Abstract — Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. MPC is often computation intensive, which limits the class of systems to which it can be applied and the performance criteria it can use. This paper describes a general framework called reactive, control-integrated dataflow modeling for analyzing and improving the algorithms used for MPC and their hardware implementations. The utility of the framework is demonstrated by applying it to the Newton-KKT algorithm. The results show significant reductions in computation time for test cases. I.

Abstract — Electromagnetically driven mechanical systems are characterized by fast nonlinear dynamics that are subject to physical and control constraints, which makes controller design a challenging problem. This paper presents a novel model predictive control (MPC) scheme that can handle both the performance/physical constraints and the strict limits on computational complexity required in control of general electromagnetic (EM) actuators. The novel aspects of the MPC design are a one-step-ahead prediction horizon and an infinitynorm artificial Lyapunov function that is employed to drive the system to a desired reference. An additional optimization variable is introduced to relax the conditions on the Lyapunov function, which is not forced to decrease monotonically. In this way feasibility of the MPC algorithm is improved considerably. While the MPC scheme uses a full nonlinear model, which improves performance, we show that the resulting MPC problem can still be transformed into a low-complexity linear program that can be solved by modern microprocessors within tenths of milliseconds. Moreover, an even simpler piecewise affine explicit controller can be obtained via multiparametric programming. Simulation results are reported and compared with the results achieved by state-of-the-art explicit MPC based on a piecewise affine model. I.

This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires online optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem.

In this paper, we develop a model predictive control (MPC) scheme for robots to follow a virtual leader. The stability of this control scheme is guaranteed by adding a terminal state penalty to the cost function and a terminal state region to the optimization constraints. The terminal state region is found by analyzing the stability. Also a terminal state controller is defined for this control scheme. The terminal state controller is a virtual controller and is never used in the control process. Two virtual leader-following formation models are studied. Simulations on different formation patterns are provided to verify the proposed control strategy.

Abstract — This paper considers the system architecture and design issues for implementation of on-line

Most academic control schemes for MIMO systems assume all the control variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The multiplexed MPC scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem. The multiplexed MPC scheme is also closer to industrial practice in many cases. This paper presents initial stability results for multiplexed MPC. Key words: predictive control, decentralised control, multivariable, control, periodic systems, constrained optimization. ⋆ This work is supported by A*STAR project “Model Predictive Control on a

Embedded systems are becoming more and more important. The products containing embedded systems span from day-to-day household and consumer products, such as digital TVs, mobile phones, and automobiles, to industrial devices and equipment, including, for example, robots, aviation equipment, and high end military and scientific devices such as aircraft. Previously, because embedded systems were highly limited in computational capability, memory size, and power consumption, much research was dedicated to making the best use of limited system resources. In these works, system performance issues, such as execution time, were traded off with system resources, and resources were carefully scheduled and utilized. With more available computational capability in embedded system devices, and more complicated requirements demanding more intensive computation, the most critical design concerns are changing in some important application domains. In such application areas, researchers are paying more and more attention to improving system execution time, which is also the core topic of our work. Executiontime is especially critical to real time systems, in the sense that it is related not only to system performance, but also to system correctness and reliability. Multi-core devices, which incorporate two or more processors on the same integrated

Abstract — Model Predictive Control (MPC) has been used in a wide range of application areas including chemical engineering, food processing, automotive engineering, aerospace, and metallurgy. An important limitation on the application of MPC is the difficulty in completing the necessary computations within the sampling interval. Recent trends in computing hardware towards greatly increased parallelism offer a solution to this problem. This paper describes modeling and analysis tools to facilitate implementing the MPC algorithms on parallel computers, thereby greatly reducing the time needed to complete the calculations. The use of these tools is illustrated by an application to a class of MPC problems. I.