMCs performed better in minimizing the yaw (directional) error at the end of the trajectory. However, at a higher velocity, SMC2 resulted in a more stable manoeuvre than SMC1.